1<!DOCTYPE html> 2<html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head> 3 4<meta charset="utf-8"> 5<meta name="generator" content="quarto-1.5.57"> 6 7<meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes"> 8 9<meta name="author" content="Stephen C. Sanders"> 10<meta name="dcterms.date" content="2025-05-13"> 11 12<title>Mapping the Margins: A Block Group-Level Analysis of Unexplained Economic Distress in New Yorkâs Southern Tier (2009-2023)</title> 13<style> 14code{white-space: pre-wrap;} 15span.smallcaps{font-variant: small-caps;} 16div.columns{display: flex; gap: min(4vw, 1.5em);} 17div.column{flex: auto; overflow-x: auto;} 18div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;} 19ul.task-list{list-style: none;} 20ul.task-list li input[type="checkbox"] { 21width: 0.8em; 22margin: 0 0.8em 0.2em -1em; vertical-align: middle; 23} 24 25pre > code.sourceCode { white-space: pre; position: relative; } 26pre > code.sourceCode > span { line-height: 1.25; } 27pre > code.sourceCode > span:empty { height: 1.2em; } 28.sourceCode { overflow: visible; } 29code.sourceCode > span { color: inherit; text-decoration: inherit; } 30div.sourceCode { margin: 1em 0; } 31pre.sourceCode { margin: 0; } 32@media screen { 33div.sourceCode { overflow: auto; } 34} 35@media print { 36pre > code.sourceCode { white-space: pre-wrap; } 37pre > code.sourceCode > span { display: inline-block; text-indent: -5em; padding-left: 5em; } 38} 39pre.numberSource code 40{ counter-reset: source-line 0; } 41pre.numberSource code > span 42{ position: relative; left: -4em; counter-increment: source-line; } 43pre.numberSource code > span > a:first-child::before 44{ content: counter(source-line); 45position: relative; left: -1em; text-align: right; vertical-align: baseline; 46border: none; display: inline-block; 47-webkit-touch-callout: none; -webkit-user-select: none; 48-khtml-user-select: none; -moz-user-select: none; 49-ms-user-select: none; user-select: none; 50padding: 0 4px; width: 4em; 51} 52pre.numberSource { margin-left: 3em; padding-left: 4px; } 53div.sourceCode 54{ } 55@media screen { 56pre > code.sourceCode > span > a:first-child::before { text-decoration: underline; } 57} 58</style> 59 60
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415 } 416 toggleTitle.classList.add("zindex-over-content"); 417 toggleTitle.classList.add("quarto-sidebar-toggle-title"); 418 toggleContainer.append(toggleTitle); 419 420 const toggleContents = window.document.createElement("div"); 421 toggleContents.classList = el.classList; 422 toggleContents.classList.add("zindex-over-content"); 423 toggleContents.classList.add("quarto-sidebar-toggle-contents"); 424 for (const child of el.children) { 425 if (child.id === "toc-title") { 426 continue; 427 } 428 429 const clone = child.cloneNode(true); 430 clone.style.opacity = 1; 431 clone.style.pointerEvents = null; 432 clone.style.display = null; 433 toggleContents.append(clone); 434 } 435 toggleContents.style.height = "0px"; 436 const positionToggle = () => { 437 // position the element (top left of parent, same width as parent) 438 if (!elRect) { 439 elRect = el.getBoundingClientRect(); 440 } 441 toggleContainer.style.left = `${elRect.left}px`; 442 toggleContainer.style.top = `${elRect.top}px`; 443 toggleContainer.style.width = `${elRect.width}px`; 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445 positionToggle(); 446 447 toggleContainer.append(toggleContents); 448 el.parentElement.prepend(toggleContainer); 449 450 // Process clicks 451 let tocShowing = false; 452 // Allow the caller to control whether this is dismissed 453 // when it is clicked (e.g. sidebar navigation supports 454 // opening and closing the nav tree, so don't dismiss on click) 455 const clickEl = placeholderDescriptor.dismissOnClick 456 ? toggleContainer 457 : toggleTitle; 458 459 const closeToggle = () => { 460 if (tocShowing) { 461 toggleContainer.classList.remove("expanded"); 462 toggleContents.style.height = "0px"; 463 tocShowing = false; 464 } 465 }; 466 467 // Get rid of any expanded toggle if the user scrolls 468 window.document.addEventListener( 469 "scroll", 470 throttle(() => { 471 closeToggle(); 472 }, 50) 473 ); 474 475 // Handle positioning of the toggle 476 window.addEventListener( 477 "resize", 478 throttle(() => { 479 elRect = undefined; 480 positionToggle(); 481 }, 50) 482 ); 483 484 window.addEventListener("quarto-hrChanged", () => { 485 elRect = undefined; 486 }); 487 488 // Process the click 489 clickEl.onclick = () => { 490 if (!tocShowing) { 491 toggleContainer.classList.add("expanded"); 492 toggleContents.style.height = null; 493 tocShowing = true; 494 } else { 495 closeToggle(); 496 } 497 }; 498 }); 499 }; 500 501 // Converts a sidebar from a menu back to a sidebar 502 const convertToSidebar = () => { 503 for (const child of el.children) { 504 child.style.opacity = 1; 505 child.style.overflow = null; 506 child.style.pointerEvents = null; 507 } 508 509 const placeholderEl = window.document.getElementById( 510 placeholderDescriptor.id 511 ); 512 if (placeholderEl) { 513 placeholderEl.remove(); 514 } 515 516 el.classList.remove("rollup"); 517 }; 518 519 if (isReaderMode()) { 520 convertToMenu(); 521 isVisible = false; 522 } else { 523 // Find the top and bottom o the element that is being managed 524 const elTop = el.offsetTop; 525 const elBottom = 526 elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; 527 528 if (!isVisible) { 529 // If the element is current not visible reveal if there are 530 // no conflicts with overlay regions 531 if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { 532 convertToSidebar(); 533 isVisible = true; 534 } 535 } else { 536 // If the element is visible, hide it if it conflicts with overlay regions 537 // and insert a placeholder toggle (or if we're in reader mode) 538 if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { 539 convertToMenu(); 540 isVisible = false; 541 } 542 } 543 } 544 } 545 }; 546 }; 547 548 const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); 549 for (const tabEl of tabEls) { 550 const id = tabEl.getAttribute("data-bs-target"); 551 if (id) { 552 const columnEl = document.querySelector( 553 `${id} .column-margin, .tabset-margin-content` 554 ); 555 if (columnEl) 556 tabEl.addEventListener("shown.bs.tab", function (event) { 557 const el = event.srcElement; 558 if (el) { 559 const visibleCls = `${el.id}-margin-content`; 560 // walk up until we find a parent tabset 561 let panelTabsetEl = el.parentElement; 562 while (panelTabsetEl) { 563 if (panelTabsetEl.classList.contains("panel-tabset")) { 564 break; 565 } 566 panelTabsetEl = panelTabsetEl.parentElement; 567 } 568 569 if (panelTabsetEl) { 570 const prevSib = panelTabsetEl.previousElementSibling; 571 if ( 572 prevSib && 573 prevSib.classList.contains("tabset-margin-container") 574 ) { 575 const childNodes = prevSib.querySelectorAll( 576 ".tabset-margin-content" 577 ); 578 for (const childEl of childNodes) { 579 if (childEl.classList.contains(visibleCls)) { 580 childEl.classList.remove("collapse"); 581 } else { 582 childEl.classList.add("collapse"); 583 } 584 } 585 } 586 } 587 } 588 589 layoutMarginEls(); 590 }); 591 } 592 } 593 594 // Manage the visibility of the toc and the sidebar 595 const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { 596 id: "quarto-toc-toggle", 597 titleSelector: "#toc-title", 598 dismissOnClick: true, 599 }); 600 const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { 601 id: "quarto-sidebarnav-toggle", 602 titleSelector: ".title", 603 dismissOnClick: false, 604 }); 605 let tocLeftScrollVisibility; 606 if (leftTocEl) { 607 tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { 608 id: "quarto-lefttoc-toggle", 609 titleSelector: "#toc-title",
610 dismissOnClick: true, 611 }); 612 } 613 614 // Find the first element that uses formatting in special columns 615 const conflictingEls = window.document.body.querySelectorAll( 616 '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' 617 ); 618 619 // Filter all the possibly conflicting elements into ones 620 // the do conflict on the left or ride side 621 const arrConflictingEls = Array.from(conflictingEls); 622 const leftSideConflictEls = arrConflictingEls.filter((el) => { 623 if (el.tagName === "ASIDE") { 624 return false; 625 } 626 return Array.from(el.classList).find((className) => { 627 return ( 628 className !== "column-body" && 629 className.startsWith("column-") && 630 !className.endsWith("right") && 631 !className.endsWith("container") && 632 className !== "column-margin" 633 ); 634 }); 635 }); 636 const rightSideConflictEls = arrConflictingEls.filter((el) => { 637 if (el.tagName === "ASIDE") { 638 return true; 639 } 640 641 const hasMarginCaption = Array.from(el.classList).find((className) => { 642 return className == "margin-caption"; 643 }); 644 if (hasMarginCaption) { 645 return true; 646 } 647 648 return Array.from(el.classList).find((className) => { 649 return ( 650 className !== "column-body" && 651 !className.endsWith("container") && 652 className.startsWith("column-") && 653 !className.endsWith("left") 654 ); 655 }); 656 }); 657 658 const kOverlapPaddingSize = 10; 659 function toRegions(els) { 660 return els.map((el) => { 661 const boundRect = el.getBoundingClientRect(); 662 const top = 663 boundRect.top + 664 document.documentElement.scrollTop - 665 kOverlapPaddingSize; 666 return { 667 top, 668 bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, 669 }; 670 }); 671 } 672 673 let hasObserved = false; 674 const visibleItemObserver = (els) => { 675 let visibleElements = [...els]; 676 const intersectionObserver = new IntersectionObserver( 677 (entries, _observer) => { 678 entries.forEach((entry) => { 679 if (entry.isIntersecting) { 680 if (visibleElements.indexOf(entry.target) === -1) { 681 visibleElements.push(entry.target); 682 } 683 } else { 684 visibleElements = visibleElements.filter((visibleEntry) => { 685 return visibleEntry !== entry; 686 }); 687 } 688 }); 689 690 if (!hasObserved) { 691 hideOverlappedSidebars(); 692 } 693 hasObserved = true; 694 }, 695 {} 696 ); 697 els.forEach((el) => { 698 intersectionObserver.observe(el); 699 }); 700 701 return { 702 getVisibleEntries: () => { 703 return visibleElements; 704 }, 705 }; 706 }; 707 708 const rightElementObserver = visibleItemObserver(rightSideConflictEls); 709 const leftElementObserver = visibleItemObserver(leftSideConflictEls); 710 711 const hideOverlappedSidebars = () => { 712 marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); 713 sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); 714 if (tocLeftScrollVisibility) { 715 tocLeftScrollVisibility( 716 toRegions(leftElementObserver.getVisibleEntries()) 717 ); 718 } 719 }; 720 721 window.quartoToggleReader = () => { 722 // Applies a slow class (or removes it) 723 // to update the transition speed 724 const slowTransition = (slow) => { 725 const manageTransition = (id, slow) => { 726 const el = document.getElementById(id); 727 if (el) { 728 if (slow) { 729 el.classList.add("slow"); 730 } else { 731 el.classList.remove("slow"); 732 } 733 } 734 }; 735 736 manageTransition("TOC", slow); 737 manageTransition("quarto-sidebar", slow); 738 }; 739 const readerMode = !isReaderMode(); 740 setReaderModeValue(readerMode); 741 742 // If we're entering reader mode, slow the transition 743 if (readerMode) { 744 slowTransition(readerMode); 745 } 746 highlightReaderToggle(readerMode); 747 hideOverlappedSidebars(); 748 749 // If we're exiting reader mode, restore the non-slow transition 750 if (!readerMode) { 751 slowTransition(!readerMode); 752 } 753 }; 754 755 const highlightReaderToggle = (readerMode) => { 756 const els = document.querySelectorAll(".quarto-reader-toggle"); 757 if (els) {
758 els.forEach((el) => { 759 if (readerMode) { 760 el.classList.add("reader"); 761 } else { 762 el.classList.remove("reader"); 763 } 764 }); 765 } 766 }; 767 768 const setReaderModeValue = (val) => { 769 if (window.location.protocol !== "file:") { 770 window.localStorage.setItem("quarto-reader-mode", val); 771 } else { 772 localReaderMode = val; 773 } 774 }; 775 776 const isReaderMode = () => { 777 if (window.location.protocol !== "file:") { 778 return window.localStorage.getItem("quarto-reader-mode") === "true"; 779 } else { 780 return localReaderMode; 781 } 782 }; 783 let localReaderMode = null; 784 785 const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); 786 const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; 787 788 // Walk the TOC and collapse/expand nodes 789 // Nodes are expanded if: 790 // - they are top level 791 // - they have children that are 'active' links 792 // - they are directly below an link that is 'active' 793 const walk = (el, depth) => { 794 // Tick depth when we enter a UL 795 if (el.tagName === "UL") { 796 depth = depth + 1; 797 } 798 799 // It this is active link 800 let isActiveNode = false; 801 if (el.tagName === "A" && el.classList.contains("active")) { 802 isActiveNode = true; 803 } 804 805 // See if there is an active child to this element 806 let hasActiveChild = false; 807 for (child of el.children) { 808 hasActiveChild = walk(child, depth) || hasActiveChild; 809 } 810 811 // Process the collapse state if this is an UL 812 if (el.tagName === "UL") { 813 if (tocOpenDepth === -1 && depth > 1) { 814 // toc-expand: false 815 el.classList.add("collapse"); 816 } else if ( 817 depth <= tocOpenDepth || 818 hasActiveChild || 819 prevSiblingIsActiveLink(el) 820 ) { 821 el.classList.remove("collapse"); 822 } else { 823 el.classList.add("collapse"); 824 } 825 826 // untick depth when we leave a UL 827 depth = depth - 1; 828 } 829 return hasActiveChild || isActiveNode; 830 }; 831 832 // walk the TOC and expand / collapse any items that should be shown 833 if (tocEl) { 834 updateActiveLink(); 835 walk(tocEl, 0); 836 } 837 838 // Throttle the scroll event and walk peridiocally 839 window.document.addEventListener( 840 "scroll", 841 throttle(() => { 842 if (tocEl) { 843 updateActiveLink(); 844 walk(tocEl, 0); 845 } 846 if (!isReaderMode()) { 847 hideOverlappedSidebars(); 848 } 849 }, 5) 850 ); 851 window.addEventListener( 852 "resize", 853 throttle(() => { 854 if (tocEl) { 855 updateActiveLink(); 856 walk(tocEl, 0); 857 } 858 if (!isReaderMode()) { 859 hideOverlappedSidebars(); 860 } 861 }, 10) 862 ); 863 hideOverlappedSidebars(); 864 highlightReaderToggle(isReaderMode()); 865}); 866 867// grouped tabsets 868window.addEventListener("pageshow", (_event) => { 869 function getTabSettings() { 870 const data = localStorage.getItem("quarto-persistent-tabsets-data"); 871 if (!data) { 872 localStorage.setItem("quarto-persistent-tabsets-data", "{}"); 873 return {}; 874 } 875 if (data) { 876 return JSON.parse(data); 877 } 878 } 879 880 function setTabSettings(data) { 881 localStorage.setItem( 882 "quarto-persistent-tabsets-data", 883 JSON.stringify(data) 884 ); 885 } 886 887 function setTabState(groupName, groupValue) { 888 const data = getTabSettings(); 889 data[groupName] = groupValue; 890 setTabSettings(data); 891 } 892 893 function toggleTab(tab, active) { 894 const tabPanelId = tab.getAttribute("aria-controls"); 895 const tabPanel = document.getElementById(tabPanelId); 896 if (active) { 897 tab.classList.add("active"); 898 tabPanel.classList.add("active"); 899 } else { 900 tab.classList.remove("active"); 901 tabPanel.classList.remove("active"); 902 } 903 } 904 905 function toggleAll(selectedGroup, selectorsToSync) { 906 for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { 907 const active = selectedGroup === thisGroup; 908 for (const tab of tabs) { 909 toggleTab(tab, active); 910 } 911 } 912 } 913 914 function findSelectorsToSyncByLanguage() { 915 const result = {}; 916 const tabs = Array.from( 917 document.querySelectorAll(`div[data-group] a[id^='tabset-']`) 918 ); 919 for (const item of tabs) { 920 const div = item.parentElement.parentElement.parentElement; 921 const group = div.getAttribute("data-group"); 922 if (!result[group]) { 923 result[group] = {}; 924 } 925 const selectorsToSync = result[group]; 926 const value = item.innerHTML; 927 if (!selectorsToSync[value]) { 928 selectorsToSync[value] = []; 929 } 930 selectorsToSync[value].push(item); 931 } 932 return result; 933 } 934 935 function setupSelectorSync() { 936 const selectorsToSync = findSelectorsToSyncByLanguage();
937 Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { 938 Object.entries(tabSetsByValue).forEach(([value, items]) => { 939 items.forEach((item) => { 940 item.addEventListener("click", (_event) => { 941 setTabState(group, value); 942 toggleAll(value, selectorsToSync[group]); 943 }); 944 }); 945 }); 946 }); 947 return selectorsToSync; 948 } 949 950 const selectorsToSync = setupSelectorSync(); 951 for (const [group, selectedName] of Object.entries(getTabSettings())) { 952 const selectors = selectorsToSync[group]; 953 // it's possible that stale state gives us empty selections, so we explicitly check here. 954 if (selectors) { 955 toggleAll(selectedName, selectors); 956 } 957 } 958}); 959 960function throttle(func, wait) { 961 let waiting = false; 962 return function () { 963 if (!waiting) { 964 func.apply(this, arguments); 965 waiting = true; 966 setTimeout(function () { 967 waiting = false; 968 }, wait); 969 } 970 }; 971} 972 973function nexttick(func) { 974 return setTimeout(func, 0); 975} 976</script>
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vendor: 9,227 bytes, line 1003
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vendor: 3,868 bytes, line 1003
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vi=Object.freeze(Object.defineProperty({__proto__:null,afterMain:ae,afterRead:se,afterWrite:he,applyStyles:_e,arrow:je,auto:Kt,basePlacements:Qt,beforeMain:oe,beforeRead:ie,beforeWrite:le,bottom:Rt,clippingParents:Ut,computeStyles:Be,createPopper:bi,createPopperBase:gi,createPopperLite:_i,detectOverflow:ii,end:Yt,eventListeners:Re,flip:si,hide:ai,left:Vt,main:re,modifierPhases:de,offset:li,placements:ee,popper:Jt,popperGenerator:mi,popperOffsets:ci,preventOverflow:hi,read:ne,reference:Zt,right:qt,start:Xt,top:zt,variationPlacements:te,viewport:Gt,write:ce},Symbol.toStringTag,{value:"Module"})),yi="dropdown",wi=".bs.dropdown",Ai=".data-api",Ei="ArrowUp",Ti="ArrowDown",Ci=`hide${wi}`,Oi=`hidden${wi}`,xi=`show${wi}`,ki=`shown${wi}`,Li=`click${wi}${Ai}`,Si=`keydown${wi}${Ai}`,Di=`keyup${wi}${Ai}`,$i="show",Ii='[data-bs-toggle="dropdown"]:not(.disabled):not(:disabled)',Ni=`${Ii}.${$i}`,Pi=".dropdown-menu",Mi=p()?"top-end":"top-start",ji=p()?"top-start":"top-end",Fi=p()?"bottom-end":"bottom-start",Hi=p()?"bottom-start":"bottom-end",Wi=p()?"left-start":"right-start",Bi=p()?"right-start":"left-start",zi={autoClose:!0,boundary:"clippingParents",display:"dynamic",offset:[0,2],popperConfig:null,reference:"toggle"},Ri={autoClose:"(boolean|string)",boundary:"(string|element)",display:"string",offset:"(array|string|function)",popperConfig:"(null|object|function)",reference:"(string|element|object)"};class qi extends W{constructor(t,e){super(t,e),this._popper=null,this._parent=this._element.parentNode,this._menu=z.next(this._element,Pi)[0]||z.prev(this._element,Pi)[0]||z.findOne(Pi,this._parent),this._inNavbar=this._detectNavbar()}static get Default(){return zi}static get DefaultType(){return Ri}static get NAME(){return yi}toggle(){return this._isShown()?this.hide():this.show()}show(){if(l(this._element)||this._isShown())return;const t={relatedTarget:this._element};if(!N.trigger(this._element,xi,t).defaultPrevented){if(this._createPopper(),"ontouchstart"in document.documentElement&&!this._parent.closest(".
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vendor: 10,302 bytes, line 1003
1003i:e?Hi:Fi}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(F.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,...g(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=z.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter((t=>a(t)));i.length&&b(i,e,t===Ti,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=qi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(2===t.button||"keyup"===t.type&&"Tab"!==t.key)return;const e=z.find(Ni);for(const i of e){const e=qi.getInstance(i);if(!e||!1===e._config.autoClose)continue;const n=t.composedPath(),s=n.includes(e._menu);if(n.includes(e._element)||"inside"===e._config.autoClose&&!s||"outside"===e._config.autoClose&&s)continue;if(e._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;const o={relatedTarget:e._element};"click"===t.type&&(o.clickEvent=t),e._completeHide(o)}}static dataApiKeydownHandler(t){const e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ei,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ii)?this:z.prev(this,Ii)[0]||z.next(this,Ii)[0]||z.findOne(Ii,t.delegateTarget.parentNode),o=qi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}N.on(document,Si,Ii,qi.dataApiKeydownHandler),N.on(document,Si,Pi,qi.dataApiKeydownHandler),N.on(document,Li,qi.clearMenus),N.on(document,Di,qi.clearMenus),N.on(document,Li,Ii,(function(t){t.preventDefault(),qi.getOrCreateInstance(this).toggle()})),m(qi);const Vi="backdrop",Ki="show",Qi=`mousedown.bs.${Vi}`,Xi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Yi={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Ui extends H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const 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vendor: 3,480 bytes, line 1003
1003'[data-bs-toggle="modal"]',(function(t){const e=z.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div
vendor: 5,687 bytes, line 1003
1003:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"<div></div>"},Un={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},Gn={entry:"(string|element|function|null)",selector:"(string|element)"};class Jn extends H{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Yn}static get DefaultType(){return Un}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'<div class="tooltip" role="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),N.off(this._element.closest(is),ns,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=N.trigger(this._element,this.constructor.eventName("show")),e=(c(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}
vendor: 5,396 bytes, line 1003
1003hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),N.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=g(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return bi(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return g(t,[this._element])}_getPopperConfig(t){const 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t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=F.getDataAttributes(this._element);for(const t of Object.keys(e))Zn.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return 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vendor: 4,636 bytes, line 1003
1003amed "${t}"`);e[t]()}}))}}m(cs);const hs={...cs.Default,content:"",offset:[0,8],placement:"right",template:'<div class="popover" role="tooltip"><div class="popover-arrow"></div><h3 class="popover-header"></h3><div class="popover-body"></div></div>',trigger:"click"},ds={...cs.DefaultType,content:"(null|string|element|function)"};class us extends cs{static get Default(){return hs}static get DefaultType(){return ds}static get NAME(){return"popover"}_isWithContent(){return this._getTitle()||this._getContent()}_getContentForTemplate(){return{".popover-header":this._getTitle(),".popover-body":this._getContent()}}_getContent(){return this._resolvePossibleFunction(this._config.content)}static jQueryInterface(t){return this.each((function(){const e=us.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}m(us);const fs=".bs.scrollspy",ps=`activate${fs}`,ms=`click${fs}`,gs=`load${fs}.data-api`,_s="active",bs="[href]",vs=".nav-link",ys=`${vs}, .nav-item > ${vs}, .list-group-item`,ws={offset:null,rootMargin:"0px 0px -25%",smoothScroll:!1,target:null,threshold:[.1,.5,1]},As={offset:"(number|null)",rootMargin:"string",smoothScroll:"boolean",target:"element",threshold:"array"};class Es extends W{constructor(t,e){super(t,e),this._targetLinks=new Map,this._observableSections=new Map,this._rootElement="visible"===getComputedStyle(this._element).overflowY?null:this._element,this._activeTarget=null,this._observer=null,this._previousScrollData={visibleEntryTop:0,parentScrollTop:0},this.refresh()}static get Default(){return ws}static get DefaultType(){return As}static get NAME(){return"scrollspy"}refresh(){this._initializeTargetsAndObservables(),this._maybeEnableSmoothScroll(),this._observer?this._observer.disconnect():this._observer=this._getNewObserver();for(const t of this._observableSections.values())this._observer.observe(t)}dispose(){this._observer.disconnect(),super.dispose()}_configAfterMerge(t){return t.target=r(t.target)||document.body,t.rootMargin=t.offset?`${t.offset}px 0px -30%`:t.rootMargin,"string"==typeof t.threshold&&(t.threshold=t.threshold.split(",").map((t=>Number.parseFloat(t)))),t}_maybeEnableSmoothScroll(){this._config.smoothScroll&&(N.off(this._config.target,ms),N.on(this._config.target,ms,bs,(t=>{const e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:"smooth"});i.scrollTop=n}})))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver((t=>this._observerCallback(t)),t)}_observerCallback(t){const e=t=>this._targetLinks.get(`#${t.target.id}`),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=z.find(bs,this._config.target);for(const e of t){if(!e.hash||l(e))continue;const t=z.findOne(decodeURI(e.hash),this._element);a(t)&&(this._targetLinks.set(decodeURI(e.hash),e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add(_s),this._activateParents(t),N.trigger(this._element,ps,{relatedTarget:t}))}_activateParents(t){if(t.classList.contains("dropdown-item"))z.findOne(".dropdown-toggle",t.closest(".dropdown")).classList.add(_s);else for(const e of z.parents(t,".nav, .list-group"))for(const t of z.prev(e,ys))t.classList.add(_s)}_clearActiveClass(t){t.classList.remove(_s);const e=z.find(`${bs}.${_s}`,t);for(const t of e)t.classList.remove(_s)}static jQueryInterface(t){return this.each((function(){const e=Es.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}N.on(window,gs,(()=>{for(const t of z.find('[data-bs-spy="scroll"]'))Es.getOrCreateInstance(t)})),m(Es);const Ts=".bs.tab",Cs=`hide${Ts}`,Os=`hidden${Ts}`,xs=`show${Ts}`,ks=`shown${Ts}`,Ls=`click${Ts}`,Ss=`keydown${Ts}`,Ds=`load${Ts}`,$s="ArrowLeft",Is="ArrowRight",Ns="ArrowUp",Ps="ArrowDown",
vendor: 5,833 bytes, line 1003
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jdU+JZGtwyzW3iqDVZsJnbqHDOcpwZaI8tSKRGZPG7CpGEdZYZzzjHdN7pNYEmbDBEZ4WHsaEKA0T9U6LI/FDsnyhuikpdEH/IJggUtvsYLiIQdUF0DgqyEvnDsY0Fmgv0OrryIVh+QQsJgN03VqvbHTTRUi3/NqbYXaETxTWA/t+M1vD91f+5+r5k438HtcSDdMYY1M9f2qLpXz93bi3R+iyaXn/xNA2qcgJT7YyeDwY5Y8g4tHy/HAR4RjQ0aFT/1TTrAWsVbcEu0O9BGnwaRs/uiHTnGcRnx6VZ1D7MJbJAc64G0moa/feFwKG3jMFkXtCusIHKissa1GFu3tWeJE4BsMp3QZuqAbDnBbmmOqK0wVr0LtagX1BZSspH/Pk00OpEoa4AqPDfp6alb8RbYMdn1Ab57LaL57OBas5LpOcv2t0Z362z3Ld0DSymxcZIuDEY8N+yXyYX+Ua0mqRZhW9BC7YfRnizCf06icIZd4E3z6fhEGtP7JhJnxnEybgMm6b9rRdP+2G524+SnZH+Hocul3PoEjg/eghl6hjk/1TyxwJdkUhXTkY8MAFW1Tp3CiNH+8PDpHaBBT5BBzUfiFjLNnfMB38YLrcUEu+9dzxmkGOya4pr0WiP41tJL7jUV3YuwV5W/Uz1i2NJsYVz5zaHCzIG/yTYyZUwWuWEP6OCPr0Y/Gnwl7BOFQO32w8bEl4aaIBE5qABh73PVr7gJwZeYhCdZ5Dvx9bMOD+GHwhiXZHOCqIofFJaOH16kwFbYlNpBTt5GqMBZoFfonMy16VpgEE1XlhgO6DFMTImLWxJ+G35V5HrQopYv174wkgU2RAkDSUPI8spbX0LuuQSdJnh5vwZuFXKX9jnNramUrfjhH99pel02uGf13EuioLahD0P/09oeceoz8+vwT9HM8ei6bz/35q/P7Q1heM5tTG38iHuGHcNdzfm5e0nk6vH5s2VOvGg/ThxQXNXdjBvdtPJE0xj6rTaffRzgfmPuc27vruu3+n1m/jcSq/bwaTarodRAkp/ic8T1+s96zTK5cZenMx4RzWYmQLRkjMVM2/oks6beV0xcrZk8jldllRTkf9HNGVu4IVFt1Bwn3YLhoo1hIg16cFR1fZft7dRXp/wZ7omFhY0g6gqMUs6z7tE0wxNI6ZWkEiF16qCAuhs8qokE+8vaLo+eAIsN3z/Cd38gGrcQfJlOVeZUCuRToy+MMXPIhmsx3PC2KU2sPssM+MwrTEY3Zh/13U61E3W6to0egmj7mYc51+zNLosL/Ga36yC+wJNHsjof5YlEtR1dbMAkFkUPC4+d6vSSrfBOuU62QKCPkKAuQhnXDLs4/czTjsbJjDctBz8rkPWHSs+H+TROLEe5rkKs1/3e/0OFRRBFmx/TrYTHLRbMV+g124qNfT5iU0ezFa+Sb2AzzCO/xm2+6lYvvmP1j5RIwp1+l2DpTiupp6/jXh/t1mA18+srXGDczNLwOvXx1
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44c8Sn1jxxpBEQGIpr/4aXZ2Xq9Ggd+paL4LzdbM7N1/LJhuIy5hoE01WdnWoKO7yE+ObTaMkfD4c5vwpOlySExoRT6fGINw5nAJExduzvoE1qff/TR+a5KJ6eJoipkZpIqt4HV2Lu9Smn19h7ovBstfmWqLlu0dXkb/lPvfBrhju9fZMHiXKMTMhZ2GnOLj1E4+747oVvjuQnpa38FdPWAroa0xOMUUDKaGlbiFhIkBMMJAoNBSK3H0ylD0fQLJKboI6fQfF3n6H0pFsme4y8vmJpRcU2zdzyiNDreM023Ymgo32drsxsbs7XOXXd9uMACApazMwqdnOEnpyepegZPHf8Prq9UKl6pErVnpxpyhbGK3JiabUeVkofF8LNzc3Ot7urqr+d8Yuqwxfvx+Ok7cTwYrhc1gfLEuRZBAvEN1RHOFFi0tjs5s3qDFYbT7gdxtV6fnb3hyiNoBdMS9IVpaB1arKUk8qhaOBRTC0Djr87V
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
1009Dv5Ls1m3+NgShy1SGwy+ORjs8mWRi9FsRC4Ntre3L/OFlYW9PRwx9iSpiCNUlhC/dznLlp2GiCCyZnzghxDEaQDD1KB1/F37PklqreNz29t8omsbbnxxPwnp9zH36xRHipH6oP/n0TydSrOz1sb5zabATQS1azoBDG9tJjm8k1SBmFxcFZe+5VORQoIx0OpBPfStYFCzncuNuNKqiLx+sBM3Ljaee7tPk3jROvfHxTzrdwTWlH/pUtwI4K5LuMR1CXaCRoznpCyHCNhgOkhtTloHG13oljwrWW8fYRnNOIJk40WXAHvY0QjmBkhPvFeg8C5OMtvY5amtGP2pnJ+rgphhAhOqz5W5OkiXM4YKfgso6yWOmz8s6etL9uhatJ7Aqq7317bWUwnv7hfrDKcPKS3I9sJYiT7AiUPs2WzuksemzEgLIjN6XPDU7reaov/sC9+qVaJtJOGIrc7RYi0JQaxXNuDbPCXY7rYQKbpQVVrvcuyn4sbl21M5MvacT7m4HuWU4ZookjbwpywQ8Q8mAsQs6Cn24jJfS5jB1WIgzCOdJi4NhaUKjBLxeg8GhM0O5h7qtLV4E4eOUCPLGnv8cVk+D2JJd140TF2Wv0+TZeiwW9CBBkFAFPrqX2W7+suy/DIhxMcNeVnG/M+Jb1018S6VCJ/a9Uiz1dFU7k56iqz34v6W1gnjXn/TI5taSBagGzovy48/Dp9Pdt43GJQHg4sDskjZq39FFf5NlREt2Z33Xvbg789f9l4u+Ap6wHV/jM+LSPkk1iXRnSpJ/KP1gIYeqEItbQ27kNy74ttMDbFnbXMgAwwYVH6eTLqTpBLPzsaVq7yDQBDMywipgv1kEVJl/sSJwcV77rko5VhTGvdtWpNO4jrlgZ7iSYZSLZkezueLh1CwtM1huse6P70TOuaZaYx4bMsI+NIVEzZfyLm4ZqVRcwnYMeEIyB+miKmQgcScSOCOwFK5OMj5ytsr5gnrAk/FFD1aB30IMb87jGi4TsIXIPuIb5fkPImL6XzPVaE6nYGSrc7c1W3E9TvP3VmPGxF1jIjYNokMhxaW3b/yGI5aj5WmJh568OjRBx+aeNiwyN1Qc7S7iYVjufzaqzwnyyqM5zW+LiiAjDmusUg6AGrfR+aWl+eO3N/t3v9A66E7O/LiWb5tkbB85yN3ls06bOtKkJ6HbUXYZfBujGna4d5AQn/cXNvgXrpr7U6LO/fC/6HQr0Rizw6eaXGVamNzDdXTPgxTTT5YNRNDJUWIC/+4vsQYOjk4qjJ9B7lzGnQsQ9UIsUsLcw3PNXWVaIbpOCXDLcuqFS3dObM0uzRTC6sIZVkNa2fZlB/Olux65Juq7BDioG+uV442bptZtEyZYKhDTZYJ/E89ULvQnbd3X7W+uFiv3vdArV52nHK9lvvmgd1bAluxI3CCW02xMggjiTyUODGDsr4St+PSTNWJO7EPP+etShzYdhBXrLaBZpSxF3ciuzpTSn4iBD1bxk00tbh42+JicfxHjOIy9ChHuNWdtoTNDtguIOHOZuK62ou5b7VYBL/mV334C7YHlVNH9xCZzLtd1xWoIKZqyLVX0Lp4ETd/MKF+WFbj2Q+RyMPbyKwBYu9K+TjK87qtQ49/F8+kllNwgrQ7cfrB1HM4zeRaUPn4oir0LgVSgdK1Xichsj/QE7paDAmtxitHFhtpGtPZI/ML5kPHr3DXlrI2beo58WxW6yG5ZzjtmDPHKhfiCWslw6APqbOdBzA1MCjiO31kycrGMsxovyPdI90vPSw9Kr1dOg+W53uL81DCLbrg27PPnCzGn4ABNdLzaiFoFAf843GDb9MVDfonTdE/gYevcFQ1jMZ8Mt0j6mPiDJrsXw+2g51gEGyXt+Gf6MFUnGVTRT+Wd1b5npEd1NtD/+VzpSlW5UKynrHPz2pfZEsapUVTDeVgHzPfbiMoLBjK+fgS5I54QXGm6iKSeRGY5m5a/LlzSYAJFLFwZwqEk5NIfaueFT/IhjbES5dbMQ8htuGBXmMHZbx+XdzVWRpOfeZ1MdrNly4u3zTPKxwncIn7/N0HNXl/VGw78axQm51b4beezXd0ZJm5TJZ1wsiXboLj5Vxkq6gBQsf+7hvmN/XPE7iIqO2chl7pMWy14+KAxQIXtMs3knvuPXh1mxvV2DB/D0YPD5coCJP/r9cniHkxc2zctDyMoTovZtMfkR6X3ic9c8P1Xggp7uQyurUGMeR6yWFHv8s7LyGuP9Jycf27W20odTFnLWtCcE/cZD1K5YY+nm+RHuRyO1xqb5ig9pK2xAXFl1Fn9LxR/cytCqd5k1LJ+5KQY0dvcK/CdyZ5UvaNfFrYOCFzNjHQRHCrNMLUlf1AvocOfgrU9ObdmQv02XegC/Tdmlqvq9pZdIhOfLZfFohRYpPDBmQCWKC0GflXkMkrfrS7vY08KvU6Bs3scq6/OsRtyrSWxENNjPLcR0spmw1Oq0BnszElc8oxF4SQxXwjA7cvzoBtHngwzLN39ziete8cyjM0oVaYs7yzU26nLLfP87CU8SwnOvwKt5PXRJR9it/b4YuUUHjo5aqJLJioAG2l6S7RGlOPgIFy5Midi1Go0GnVIiyqPb0sU5UelVW5pVeU4sFXFs6022feeqatEtmhmjahsEr/+F0UuoE5WZ5QmV7YL+R3mOZerRKqkzhjgwjRq5ipx5URx4u7RYH2CzYH18M5Li8633Y5uAi/iavoX/YcH7TseLLmgqBqoF1GTm0qPhIbJcNf0VQ7tPxJD9qWrf+/xL0LlCTHdSWWkZH/zMpvZWZVd1X/qqtqerqne6arq6vn1wMMvkQDJEBiIAIQmiRI7fDPGYkUyeEHlIYSRYmQRFBs7h4dE9TKkjFaLu3Ds1x9MDQlWRC9WgvYI6+WkI/so5F1JC+40rFMeL1Lk6Dfi4j8VHV1Tw8EeIHprMysrMgXLyJevHjx3n269x6r6k5XfDStenX3j6qWM3/ylo4zObHQnWza7ZtPt1XVusm2orB5tBmEhmPW75WDaCWuGmFQP9Is9q0wbmJjRF9nZwwyGQcQu3JJjoQtwMmWZfxym3tEwwittg61qtBuSbd2XLhJqMrxWjfRXkTb3TYeGqqChhu0MbG9xtrChNzhM0pHnliosczpmT7vkhWgbRk0hVPMLlbVu+ucnlhfHaTaeIIE7YMYPZ9hfcvy93TTQfdF1SPwnu00dOP6CIUGUH/3e7T5Q5ON+72JqZ++Vrm247uebjAytyuuN0omr8iVH35vlOgzv3p/y5vwvga/GqX9lHQX83e+UdrRXqplVdDw2xuoSJhMpT6rjGuGvhneWI2i18ACJfZ8VjGXyhZ0v6ioX7EniXP4bSLe84C9BZady+KpeCg78Nn9+s/HeTqlDvfU/+r1+1CDmUjYRLIhet42Glw1oZMdYbl0UCe7A+MlmOHDZZGisc4kKZtCEsz2Uc62o8+1kNgMYGsExxp3NlsxGnu+HMbK5Hw9qYT3HquEa5pO4tZS+3cNL3DNaCI8TIhO51SjWf27T6i6rn5C0/Xzt0z4f0Kb86lcD6PKk6H7qKHeF7eT+v9guib8q4ThN8L4Xlyg3h43vqJrj8GP8HC0Urn99mEbPNr1bdAWpAi9EuBPF5+f+cxnvpT9I0uf+tSnoieeeILp5iLfGO4zB9KE1ILZZQN0DFhfimAKsZ8PfXPkTjXpMsdQ9IHm6+sOeq932W4Wpg7BJH0IjM8M1K05XGG3WPqp3hZHGUE4mBlor18vXcbxcxSzeWmkAbrYX8C02SBJ7U9qvw7vq+p2pJux0TE1w4O/hqFZuqPItKHD8rRqR9wRIMKUWtPJ8NX/qegwScF/O4pz+fKGaVmmZRJH1x1UWnRVobqmWBZbb+byvcyTl8+Rl1Hbg9ckk5nSc0Bxs/DWECuNnqaLsOBNgtavHEElozw7yc1C+Z3LduDZTojma7rNkEZCx/Ywc4rKfc3Yh8p3mcV+PK4XnnV825Q1lrpbNm12Y0PEQPPBKpS6noAr00r0H5HWWYbb+0qeBjDe0BrHeLqMMDqDcRpvRn+898l5WMeMrVSxLLZ4dXb/4+rrrrp9q9B6RK2GYQDFVSmfwRFY8d8Ga/6HWRtxt4ksgnv8NWiqAdaQJybWi5Q+0J6g6afHeqhltNYR9HLQQ//xZfn7WJmGgIVr7Lpgdf2SDkqe7DjN6fpib7E+03QqRNd1YpiJ+y43Ns86H3dANM/FZAmqgq21prI/3nIs515DQ0Aat3O2vzwxsdw/2/E8TavadqNeb9g/EU1OHG5Dl29LhO07TLA2RmuvDh1uimAWDVCbWKYZnqUOhMZFmcgBmT/TcV14HU1umj985+H+omLJpKsYyvRc+0ybEqpbtDF1+I7DR18bQitKQ3srTek0sy/s955ppjVOiUw/rlAcUX3DMIHNTKsUcetCrRQwBIO9a
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} 1502.bi-cup-fill::before { content: "\f2de"; } 1503.bi-cup-straw::before { content: "\f2df"; } 1504.bi-cup::before { content: "\f2e0"; } 1505.bi-cursor-fill::before { content: "\f2e1"; } 1506.bi-cursor-text::before { content: "\f2e2"; } 1507.bi-cursor::before { content: "\f2e3"; } 1508.bi-dash-circle-dotted::before { content: "\f2e4"; } 1509.bi-dash-circle-fill::before { content: "\f2e5"; } 1510.bi-dash-circle::before { content: "\f2e6"; } 1511.bi-dash-square-dotted::before { content: "\f2e7"; } 1512.bi-dash-square-fill::before { content: "\f2e8"; } 1513.bi-dash-square::before { content: "\f2e9"; } 1514.bi-dash::before { content: "\f2ea"; } 1515.bi-diagram-2-fill::before { content: "\f2eb"; } 1516.bi-diagram-2::before { content: "\f2ec"; } 1517.bi-diagram-3-fill::before { content: "\f2ed"; } 1518.bi-diagram-3::before { content: "\f2ee"; } 1519.bi-diamond-fill::before { content: "\f2ef"; } 1520.bi-diamond-half::before { content: "\f2f0"; } 1521.bi-diamond::before { content: "\f2f1"; } 1522.bi-dice-1-fill::before { content: "\f2f2"; 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} 1656.bi-file-earmark-person-fill::before { content: "\f378"; } 1657.bi-file-earmark-person::before { content: "\f379"; } 1658.bi-file-earmark-play-fill::before { content: "\f37a"; } 1659.bi-file-earmark-play::before { content: "\f37b"; } 1660.bi-file-earmark-plus-fill::before { content: "\f37c"; } 1661.bi-file-earmark-plus::before { content: "\f37d"; } 1662.bi-file-earmark-post-fill::before { content: "\f37e"; } 1663.bi-file-earmark-post::before { content: "\f37f"; } 1664.bi-file-earmark-ppt-fill::before { content: "\f380"; } 1665.bi-file-earmark-ppt::before { content: "\f381"; } 1666.bi-file-earmark-richtext-fill::before { content: "\f382"; } 1667.bi-file-earmark-richtext::before { content: "\f383"; } 1668.bi-file-earmark-ruled-fill::before { content: "\f384"; } 1669.bi-file-earmark-ruled::before { content: "\f385"; } 1670.bi-file-earmark-slides-fill::before { content: "\f386"; } 1671.bi-file-earmark-slides::before { content: "\f387"; } 1672.bi-file-earmark-spreadsheet-fill::before { content: "\f388"; } 1673.bi-file-earmark-spreadsheet::before { content: "\f389"; } 1674.bi-file-earmark-text-fill::before { content: "\f38a"; } 1675.bi-file-earmark-text::before { content: "\f38b"; } 1676.bi-file-earmark-word-fill::before { content: "\f38c"; } 1677.bi-file-earmark-word::before { content: "\f38d"; } 1678.bi-file-earmark-x-fill::before { content: "\f38e"; } 1679.bi-file-earmark-x::before { content: "\f38f"; } 1680.bi-file-earmark-zip-fill::before { content: "\f390"; } 1681.bi-file-earmark-zip::before { content: "\f391"; } 1682.bi-file-earmark::before { content: "\f392"; } 1683.bi-file-easel-fill::before { content: "\f393"; } 1684.bi-file-easel::before { content: "\f394"; } 1685.bi-file-excel-fill::before { content: "\f395"; } 1686.bi-file-excel::before { content: "\f396"; } 1687.bi-file-fill::before { content: "\f397"; } 1688.bi-file-font-fill::before { content: "\f398"; } 1689.bi-file-font::before { content: "\f399"; } 1690.bi-file-image-fill::before { content: "\f39a"; } 1691.bi-file-image::before { content: "\f39b"; } 1692.bi-file-lock-fill::before { content: "\f39c"; } 1693.bi-file-lock::before { content: "\f39d"; } 1694.bi-file-lock2-fill::before { content: "\f39e"; } 1695.bi-file-lock2::before { content: "\f39f"; } 1696.bi-file-medical-fill::before { content: "\f3a0"; } 1697.bi-file-medical::before { content: "\f3a1"; } 1698.bi-file-minus-fill::before { content: "\f3a2"; } 1699.bi-file-minus::before { content: "\f3a3"; } 1700.bi-file-music-fill::before { content: "\f3a4"; } 1701.bi-file-music::before { content: "\f3a5"; } 1702.bi-file-person-fill::before { content: "\f3a6"; } 1703.bi-file-person::before { content: "\f3a7"; } 1704.bi-file-play-fill::before { content: "\f3a8"; } 1705.bi-file-play::before { content: "\f3a9"; } 1706.bi-file-plus-fill::before { content: "\f3aa"; } 1707.bi-file-plus::before { content: "\f3ab"; } 1708.bi-file-post-fill::before { content: "\f3ac"; } 1709.bi-file-post::before { content: "\f3ad"; } 1710.bi-file-ppt-fill::before { content: "\f3ae"; } 1711.bi-file-ppt::before { content: "\f3af"; } 1712.bi-file-richtext-fill::before { content: "\f3b0"; } 1713.bi-file-richtext::before { content: "\f3b1"; } 1714.bi-file-ruled-fill::before { content: "\f3b2"; } 1715.bi-file-ruled::before { content: "\f3b3"; } 1716.bi-file-slides-fill::before { content: "\f3b4"; } 1717.bi-file-slides::before { content: "\f3b5"; } 1718.bi-file-spreadsheet-fill::before { content: "\f3b6"; } 1719.bi-file-spreadsheet::before { content: "\f3b7"; } 1720.bi-file-text-fill::before { content: "\f3b8"; } 1721.bi-file-text::before { content: "\f3b9"; } 1722.bi-file-word-fill::before { content: "\f3ba"; } 1723.bi-file-word::before { content: "\f3bb"; } 1724.bi-file-x-fill::before { content: "\f3bc"; } 1725.bi-file-x::before { content: "\f3bd"; } 1726.bi-file-zip-fill::before { content: "\f3be"; } 1727.bi-file-zip::before { content: "\f3bf"; } 1728.bi-file::before { content: "\f3c0"; } 1729.bi-files-alt::before { content: "\f3c1"; } 1730.bi-files::before { content: "\f3c2"; } 1731.bi-film::before { content: "\f3c3"; 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} 1753.bi-folder2::before { content: "\f3d9"; } 1754.bi-fonts::before { content: "\f3da"; } 1755.bi-forward-fill::before { content: "\f3db"; } 1756.bi-forward::before { content: "\f3dc"; } 1757.bi-front::before { content: "\f3dd"; } 1758.bi-fullscreen-exit::before { content: "\f3de"; } 1759.bi-fullscreen::before { content: "\f3df"; } 1760.bi-funnel-fill::before { content: "\f3e0"; } 1761.bi-funnel::before { content: "\f3e1"; } 1762.bi-gear-fill::before { content: "\f3e2"; } 1763.bi-gear-wide-connected::before { content: "\f3e3"; } 1764.bi-gear-wide::before { content: "\f3e4"; } 1765.bi-gear::before { content: "\f3e5"; } 1766.bi-gem::before { content: "\f3e6"; } 1767.bi-geo-alt-fill::before { content: "\f3e7"; } 1768.bi-geo-alt::before { content: "\f3e8"; } 1769.bi-geo-fill::before { content: "\f3e9"; } 1770.bi-geo::before { content: "\f3ea"; } 1771.bi-gift-fill::before { content: "\f3eb"; } 1772.bi-gift::before { content: "\f3ec"; } 1773.bi-github::before { content: "\f3ed"; } 1774.bi-globe::before { content: "\f3ee"; } 1775.bi-globe2::before { content: "\f3ef"; } 1776.bi-google::before { content: "\f3f0"; } 1777.bi-graph-down::before { content: "\f3f1"; } 1778.bi-graph-up::before { content: "\f3f2"; } 1779.bi-grid-1x2-fill::before { content: "\f3f3"; } 1780.bi-grid-1x2::before { content: "\f3f4"; } 1781.bi-grid-3x2-gap-fill::before { content: "\f3f5"; } 1782.bi-grid-3x2-gap::before { content: "\f3f6"; } 1783.bi-grid-3x2::before { content: "\f3f7"; } 1784.bi-grid-3x3-gap-fill::before { content: "\f3f8"; } 1785.bi-grid-3x3-gap::before { content: "\f3f9"; } 1786.bi-grid-3x3::before { content: "\f3fa"; } 1787.bi-grid-fill::before { content: "\f3fb"; } 1788.bi-grid::before { content: "\f3fc"; } 1789.bi-grip-horizontal::before { content: "\f3fd"; } 1790.bi-grip-vertical::before { content: "\f3fe"; } 1791.bi-hammer::before { content: "\f3ff"; } 1792.bi-hand-index-fill::before { content: "\f400"; } 1793.bi-hand-index-thumb-fill::before { content: "\f401"; } 1794.bi-hand-index-thumb::before { content: "\f402"; } 1795.bi-hand-index::before { content: "\f403"; } 1796.bi-hand-thumbs-down-fill::before { content: "\f404"; } 1797.bi-hand-thumbs-down::before { content: "\f405"; } 1798.bi-hand-thumbs-up-fill::before { content: "\f406"; } 1799.bi-hand-thumbs-up::before { content: "\f407"; } 1800.bi-handbag-fill::before { content: "\f408"; } 1801.bi-handbag::before { content: "\f409"; } 1802.bi-hash::before { content: "\f40a"; } 1803.bi-hdd-fill::before { content: "\f40b"; } 1804.bi-hdd-network-fill::before { content: "\f40c"; } 1805.bi-hdd-network::before { content: "\f40d"; } 1806.bi-hdd-rack-fill::before { content: "\f40e"; } 1807.bi-hdd-rack::before { content: "\f40f"; } 1808.bi-hdd-stack-fill::before { content: "\f410"; } 1809.bi-hdd-stack::before { content: "\f411"; } 1810.bi-hdd::before { content: "\f412"; } 1811.bi-headphones::before { content: "\f413"; } 1812.bi-headset::before { content: "\f414"; } 1813.bi-heart-fill::before { content: "\f415"; } 1814.bi-heart-half::before { content: "\f416"; } 1815.bi-heart::before { content: "\f417"; } 1816.bi-heptagon-fill::before { content: "\f418"; } 1817.bi-heptagon-half::before { content: "\f419"; } 1818.bi-heptagon::before { content: "\f41a"; } 1819.bi-hexagon-fill::before { content: "\f41b"; } 1820.bi-hexagon-half::before { content: "\f41c"; } 1821.bi-hexag
1821on::before { content: "\f41d"; } 1822.bi-hourglass-bottom::before { content: "\f41e"; } 1823.bi-hourglass-split::before { content: "\f41f"; } 1824.bi-hourglass-top::before { content: "\f420"; } 1825.bi-hourglass::before { content: "\f421"; } 1826.bi-house-door-fill::before { content: "\f422"; } 1827.bi-house-door::before { content: "\f423"; } 1828.bi-house-fill::before { content: "\f424"; } 1829.bi-house::before { content: "\f425"; } 1830.bi-hr::before { content: "\f426"; } 1831.bi-hurricane::before { content: "\f427"; } 1832.bi-image-alt::before { content: "\f428"; } 1833.bi-image-fill::before { content: "\f429"; } 1834.bi-image::before { content: "\f42a"; } 1835.bi-images::before { content: "\f42b"; } 1836.bi-inbox-fill::before { content: "\f42c"; } 1837.bi-inbox::before { content: "\f42d"; } 1838.bi-inboxes-fill::before { content: "\f42e"; } 1839.bi-inboxes::before { content: "\f42f"; } 1840.bi-info-circle-fill::before { content: "\f430"; } 1841.bi-info-circle::before { content: "\f431"; } 1842.bi-info-square-fill::before { content: "\f432"; } 1843.bi-info-square::before { content: "\f433"; } 1844.bi-info::before { content: "\f434"; } 1845.bi-input-cursor-text::before { content: "\f435"; } 1846.bi-input-cursor::before { content: "\f436"; } 1847.bi-instagram::before { content: "\f437"; } 1848.bi-intersect::before { content: "\f438"; } 1849.bi-journal-album::before { content: "\f439"; } 1850.bi-journal-arrow-down::before { content: "\f43a"; } 1851.bi-journal-arrow-up::before { content: "\f43b"; } 1852.bi-journal-bookmark-fill::before { content: "\f43c"; } 1853.bi-journal-bookmark::before { content: "\f43d"; } 1854.bi-journal-check::before { content: "\f43e"; } 1855.bi-journal-code::before { content: "\f43f"; } 1856.bi-journal-medical::before { content: "\f440"; } 1857.bi-journal-minus::before { content: "\f441"; } 1858.bi-journal-plus::before { content: "\f442"; } 1859.bi-journal-richtext::before { content: "\f443"; } 1860.bi-journal-text::before { content: "\f444"; } 1861.bi-journal-x::before { content: "\f445"; } 1862.bi-journal::before { content: "\f446"; } 1863.bi-journals::before { content: "\f447"; } 1864.bi-joystick::before { content: "\f448"; } 1865.bi-justify-left::before { content: "\f449"; } 1866.bi-justify-right::before { content: "\f44a"; } 1867.bi-justify::before { content: "\f44b"; } 1868.bi-kanban-fill::before { content: "\f44c"; } 1869.bi-kanban::before { content: "\f44d"; } 1870.bi-key-fill::before { content: "\f44e"; } 1871.bi-key::before { content: "\f44f"; } 1872.bi-keyboard-fill::before { content: "\f450"; } 1873.bi-keyboard::before { content: "\f451"; } 1874.bi-ladder::before { content: "\f452"; } 1875.bi-lamp-fill::before { content: "\f453"; } 1876.bi-lamp::before { content: "\f454"; } 1877.bi-laptop-fill::before { content: "\f455"; } 1878.bi-laptop::before { content: "\f456"; } 1879.bi-layer-backward::before { content: "\f457"; } 1880.bi-layer-forward::before { content: "\f458"; } 1881.bi-layers-fill::before { content: "\f459"; } 1882.bi-layers-half::before { content: "\f45a"; } 1883.bi-layers::before { content: "\f45b"; } 1884.bi-layout-sidebar-inset-reverse::before { content: "\f45c"; } 1885.bi-layout-sidebar-inset::before { content: "\f45d"; } 1886.bi-layout-sidebar-reverse::before { content: "\f45e"; } 1887.bi-layout-sidebar::before { content: "\f45f"; } 1888.bi-layout-split::before { content: "\f460"; } 1889.bi-layout-text-sidebar-reverse::before { content: "\f461"; } 1890.bi-layout-text-sidebar::before { content: "\f462"; } 1891.bi-layout-text-window-reverse::before { content: "\f463"; } 1892.bi-layout-text-window::before { content: "\f464"; } 1893.bi-layout-three-columns::before { content: "\f465"; } 1894.bi-layout-wtf::before { content: "\f466"; } 1895.bi-life-preserver::before { content: "\f467"; } 1896.bi-lightbulb-fill::before { content: "\f468"; } 1897.bi-lightbulb-off-fill::before { content: "\f469"; } 1898.bi-lightbulb-off::before { content: "\f46a"; } 1899.bi-lightbulb::before { content: "\f46b"; } 1900.bi-lightning-charge-fill::before { content: "\f46c"; } 1901.bi-lightning-charge::before { content: "\f46d"; } 1902.bi-lightning-fill::before { content: "\f46e"; } 1903.bi-lightning::before { content: "\f46f"; } 1904.bi-link-45deg::before { content: "\f470"; } 1905.bi-link::before { content: "\f471"; } 1906.bi-linkedin::before { content: "\f472"; } 1907.bi-list-check::before { content: "\f473"; } 1908.bi-list-nested::before { content: "\f474"; } 1909.bi-list-ol::before { content: "\f475"; } 1910.bi-list-stars::before { content: "\f476"; } 1911.bi-list-task::before { content: "\f477"; } 1912.bi-list-ul::before { content: "\f478"; } 1913.bi-list::before { content: "\f479"; } 1914.bi-lock-fill::before { content: "\f47a"; } 1915.bi-lock::before { content: "\f47b"; } 1916.bi-mailbox::before { content: "\f47c"; } 1917.bi-mailbox2::before { content: "\f47d"; } 1918.bi-map-fill::before { content: "\f47e"; } 1919.bi-map::before { content: "\f47f"; } 1920.bi-markdown-fill::before { content: "\f480"; } 1921.bi-markdown::before { content: "\f481"; } 1922.bi-mask::before { content: "\f482"; } 1923.bi-megaphone-fill::before { content: "\f483"; } 1924.bi-megaphone::before { content: "\f484"; } 1925.bi-menu-app-fill::before { content: "\f485"; } 1926.bi-menu-app::before { content: "\f486"; } 1927.bi-menu-button-fill::before { content: "\f487"; } 1928.bi-menu-button-wide-fill::before { content: "\f488"; } 1929.bi-menu-button-wide::before { content: "\f489"; } 1930.bi-menu-button::before { content: "\f48a"; } 1931.bi-menu-down::before { content: "\f48b"; } 1932.bi-menu-up::before { content: "\f48c"; } 1933.bi-mic-fill::before { content: "\f48d"; } 1934.bi-mic-mute-fill::before { content: "\f48e"; } 1935.bi-mic-mute::before { content: "\f48f"; } 1936.bi-mic::before { content: "\f490"; }
1937.bi-minecart-loaded::before { content: "\f491"; } 1938.bi-minecart::before { content: "\f492"; } 1939.bi-moisture::before { content: "\f493"; } 1940.bi-moon-fill::before { content: "\f494"; } 1941.bi-moon-stars-fill::before { content: "\f495"; } 1942.bi-moon-stars::before { content: "\f496"; } 1943.bi-moon::before { content: "\f497"; } 1944.bi-mouse-fill::before { content: "\f498"; } 1945.bi-mouse::before { content: "\f499"; } 1946.bi-mouse2-fill::before { content: "\f49a"; } 1947.bi-mouse2::before { content: "\f49b"; } 1948.bi-mouse3-fill::before { content: "\f49c"; } 1949.bi-mouse3::before { content: "\f49d"; } 1950.bi-music-note-beamed::before { content: "\f49e"; } 1951.bi-music-note-list::before { content: "\f49f"; } 1952.bi-music-note::before { content: "\f4a0"; } 1953.bi-music-player-fill::before { content: "\f4a1"; } 1954.bi-music-player::before { content: "\f4a2"; } 1955.bi-newspaper::before { content: "\f4a3"; } 1956.bi-node-minus-fill::before { content: "\f4a4"; } 1957.bi-node-minus::before { content: "\f4a5"; } 1958.bi-node-plus-fill::before { content: "\f4a6"; } 1959.bi-node-plus::before { content: "\f4a7"; } 1960.bi-nut-fill::before { content: "\f4a8"; } 1961.bi-nut::before { content: "\f4a9"; } 1962.bi-octagon-fill::before { content: "\f4aa"; } 1963.bi-octagon-half::before { content: "\f4ab"; } 1964.bi-octagon::before { content: "\f4ac"; } 1965.bi-option::before { content: "\f4ad"; } 1966.bi-outlet::before { content: "\f4ae"; } 1967.bi-paint-bucket::before { content: "\f4af"; } 1968.bi-palette-fill::before { content: "\f4b0"; } 1969.bi-palette::before { content: "\f4b1"; } 1970.bi-palette2::before { content: "\f4b2"; } 1971.bi-paperclip::before { content: "\f4b3"; } 1972.bi-paragraph::before { content: "\f4b4"; } 1973.bi-patch-check-fill::before { content: "\f4b5"; } 1974.bi-patch-check::before { content: "\f4b6"; } 1975.bi-patch-exclamation-fill::before { content: "\f4b7"; } 1976.bi-patch-exclamation::before { content: "\f4b8"; } 1977.bi-patch-minus-fill::before { content: "\f4b9"; } 1978.bi-patch-minus::before { content: "\f4ba"; } 1979.bi-patch-plus-fill::before { content: "\f4bb"; } 1980.bi-patch-plus::before { content: "\f4bc"; } 1981.bi-patch-question-fill::before { content: "\f4bd"; } 1982.bi-patch-question::before { content: "\f4be"; } 1983.bi-pause-btn-fill::before { content: "\f4bf"; } 1984.bi-pause-btn::before { content: "\f4c0"; } 1985.bi-pause-circle-fill::before { content: "\f4c1"; } 1986.bi-pause-circle::before { content: "\f4c2"; } 1987.bi-pause-fill::before { content: "\f4c3"; } 1988.bi-pause::before { content: "\f4c4"; } 1989.bi-peace-fill::before { content: "\f4c5"; } 1990.bi-peace::before { content: "\f4c6"; } 1991.bi-pen-fill::before { content: "\f4c7"; } 1992.bi-pen::before { content: "\f4c8"; } 1993.bi-pencil-fill::before { content: "\f4c9"; } 1994.bi-pencil-square::before { content: "\f4ca"; } 1995.bi-pencil::before { content: "\f4cb"; } 1996.bi-pentagon-fill::before { content: "\f4cc"; } 1997.bi-pentagon-half::before { content: "\f4cd"; } 1998.bi-pentagon::before { content: "\f4ce"; } 1999.bi-people-fill::before { content: "\f4cf"; } 2000.bi-people::before { content: "\f4d0"; } 2001.bi-percent::before { content: "\f4d1"; } 2002.bi-person-badge-fill::before { content: "\f4d2"; } 2003.bi-person-badge::before { content: "\f4d3"; } 2004.bi-person-bounding-box::before { content: "\f4d4"; } 2005.bi-person-check-fill::before { content: "\f4d5"; } 2006.bi-person-check::before { content: "\f4d6"; } 2007.bi-person-circle::before { content: "\f4d7"; } 2008.bi-person-dash-fill::before { content: "\f4d8"; } 2009.bi-person-dash::before { content: "\f4d9"; } 2010.bi-person-fill::before { content: "\f4da"; } 2011.bi-person-lines-fill::before { content: "\f4db"; } 2012.bi-person-plus-fill::before { content: "\f4dc"; } 2013.bi-person-plus::before { content: "\f4dd"; } 2014.bi-person-square::before { content: "\f4de"; } 2015.bi-person-x-fill::before { content: "\f4df"; } 2016.bi-person-x::before { content: "\f4e0"; } 2017.bi-person::before { content: "\f4e1"; } 2018.bi-phone-fill::before { content: "\f4e2"; } 2019.bi-phone-landscape-fill::before { content: "\f4e3"; 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3053.bi-highlights::before { content: "\f8ee"; } 3054.bi-luggage-fill::before { content: "\f8ef"; } 3055.bi-luggage::before { content: "\f8f0"; } 3056.bi-mailbox-flag::before { content: "\f8f1"; } 3057.bi-mailbox2-flag::before { content: "\f8f2"; } 3058.bi-noise-reduction::before { content: "\f8f3"; } 3059.bi-passport-fill::before { content: "\f8f4"; } 3060.bi-passport::before { content: "\f8f5"; } 3061.bi-person-arms-up::before { content: "\f8f6"; } 3062.bi-person-raised-hand::before { content: "\f8f7"; } 3063.bi-person-standing-dress::before { content: "\f8f8"; } 3064.bi-person-standing::before { content: "\f8f9"; } 3065.bi-person-walking::before { content: "\f8fa"; } 3066.bi-person-wheelchair::before { content: "\f8fb"; } 3067.bi-shadows::before { content: "\f8fc"; } 3068.bi-suitcase-fill::before { content: "\f8fd"; } 3069.bi-suitcase-lg-fill::before { content: "\f8fe"; } 3070.bi-suitcase-lg::before { content: "\f8ff"; } 3071.bi-suitcase::before { content: "\f900"; } 3072.bi-suitcase2-fill::before { content: "\f901"; } 3073.bi-suitcase2::before { content: "\f902"; } 3074.bi-vignette::before { content: "\f903"; } 3075</style> 3076<link 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30761em%7D%2Equarto%2Dtitle%2Dtools%2Donly%7Bdisplay%3Aflex%3Bjustify%2Dcontent%3Aright%7D%0A" rel="stylesheet" id="quarto-bootstrap" data-mode="light"> 3077<style>html{ scroll-behavior: smooth; }</style> 3078 3079 3080</head> 3081 3082<body> 3083 3084<div id="quarto-content" class="page-columns page-rows-contents page-layout-full toc-left"> 3085<div id="quarto-sidebar-toc-left" class="sidebar toc-left"> 3086 <nav id="TOC" role="doc-toc" class="toc-active"> 3087 <h2 id="toc-title">Table of Contents</h2> 3088 3089 <ul> 3090 <li><a href="#introduction" id="toc-introduction" class="nav-link active" data-scroll-target="#introduction">Introduction</a></li> 3091 <li><a href="#materials-and-methods" id="toc-materials-and-methods" class="nav-link" data-scroll-target="#materials-and-methods">Materials and Methods</a> 3092 <ul class="collapse"> 3093 <li><a href="#data" id="toc-data" class="nav-link" data-scroll-target="#data">Data</a></li> 3094 <li><a href="#processing-methods" id="toc-processing-methods" class="nav-link" data-scroll-target="#processing-methods">Processing Methods</a></li> 3095 <li><a href="#model-using-glmmtmb" id="toc-model-using-glmmtmb" class="nav-link" data-scroll-target="#model-using-glmmtmb">Model Using glmmTMB</a></li> 3096 <li><a href="#other-methods" id="toc-other-methods" class="nav-link" data-scroll-target="#other-methods">Other Methods</a></li> 3097 </ul></li> 3098 <li><a href="#data-gathering-and-processing" id="toc-data-gathering-and-processing" class="nav-link" data-scroll-target="#data-gathering-and-processing">Data Gathering and Processing</a> 3099 <ul class="collapse"> 3100 <li><a href="#map-of-study-area" id="toc-map-of-study-area" class="nav-link" data-scroll-target="#map-of-study-area">Map of Study Area</a></li> 3101 <li><a href="#download-and-process-all-required-data" id="toc-download-and-process-all-required-data" class="nav-link" data-scroll-target="#download-and-process-all-required-data">Download and Process All Required Data</a> 3102 <ul class="collapse"> 3103 <li><a href="#acs-data" id="toc-acs-data" class="nav-link" data-scroll-target="#acs-data">ACS Data</a></li> 3104 <li><a href="#population-weighted-areal-interpolation-and-distressed-status-determination" id="toc-population-weighted-areal-interpolation-and-distressed-status-determination" class="nav-link" data-scroll-target="#population-weighted-areal-interpolation-and-distressed-status-determination">Population-Weighted Areal Interpolation and Distressed Status Determination</a></li> 3105 <li><a href="#land-cover-data" id="toc-land-cover-data" class="nav-link" data-scroll-target="#land-cover-data">Land Cover Data</a></li> 3106 </ul></li> 3107 </ul></li> 3108 <li><a href="#results" id="toc-results" class="nav-link" data-scroll-target="#results">Results</a> 3109 <ul class="collapse"> 3110 <li><a href="#logit-mixed-effects-model-using-glmmtmb" id="toc-logit-mixed-effects-model-using-glmmtmb" class="nav-link" data-scroll-target="#logit-mixed-effects-model-using-glmmtmb">Logit Mixed-Effects Model Using glmmTMB</a> 3111 <ul class="collapse"> 3112 <li><a href="#diagnostic-testing" id="toc-diagnostic-testing" class="nav-link" data-scroll-target="#diagnostic-testing">Diagnostic Testing</a></li> 3113 <li><a href="#fixed-effects" id="toc-fixed-effects" class="nav-link" data-scroll-target="#fixed-effects">Fixed Effects</a></li> 3114 <li><a href="#model-prediction-accuracy" id="toc-model-prediction-accuracy" class="nav-link" data-scroll-target="#model-prediction-accuracy">Model Prediction Accuracy</a></li> 3115 <li><a href="#random-effects-evaluate-areas-with-unexpectedly-high-risk-of-distress" id="toc-random-effects-evaluate-areas-with-unexpectedly-high-risk-of-distress" class="nav-link" data-scroll-target="#random-effects-evaluate-areas-with-unexpectedly-high-risk-of-distress">Random Effects (Evaluate Areas with Unexpectedly High Risk of Distress)</a></li> 3116 </ul></li> 3117 </ul></li> 3118 <li><a href="#discussion-and-conclusions" id="toc-discussion-and-conclusions" class="nav-link" data-scroll-target="#discussion-and-conclusions">Discussion and Conclusions</a></li> 3119 <li><a href="#references" id="toc-references" class="nav-link" data-scroll-target="#references">References</a> 3120 <ul class="collapse"> 3121 <li><a href="#works-cited" id="toc-works-cited" class="nav-link" data-scroll-target="#works-cited">Works Cited</a></li> 3122 <li><a href="#data-sources-and-methodology" id="toc-data-sources-and-methodology" class="nav-link" data-scroll-target="#data-sources-and-methodology">Data Sources and Methodology</a></li> 3123 </ul></li> 3124 </ul> 3125</nav> 3126</div> 3127<div id="quarto-margin-sidebar" class="sidebar margin-sidebar zindex-bottom"> 3128</div> 3129<main class="content column-page-right" id="quarto-document-content"> 3130 3131<header id="title-block-header" class="quarto-title-block default"> 3132<div class="quarto-title"> 3133<div class="quarto-title-block"><div><h1 class="title">Mapping the Margins: A Block Group-Level Analysis of Unexplained Economic Distress in New Yorkâs Southern Tier (2009-2023)</h1><button type="button" class="btn code-tools-button dropdown-toggle" id="quarto-code-tools-menu" data-bs-toggle="dropdown" aria-expanded="false"><i class="bi"></i> Code</button><ul class="dropdown-menu dropdown-menu-end" aria-labelelledby="quarto-code-tools-menu"><li><a id="quarto-show-all-code" class="dropdown-item" href="javascript:void(0)" role="button">Show All Code</a></li><li><a id="quarto-hide-all-code" class="dropdown-item" href="javascript:void(0)" role="button">Hide All Code</a></li><li><hr class="dropdown-divider"></li><li><a id="quarto-view-source" class="dropdown-item" href="javascript:void(0)" role="button">View Source</a></li></ul></div></div> 3134</div> 3135 3136 3137 3138<div class="quarto-title-meta column-page-right"> 3139 3140 <div> 3141 <div class="quarto-title-meta-heading">Author</div> 3142 <div class="quarto-title-meta-contents"> 3143 <p>Stephen C. Sanders </p> 3144 </div> 3145 </div> 3146 3147 <div> 3148 <div class="quarto-title-meta-heading">Published</div> 3149 <div class="quarto-title-meta-contents"> 3150 <p class="date">May 13, 2025</p> 3151 </div> 3152 </div> 3153 3154 3155 </div> 3156 3157 3158 3159</header> 3160 3161 3162<section id="introduction" class="level1"> 3163<h1>Introduction</h1> 3164<p>The Southern Tier of New York, comprising fourteen counties designated as part of Northern Appalachia by the Appalachian Regional Commission (ARC), has long faced significant socioeconomic challenges. These counties have experienced some of the steepest population declines in New York State outside of New York City, driven by prolonged economic restructuring and out-migration (McMahon, 2024; Johnson & Lichter, 2019). Historically reliant on manufacturing, agriculture, and extractive industries, the region has struggled to adapt to post-industrial economic realities. As a result, many communities across the Southern Tier endure persistently high poverty rates and low median incomes relative to national benchmarks (ARC, 2023).</p> 3165<div class="quarto-figure quarto-figure-center"> 3166<figure class="figure"> 3167<p><img role="img" aria-label="Map of three subregions of the New York Southern Tier region." src="data:image/png;base64,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tEE56GISEaqfbsWJYE318UmTUB2sQbtFExpnB9HlLATjUMmZ1ONgrVneKYI/CpUMwg6jnQLCFwLLjy4kWXCaxZn7al6EG4yzcjljtuE54BYRkSZ0MXl3v+0wVLLochrsMMIfNbe9illHJY/2yuaYKjmwAgjbX113DoIJE4ExU2MlbBSCiuOiFhHuKKUgGgqitMayJEG/jdZQdlwCfhulNKWygxCCfLFEJBTE9TwCfh9KKXKFErFIiKDfplwx2/SUouy4REKBqkvw3PD7bKLhIMVyheHxFOWKw/hklkpV6Kw1tDbVEo+EGBybZFtDgolUDs/T5EtlJjN5JjMP6k3yxTJSCBzXo7E2RsVxZ21Hffu1M1za3sj+nS1Y0QQ1rRHqEhECqoIuZFFDt6n84A+NG3ClZCovuRSyZRdCWiZochHQ5SJ68BayrtnkAQkBlr3oSR+5fc8KkBpMoKT1SLJ0Z4Ws24aXSwHLd0XeKBChKPbTH2d0IsXg0NCG9KaZQiQSIRaLMzo69xReY0MDra2t9Pf3k86ksW2bdHp+DVEgEKC2tgbLsm4Cg0KI9X8FtoUFMec3S3XyKTi9jFOoxtGHsPZ/CrntGGr4LEJ5iPh2vCtfxdr/Gbzr38La+RFk50s4P/j7WPs/hep7BxHbDuF6qOSxDv8c3oUvIwIJrBO/gnvhDxFWAIJxVPfrWAc/h07eQDYdQTt5xJEv4F34A6z9n8W9+GWsfT+Nd/3b2E
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" class="img-fluid figure-img" alt="Map of three subregions of the New York Southern Tier region."></p> 3168<figcaption>Map of three subregions in Appalachian NY (NY Department of State)</figcaption> 3169</figure> 3170</div> 3171<p>To monitor and address such disparities, the ARC employs its Distressed Areas Classification System, which is traditionally applied at the county level (ARC, 2024). This system designates areas as âdistressedâ if they exhibit a median family income no greater than 67% of the U.S. average and a poverty rate at least 150% of the national average. While sufficient for broad regional assessments, scholars have noted that county-level classifications risk obscuring critical intra-county variations, particularly in regions marked by sharp spatial heterogeneity (Partridge et al., 2008; Thiede et al., 2017). Applying this framework at the census block group level allows for a more granular understanding of localized socioeconomic distress, capturing neighborhood-level vulnerabilities often masked in aggregate statistics.</p> 3172<p>This study adopts such a fine-scaled approach by applying the ARCâs criteria at the block group level across the Southern Tier. Beyond mere classification, however, this analysis seeks to model the risk of socioeconomic distress by incorporating key demographic, economic, and spatial predictors. Using a Generalized Linear Mixed Model (GLMM) framework implemented via the glmmTMB package in R, this study not only identifies predictors of distress but also evaluates the spatial adequacy of the ARCâs classification system.</p> 3173<p>The glmmTMB framework is particularly well-suited for this analysis due to its flexibility in handling binary outcomes, accounting for hierarchical spatial structures, and integrating spatially lagged covariates all of which encompass a methodological advancement increasingly adopted in socioeconomic and public health research (Bivand & Piras, 2015; Brooks et al., 2017; Dormann et al., 2007). Recent studies have leveraged glmmTMB to model spatial patterns of poverty (Jokela et al., 2019) and health disparities (Barrett et al., 2023), highlighting its capacity to address complex data structures characterized by spatial autocorrelation and unobserved heterogeneity.</p> 3174<p>By combining the ARCâs established distress criteria with predictive modeling, this study aims to (1) assess the underlying risk factors contributing to distress in the Southern Tier, and (2) critically evaluate where the ARCâs classification aligns (or falls short) in capturing the true geography of socioeconomic vulnerability. This approach provides a nuanced understanding of distress that can inform more targeted policy interventions within New Yorkâs Appalachian regi
3174on.</p> 3175</section> 3176<section id="materials-and-methods" class="level1"> 3177<h1>Materials and Methods</h1> 3178<section id="data" class="level2"> 3179<h2 class="anchored" data-anchor-id="data">Data</h2> 3180<p>The analysis looked at various socioeconomic variables along with land cover classifications. Specifically, data concerning average family income, poverty rate, people over the age of 16 who hadnât worked in the previous 12 month period (hereon referred to as âunemployment rateâ), average rent, people over the age of 15 who are divorced (hereon referred to as âdivorced rateâ), and population density were pulled and/or aggregated at the block group, county, and country levels.</p> 3181<p>Census data was pulled for years between 2009 and 2023. All block group-level data was gathered using the <a href="https://cran.r-project.org/web/packages/ipumsr/index.html">ipumsr</a> package, and all of it originally came from American Community Survey (ACS) 5-year estimates housed by the <a href="https://www.nhgis.org/">NHGIS</a> in its <a href="https://www.ipums.org/">IPUMS</a> collection. For distressed status classification, some country-level data for 2009 and 2010 was pulled using the <a href="https://walker-data.com/tidycensus/">tidycensus</a> package, while the rest of it had to be sourced from governmental reports and news releases since ACS data may not have been readily available for this year at the country-level. Block group-level data was aggregated at the county-level to view changes in these socioeconomic indicators and land cover.</p> 3182<p>National land cover data for years between 2008 and 2023 come from the <a href="https://www.mrlc.gov/data/project/annual-nlcd">Multi-Resolution Land Characteristics Consortium</a>âs National Land Cover Database and were downloaded programmatically from their <a href="https://www.mrlc.gov/data?f%5B0%5D=category%3ALand%20Cover">Data page</a>.</p> 3183</section> 3184<section id="processing-methods" class="level2"> 3185<h2 class="anchored" data-anchor-id="processing-methods">Processing Methods</h2> 3186<p>To ensure the uniform block group boundaries across the 14-county study area, the interpolate_pw function within tidycensus was used for population-weighted areal interpolation via centroid assignment (Walker, 2023). The use of scaling factors applied to each individual extensive variable was required to ensure the preservation of total counts across each of the variables by correcting for biases introduced by differing spatial distributions (Gregory, 2002; Goodchild & Lam, 1980; Flowerdew & Green, 1992).</p> 3187<p>As mentioned previously, the methodology used to determine distressed status of each census tract derives from the <a href="https://www.arc.gov/distressed-areas-classification-system/">Appalachian Regional Commission</a>âs Distressed Areas Classification System. According to the ARC, the key attributes of a distressed census tract are:</p> 3188<ol type="1"> 3189<li>A median family income no greater than 67% of the U.S. average.</li> 3190<li>A poverty rate of 150% of the U.S. average or greater.</li> 3191</ol> 3192<p>Since median family incomes cannot be appropriately interpolated, average family income at the national level was used to determine distressed status of each block group rather than the median.</p> 3193<p>Land cover data was processed using the <a href="https://rspatial.github.io/terra/">terra</a> package. All land cover classifications were grouped into 9 groups (including Developed, Agriculture, Forest, etc.). Zonal statistics were calculated for each class within each block group, and the coverage percentage of each class was calculated for each block group. Developed land ultimately became the only land cover classification that was considered in the predictive model.</p> 3194</section> 3195<section id="model-using-glmmtmb" class="level2"> 3196<h2 class="anchored" data-anchor-id="model-using-glmmtmb">Model Using glmmTMB</h2> 3197<p>A logistic generalized linear mixed model was estimated using the <a href="https://github.com/glmmTMB/glmmTMB">glmmTMB</a> package to assess distress risk across the 1,004 uniform block groups. Population density, unemployment rate, average rent, and divorced rate were included as socioeconomic/demographic predictors. Additionally, spatial dependence was accounted for by incorporating a spatially lagged covariate for unemployment rate as a fixed effect in the GLMM model. This approach allows for partial control of spatial autocorrelation within glmmTMB, and was especially necessary given the socioeconomic nature of this study, the expected clustering of socioeconomic conditions, and the exp
3197ansive study area.</p> 3198<p>Including time period fixed effects in a glmmTMB model is a common approach to control for unobserved temporal factors that could bias estimates, such as policy changes, economic cycles, or external shocks affecting all units over time (Zuur et al., 2009). This method isolates the impact of key predictors by accounting for systematic variations across different periods within generalized linear mixed models.</p> 3199<p>Including GEOID as a random effect in a glmmTMB model accounts for unobserved, unit-specific heterogeneity across spatial units, such as census block groups or tracts, allowing the model to control for clustering and repeated measures within geographic areas (Gelman & Hill, 2007). This approach improves inference by addressing potential correlation within groups and capturing latent spatial characteristics not explained by fixed effects. The model assigns each unique block group a random intercept, and these random intercepts represent the baseline log-odds of distress for each block group after accounting for all fixed effects. In other words, this is the unexplained spatial variation - the residual risk attributable to block group-level factors not captured by the modelâs included predictors.</p> 3200<p>Scaling predictor variables in a glmmTMB model is a standard practice to improve model convergence, interpretability, and comparability of effect sizes, especially when predictors are on different scales or have large variances (Schielzeth, 2010). Standardization centers variables around zero and ensures that coefficients represent the effect of a one standard deviation change, facilitating more meaningful interpretation in generalized linear mixed models.</p> 3201<p>After successfully estimating the GLMM, the <a href="https://github.com/florianhartig/DHARMa">DHARMa</a> package was used for diagnostic tests to evaluate the assumptions. The <a href="https://r-spatial.github.io/spdep/">spdep</a> package was used to conduct a Moranâs I test to determine the presence of spatial autocorrelation.</p> 3202</section> 3203<section id="other-methods" class="level2"> 3204<h2 class="anchored" data-anchor-id="other-methods">Other Methods</h2> 3205<p>The <a href="https://www.tidyverse.org/">tidyverse</a> collection of packages was primarily used to easily process a large amount of data. The <a href="https://github.com/walkerke/tigris">tigris</a> package was used directly to pull study counties to create a map of the study area, as well as to pull block-level data for use as the weights in the interpolation process. The <a href="https://r-spatial.github.io/sf/">sf</a> package helped with processing spatial features, including data pulled from the ACS. The maps and general plots were created using <a href="https://ggplot2.tidyverse.org/">ggplot2</a>. The <a href="https://gt.rstudio.com/">gt</a> package allowed for the creation of better looking tables.</p> 3206</section> 3207</section> 3208<section id="data-gathering-and-processing" class="level1"> 3209<h1>Data Gathering and Processing</h1> 3210<section id="map-of-study-area" class="level2"> 3211<h2 class="anchored" data-anchor-id="map-of-study-area">Map of Study Area</h2> 3212<p>The map below shows the 14 counties in New Yorkâs Southern Tier (Allegany, Broome, Cattaraugus, Chautauqua, Chemung, Chenango, Cortland, Delaware, Otsego, Schoharie, Schuyler, Steuben, Tioga, and Tompkins) and all 1,004 block groups (in 2020-2023).</p> 3213<div class="cell" data-layout-align="center" data-fig-margin="0"> 3214<details class="code-fold"> 3215<summary>Code</summary> 3216<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="co"># set directories in cwd in which to store data</span></span> 3217<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a>data_download_path <span class="ot"><-</span> <span class="st">'data/'</span></span> 3218<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a>nhgis_data_dir <span class="ot"><-</span> <span class="fu">paste</span>(data_download_path, <span class="st">'nhgis/'</span>, <span class="at">sep =</span> <span class="st">''</span>)</span> 3219<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a>mrlc_data_dir <span class="ot"><-</span> <span class="fu">paste</span>(data_download_path, <span class="st">'mrlc/'</span>, <span class="at">sep =</span> <span class="st">''</span>)</span>
3220<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a>boundaries_data_dir <span class="ot"><-</span> <span class="fu">paste</span>(data_download_path, <span class="st">'boundaries/'</span>, <span class="at">sep =</span> <span class="st">''</span>)</span> 3221<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a></span> 3222<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a><span class="co"># set vector of southern tier counties</span></span> 3223<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a>southern_tier_counties <span class="ot">=</span> <span class="fu">c</span>(<span class="st">'Allegany'</span>, <span class="st">'Broome'</span>, <span class="st">'Cattaraugus'</span>, <span class="st">'Chautauqua'</span>, </span> 3224<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a> <span class="st">'Chemung'</span>, <span class="st">'Chenango'</span>, <span class="st">'Cortland'</span>, <span class="st">'Delaware'</span>, <span class="st">'Otsego'</span>, </span> 3225<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a> <span class="st">'Schoharie'</span>, <span class="st">'Schuyler'</span>, <span class="st">'Steuben'</span>, <span class="st">'Tioga'</span>, <span class="st">'Tompkins'</span>)</span> 3226<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a></span> 3227<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a><span class="co"># get study geographies, city and town boundaries, and basemap focused on ny state</span></span> 3228<span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a>ny_state <span class="ot"><-</span> <span class="fu">states</span>(<span class="at">cb =</span> <span class="cn">TRUE</span>, <span class="at">year =</span> <span class="dv">2020</span>) <span class="sc">%>%</span> <span class="fu">filter</span>(NAME <span class="sc">==</span> <span class="st">'New York'</span>)</span> 3229<span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a></span> 3230<span id="cb1-15"><a href="#cb1-15" aria-hidden="true" tabindex="-1"></a><span class="co"># list of county FIPS</span></span> 3231<span id="cb1-16"><a href="#cb1-16" aria-hidden="true" tabindex="-1"></a>county_fips <span class="ot"><-</span> <span class="fu">c</span>(</span> 3232<span id="cb1-17"><a href="#cb1-17" aria-hidden="true" tabindex="-1"></a> <span class="st">"003"</span>, <span class="st">"007"</span>, <span class="st">"009"</span>, <span class="st">"013"</span>, <span class="st">"015"</span>, <span class="st">"017"</span>, <span class="st">"023"</span>, </span> 3233<span id="cb1-18"><a href="#cb1-18" aria-hidden="true" tabindex="-1"></a> <span class="st">"025"</span>, <span class="st">"077"</span>, <span class="st">"095"</span>, <span class="st">"097"</span>, <span class="st">"101"</span>, <span class="st">"107"</span>, <span class="st">"109"</span></span> 3234<span id="cb1-19"><a href="#cb1-19" aria-hidden="true" tabindex="-1"></a>)</span> 3235<span id="cb1-20"><a href="#cb1-20" aria-hidden="true" tabindex="-1"></a></span> 3236<span id="cb1-21"><a href="#cb1-21" aria-hidden="true" tabindex="-1"></a><span class="co"># get southern tier county boundaries</span></span> 3237<span id="cb1-22"><a href="#cb1-22" aria-hidden="true" tabindex="-1"></a>study_counties <span class="ot"><-</span> </span> 3238<span id="cb1-23"><a href="#cb1-23" aria-hidden="true" tabindex="-1"></a> <span class="fu">counties</span>(<span class="at">state =</span> <span class="st">'NY'</span>, <span class="at">cb =</span> <span class="cn">TRUE</span>, <span class="at">year =</span> <span class="dv">2020</span>) <span class="sc">%>%</span></span> 3239<span id="cb1-24"><a href="#cb1-24" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(NAME <span class="sc">%in%</span> southern_tier_counties)</span> 3240<span id="cb1-25"><a href="#cb1-25" aria-hidden="true" tabindex="-1"></a></span> 3241<span id="cb1-26"><a href="#cb1-26" aria-hidden="true" tabindex="-1"></a><span class="co"># get block groups in southern tier</span></span> 3242<span id="cb1-27"><a href="#cb1-27" aria-hidden="true" tabindex="-1"></a>study_bgs <span class="ot"><-</span> </span> 3243<span id="cb1-28"><a href="#cb1-28" aria-hidden="true" tabindex="-1"></a> <span class="fu">block_groups</span>(<span class="at">state =</span> <span class="st">'NY'</span>, <span class="at">year =</span> <span class="dv">2020</span>) <span class="sc">%>%</span></span> 3244<span id="cb1-29"><a href="#cb1-29" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(COUNTYFP <span class="sc">%in%</span> county_fips)</span> 3245<span id="cb1-30"><a href="#cb1-30" aria-hidden="true" tabindex="-1"></a></span> 3246<span id="cb1-31"><a href="#cb1-31" aria-hidden="true" tabindex="-1"></a><span class="co"># get basemap of NY</span></span> 3247<span id="cb1-32"><a href="#cb1-32" aria-hidden="true" tabindex="-1"></a>base_ny <span class="ot"><-</span> <span class="fu">basemap_raster</span>(<span class="at">ext =</span> ny_state, <span class="at">map_service =</span> <span class="st">'carto'</span>, </span> 3248<span id="cb1-33"><a href="#cb1-33" aria-hidden="true" tabindex="-1"></a>
3248 <span class="at">map_type =</span> <span class="st">'light'</span>, <span class="at">verbose =</span> <span class="cn">FALSE</span>)</span> 3249<span id="cb1-34"><a href="#cb1-34" aria-hidden="true" tabindex="-1"></a></span> 3250<span id="cb1-35"><a href="#cb1-35" aria-hidden="true" tabindex="-1"></a><span class="co"># get villages and cities</span></span> 3251<span id="cb1-36"><a href="#cb1-36" aria-hidden="true" tabindex="-1"></a>st_civil <span class="ot"><-</span> </span> 3252<span id="cb1-37"><a href="#cb1-37" aria-hidden="true" tabindex="-1"></a> <span class="fu">st_read</span>(<span class="st">'data/boundaries/NYS_Civil_Boundaries.geojson'</span>, <span class="at">quiet =</span> <span class="cn">TRUE</span>) <span class="sc">%>%</span></span> 3253<span id="cb1-38"><a href="#cb1-38" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(COUNTY <span class="sc">%in%</span> southern_tier_counties) <span class="sc">%>%</span></span> 3254<span id="cb1-39"><a href="#cb1-39" aria-hidden="true" tabindex="-1"></a> <span class="fu">st_transform</span>(<span class="at">crs =</span> <span class="dv">26918</span>)</span> 3255<span id="cb1-40"><a href="#cb1-40" aria-hidden="true" tabindex="-1"></a></span> 3256<span id="cb1-41"><a href="#cb1-41" aria-hidden="true" tabindex="-1"></a>st_cities <span class="ot"><-</span></span> 3257<span id="cb1-42"><a href="#cb1-42" aria-hidden="true" tabindex="-1"></a> <span class="fu">st_read</span>(<span class="st">'data/boundaries/NYS_City_Boundaries.geojson'</span>, <span class="at">quiet =</span> <span class="cn">TRUE</span>) <span class="sc">%>%</span></span> 3258<span id="cb1-43"><a href="#cb1-43" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(COUNTY <span class="sc">%in%</span> southern_tier_counties) <span class="sc">%>%</span></span> 3259<span id="cb1-44"><a href="#cb1-44" aria-hidden="true" tabindex="-1"></a> <span class="fu">st_transform</span>(<span class="at">crs =</span> <span class="dv">26918</span>)</span> 3260<span id="cb1-45"><a href="#cb1-45" aria-hidden="true" tabindex="-1"></a></span> 3261<span id="cb1-46"><a href="#cb1-46" aria-hidden="true" tabindex="-1"></a><span class="co"># filter out certain villages, transform, and get centroids</span></span> 3262<span id="cb1-47"><a href="#cb1-47" aria-hidden="true" tabindex="-1"></a>civil <span class="ot"><-</span> </span> 3263<span id="cb1-48"><a href="#cb1-48" aria-hidden="true" tabindex="-1"></a> st_civil <span class="sc">%>%</span> </span> 3264<span id="cb1-49"><a href="#cb1-49" aria-hidden="true" tabindex="-1"></a> <span class="fu">st_transform</span>(<span class="at">crs =</span> <span class="dv">26918</span>) <span class="sc">%>%</span></span> 3265<span id="cb1-50"><a href="#cb1-50" aria-hidden="true" tabindex="-1"></a> <span class="fu">st_centroid</span>() <span class="sc">%>%</span></span> 3266<span id="cb1-51"><a href="#cb1-51" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(NAME, geometry) <span class="sc">%>%</span></span> 3267<span id="cb1-52"><a href="#cb1-52" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(NAME <span class="sc">%in%</span> <span class="fu">c</span>(<span class="st">"Alfred"</span>, <span class="st">"Wellsville"</span>, <span class="st">"Bath"</span>, <span class="st">"Cooperstown"</span>, </span> 3268<span id="cb1-53"><a href="#cb1-53" aria-hidden="true" tabindex="-1"></a> <span class="st">"Watkins Glen"</span>, <span class="st">"Sidney"</span>, <span class="st">"Delhi"</span>, <span class="st">"Waverly"</span>, </span> 3269<span id="cb1-54"><a href="#cb1-54" aria-hidden="true" tabindex="-1"></a> <span class="st">"Owego"</span>, <span class="st">"Cobleskill"</span>, <span class="st">"Hancock"</span>))</span> 3270<span id="cb1-55"><a href="#cb1-55" aria-hidden="true" tabindex="-1"></a></span> 3271<span id="cb1-56"><a href="#cb1-56" aria-hidden="true" tabindex="-1"></a><span class="co"># transform cities and get centroids</span></span>
3272<span id="cb1-57"><a href="#cb1-57" aria-hidden="true" tabindex="-1"></a>cities <span class="ot"><-</span></span> 3273<span id="cb1-58"><a href="#cb1-58" aria-hidden="true" tabindex="-1"></a> st_cities <span class="sc">%>%</span></span> 3274<span id="cb1-59"><a href="#cb1-59" aria-hidden="true" tabindex="-1"></a> <span class="fu">st_transform</span>(<span class="at">crs =</span> <span class="dv">26918</span>) <span class="sc">%>%</span></span> 3275<span id="cb1-60"><a href="#cb1-60" aria-hidden="true" tabindex="-1"></a> <span class="fu">st_centroid</span>() <span class="sc">%>%</span></span> 3276<span id="cb1-61"><a href="#cb1-61" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(NAME, geometry)</span> 3277<span id="cb1-62"><a href="#cb1-62" aria-hidden="true" tabindex="-1"></a></span> 3278<span id="cb1-63"><a href="#cb1-63" aria-hidden="true" tabindex="-1"></a><span class="co"># plot block groups, counties, and names of cities and villages</span></span> 3279<span id="cb1-64"><a href="#cb1-64" aria-hidden="true" tabindex="-1"></a>st_map <span class="ot"><-</span></span> 3280<span id="cb1-65"><a href="#cb1-65" aria-hidden="true" tabindex="-1"></a> <span class="fu">ggplot</span>() <span class="sc">+</span></span> 3281<span id="cb1-66"><a href="#cb1-66" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_sf</span>(<span class="at">data =</span> study_bgs <span class="sc">%>%</span> <span class="fu">st_transform</span>(<span class="at">crs =</span> <span class="st">'EPSG:26918'</span>), </span> 3282<span id="cb1-67"><a href="#cb1-67" aria-hidden="true" tabindex="-1"></a> <span class="at">color =</span> <span class="st">'lightpink'</span>, <span class="at">fill =</span> <span class="st">'white'</span>, <span class="at">size =</span> <span class="fl">2.5</span>) <span class="sc">+</span></span> 3283<span id="cb1-68"><a href="#cb1-68" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_sf</span>(<span class="at">data =</span> study_counties <span class="sc">%>%</span> <span class="fu">st_transform</span>(<span class="at">crs =</span> <span class="st">'EPSG:26918'</span>), </span> 3284<span id="cb1-69"><a href="#cb1-69" aria-hidden="true" tabindex="-1"></a> <span class="at">color =</span> <span class="st">'black'</span>, <span class="at">fill =</span> <span class="cn">NA</span>, <span class="at">size =</span> <span class="dv">2</span>) <span class="sc">+</span></span> 3285<span id="cb1-70"><a href="#cb1-70" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_sf_text</span>(<span class="at">data =</span> cities, <span class="fu">aes</span>(<span class="at">label =</span> NAME), </span> 3286<span id="cb1-71"><a href="#cb1-71" aria-hidden="true" tabindex="-1"></a> <span class="at">angle =</span> <span class="fl">22.5</span>, <span class="at">size =</span> <span class="dv">4</span>) <span class="sc">+</span></span> 3287<span id="cb1-72"><a href="#cb1-72" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_sf_text</span>(<span class="at">data =</span> civil, <span class="fu">aes</span>(<span class="at">label =</span> NAME), </span> 3288<span id="cb1-73"><a href="#cb1-73" aria-hidden="true" tabindex="-1"></a> <span class="at">angle =</span> <span class="sc">-</span><span class="fl">22.5</span>, <span class="at">size =</span> <span class="fl">3.25</span>) <span class="sc">+</span></span> 3289<span id="cb1-74"><a href="#cb1-74" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">'NY Southern Tier Counties and Block Groups'</span>,</span> 3290<span id="cb1-75"><a href="#cb1-75" aria-hidden="true" tabindex="-1"></a> <span class="at">subtitle =</span> <span class="st">'Block Groups as of 2020'</span>) <span class="sc">+</span></span> 3291<span id="cb1-76"><a href="#cb1-76" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme_map</span>() <span class="sc">+</span></span> 3292<span id="cb1-77"><a href="#cb1-77" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme</span>(</span> 3293<span id="cb1-78"><a href="#cb1-78" aria-hidden="true" tabindex="-1"></a>
3293 <span class="at">plot.margin =</span> <span class="fu">margin</span>(<span class="dv">10</span>, <span class="dv">10</span>, <span class="dv">10</span>, <span class="dv">10</span>),</span> 3294<span id="cb1-79"><a href="#cb1-79" aria-hidden="true" tabindex="-1"></a> <span class="at">legend.position =</span> <span class="st">'none'</span></span> 3295<span id="cb1-80"><a href="#cb1-80" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">+</span></span> 3296<span id="cb1-81"><a href="#cb1-81" aria-hidden="true" tabindex="-1"></a> <span class="fu">coord_sf</span>(<span class="at">expand =</span> <span class="cn">FALSE</span>)</span> 3297<span id="cb1-82"><a href="#cb1-82" aria-hidden="true" tabindex="-1"></a></span> 3298<span id="cb1-83"><a href="#cb1-83" aria-hidden="true" tabindex="-1"></a>st_map</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div> 3299</details> 3300<div class="cell-output-display"> 3301<div class="quarto-figure quarto-figure-center"> 3302<figure class="figure"> 3303<p><img role="img" src="data:image/png;base64,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3303ZO8iQuAbUbTV4kOYJSKL5GmDIKJRFghXprRu9uMigmSiRKi18vLAlhuE2BwnUkA+9q8Kc/dN5/DDxr/h8YRvZ1oaDZdbjzRt244EuZo2aVGTO2NRLaL1TwpTOLeYQQYT4DMp44fBIABU40RATAvwfgRr27JuPHTdDeVsSEqphckmkQ4FiEl0oMqAFZAszuOK+VhPwV1AmI8UZV2xSgSiQaiAtj8rBfJ8jMo2neRCWx/ASzR4vzuH85e/N0ar8xvs8+y+bebcViTmCOvi5MbQeeHLpWcicFFAGzhVtT4+hngj2NKXSJU233FXjVudw5ivsuUoOHQjCpQ7ESAvDsUeDc57xKewATA9lbzxwnbutLdf6YPP6rijKZqm3zS6hOJgOIDMN+xCY7JOrKjlADYKJVfWfcaZvGXCfbwbfXUR1rX/zzjcOtCPasifue7JNpPKTW7xHix27nC+92NgLCMvS0w5qObLDNvA7ZhAIvwQE6eJ21IdPRUAIYbNHeBynqT0J5oSPxFyrIAGK+mt5rSua0HhiY9VxgzAH/3QmAiEUzwdyXR2Qh5iWU12XGKupiCIQQiZm4TYHCVMVM1eRnk+rbxRN9suby7bQ5WZkTs6b+//eybqbGv7FwXATCyzRx1A4GLjIDlT7przm1/DiJDaURHaTNyTFu9NzKQCsF0f/KG6ZLGMYeXGwATAOOUZ/CLZMaaagq6IH9VND4UiY5QiwKYL5qrFnyrydygGc2saEsidoktDWCZz9rwIgOmdndP33xbnbz0xw/MnMMr75twzT7L50pdv6//8W5kz682m0TPfRMzxHixVdhrv6zZ/lrZnjhAsIMAsMR8dD34i2Y/YD7XVjo455en8+3JLq9JHbwrQTPBM8SnVACMTXp+OzM+p11n1fgwXOC0hcBEoqS4AIxWf869h01DxAbNZABtX7QXAJbbCBhdpN/U7rb67bOnP6ncUJe6bn+4LZcfnboH+cqbsTQQDs8T4gew1fmrFTkjbANggHDs83RWArDp4+iHLd9BfIYjgI0yBnlA2G9PkbfDy7WBLojoUrzASWopCIDxyLfJILGxM3aAGHKHmBQ8cWmTiUQpUQAs+wMYtoSfSRJ0xyiEth3RWpuM7WFQRNdN3QtpcnyN/tg1bJkJhyahh82kQ7Mn2HP1qUtLYC/fC7UwQ01bZC5z40UAjGyRXcRxML4HDjDCchgANn1pouFif3sizBTLFOLyQfzFXZyF3g6/K1A/A0esBE6Tn1EBMA551/lmr6gevkb4WhKRvLS5RKKE8AAW6F7DJ806KyVkBU5oMumxoEgEtjyAZU3um1ziIPrn7966dceu6jIuDt9yjUn9qVKvmF8P3zaTEesIv/7wFw8mUdNN260A2GbGvzrhOXpRANt+Cx82AgYt4anjtZSQmQIP+sDyy9x4LPh2TP18+BNMbxym9WzsjD5KKwCWrcG9GJZN7qYu434MfKkLgIlEfAK+JIOq2f/OEuaT8JjJsBKYdSFzHguo0BAYL4Dl2R6tAetfmIf/2k0uPLFzC8+VerVp7zSLvyrt3HDMuw1AbUXWEgApAGBUc+WE3iJKAAxocoRJ0ISjjtfARsAn/BwHJCHmu1P05dAtO41uxmSDhmgOBq1xlsEWAbBsDVd+sfCXu7uTVhyh0AXARCI2gfmLfQSMbSCNbiUnMalckCozBMa73MfblwxWe4WP/5dbP/zjX7QtwIdv1/D14p+curmH3bZfxvy5c7hhSMxs/DVLHXRxGwGwTbafkK0MSHnzVS3bn4I465Hwj07hASxjJi0CwNr/QsbVpuK9OWVfDq17V1aeEfz0RY9YbXhCAGwlTVh6cCYXwIZ/kj6eCv4qSRtJJIoL9I5MJt+zkFMg/L4AmNQtaBGKjK9BDIuXCWDmPdn9rHGmcdI406gp6/tmnOuzU4tbZr7he9a6NjB28hc3ax67OHn5K+oAnABYUN4lFLHwTGFgwfYHwIYtLg86wQEscj/YAaz3wDj4CyrWAbAFJ+dOZhACEw+NTGY8oQJgGZqO3A+KJuOWTAGM+FgOd7tMgJg0kkSiuADvyPyzmfMBTYQXADtYHT6A1a/Kb25a+LIIdsMcvFTqFXf60m7/Zbf9aj9dH9hwbzRNxH0GsE02n5CevjgHHCHBlOZt5zPaak3OW1gZAFbNPiRRu1Eb6RODxBAjX404q/dFX43pzQI90vwDYAJgZHnWenQ/Hqv3MwxXiUFomOZ3Nrh8UxpJIlFcEACb/Ft5K0vk+xwKvicAJnu9E1Tko7wpALO+5H/rTv378TsNgZ2rpz9qzn9gJyF+e6aeedBYf1Lj2slPHrj1XrAsEkNxRvTcx422nrqqC5Q/yNPGOZhWF+TGG/qweh5ZKFn8BQawQTuW4OmNryyX9k0zdXWCjCEAtq68MKz7denXM0wPbGY8lZA724KYtJJEoqjSr4hvIDwbwNh9upGMlK2FRFOhS21pABsfxQPYk7Nu5mF1oW48sBt/XbvvrOlvTtW1B7rZ9qux/uVXTVpkl6DLA9j8Pm618TTo7+V4lBBNeWgXEKczH/67AJzqAC2W8JQleLnCAaxLEGq7F99NWf7NwAJYZIBzSQDbahWyqAL3oq0k6o+JWSts9cUfYQfDdGeSOvvQZQYTVu6qSBRT6nXyu0L17T6CSrYMf2FXWguALSt8fQxoP5ByUgjAGq8aTl+a/9RIdr2b+3GuTt5rNmBGzw0KB0LmMj/izN/+Zj+zwz4jgC+CzPOksIy9xKsBGGoWYqEBsNn3iYZeVmztxqyWLjXNaRYSwcOj/jlPpgAYXqE56P3jeNF+X57cRIyFhWYI0oS6s9JOQmnUHSeOto9BiZs89pozOjMNiUq2EIAhrQiALawCn+UtAZhDq5GaETBnqvl+1hz2Pt/o1toAlv1FL6hJ4zKez/QAGCJhcFjO2W5slmIW/QAFTztvAAx0qnULSBVX7b7Ki4HsGo181gXAFpSnFrUHzIY33Zl245Lq0nxCwJYH5vIlAFZKulkUbKdFKCUTOI9B8ZscXJ4rACaiCFtuiwOYTgYJS114PovnZpPlxuzXzhO93fbrUACsG97bolCjAbihAlTCER3CFERU4osAWFZRcDHxGsNfvkuf7gg2DBPx/JP1ZAqAoRSYl9tsN9qfPHdfGNOt9wBreQUAk2YSQha43BazNYJp8TNwFIrd5NDscO8w9lEBmLwaRG0dwGJdDkmpwRT9zkYNXa/oxvTXbgTMTuWHGQeEWhfAph6Ti6VJEqqXKDFSgMr6gQCYz2QBAMsbPpudygUfnluyjeEvd7T7MHajIUHv/N2BPAwVAEPI86g4lxvt+e5wM8ei/uetWRS/ae/PHAmAFdL0bkvhHYPCd9k/OTw44UgATAQQsuQWB7DIpNuk5gBWG2s2WbbW2hEys+3XIQCYx2PyOIWgg+KFNAOwWPoJAMtJNyzOZeqrAlg/tpIaSYSbRAXk6+lnqd1XAopgv9JsUVzgfeyaf3kXIAAGlhe/7Imqevzu+9WwbBo3HBfznr6A7cDvDAmAFZAPt6XwjkHNXU43lbR/4GtwHqOyAAY1IwC2vHBLS5cHsIw5G54RsPqN+dT5DnYs1k0cATuOYwjBHdE9/jo2WBiirYUJbN6/H8tAxvgYNTDrNJN1Aaw5m8TscJuflJ3pufwnjOOWLPacTwsbUK0lDLp/cjsGBMCA8ja4XOnZPSPtt6M5/N0DN329/pC8CjMe/IMunBfEY72tOCnfp1Qamceg9lVPKdl5vQEAQw9eUFOTVyNHmNIrNwIU6UoID+vEW5c1gM3WgO0efHum1I0HdbTHNwen+QZpVwKwcLsv3gJfciDMlwy1D6kMgLHWJNylGh0hiUeMnSSZTAfU6aRhYgEwBhugdPTg08zyZjkD2QOzAmAw+ZssbausGfByp35ldjGxnXhAFxxQNscJ54RDXPnF1G3vNjgmAHZUUgC2MkoGQb7eoakSOCuTyNr/G5uJpOTVyNE2hsAAIyGjx6kHB89osZMaeaE3sv2VZnPmk9+/9Vpdz745TSInm1hTbBHrOzhbu4+bZrgMgflTAe8kBT6XEZi3gcJTqv199AIszETkHM1kOqBOJAzUPk1A1MN/eF4qayV/YqwAGESBSQR9r/jFix+3py7quqKmsfqfN2+rfrtJkO1pAjlC3llpK0WkPFMgBMCOSgrYabYPAIae8SwAtop4h8D4AczjWQKUiL
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3303dUkAxiEw0SEizL5zIhpuhylHwCBWWVNdh3sCINMSlK00+6f1f8w3wbodOcZ5IuQLk6tEANb3504vZgOwNQCYQ80hoH4aVCGDRa1xDb8gMrNuAIuqB88PTu4/3dXBjh6lH91MH7+XpOpFrq4bOncDWLRpQRVdNXBKuJLen/umIo2Rb+awUzMyACbyxHCzI6lOFm4GjO7TJ5IIwIIPPz+AOQVYRUvw1xrakfaAAAAgAElEQVQAzHGo6RjilNtFAdigUOLqH9H6JfQCRNPGNZypHEfTjoA1foNn8Z593fpIKxiHviT17vOXu2sftVkSql0huZIjsFFXa/vPsU1AhFyfTMiSHcBcSdy1ugPGhTNDqr3aUxgaywFg8PszF4Bd3anY6/U/+sOieOHzwU1z30N7FChlpxbGlBPAIEOp8Mhl1B1BupRgDI4oKAE2hqSOZqUXIQCDuBJyslgzfa2KTksrbBAAQ2TCdQ4cYZGrSFgVTanIDwTAOvhimBUCsAQEhqp/ZOsX1nUSEc1xNAOAReqkcD1hG4nFXZLhguUn1LF5WrxSKX7l34IjKYDhQySvrbajpTOpXHx36ANgEAAT8rQAAPMktk4nisBZbAEOLo8YwOxzz28XJ/f0kzwr+oWqvWZwkDEFfsNMAQAMbgs8b49g3Z2ITmBdPmhV+6SvcAOwXGagd0EoRIhiTbjYB895Ip6ZAIy2cOxQAEzALA7A/DF2gpgFkTXhGXohAMP3oQA1gkMaKevP0Fh4bLIFfPIR7gpipX0Skbi6i3/1C2tHDoFMwYXVTrvs1fgVHB85dP46DgALdilYMRzeNDQFlLBipyOKRwpgwzf3tDj5yPxdVVP9SlVoJhCZlqzo4vyFcwdoUofgSbuYYMkEtWDgsh1JvwFYdjNdxyU6aRIAi5Rh0MnkAAYpf8QyukoAY1GBVwh7+COO4oU2BCZcvfBgl4pocwFYqB9yfCLQIxvzgcyS/4yPX60c9b8ugoFNottqtdSikU90ZsNBy3EAmCtOs161ZsoDwTIYwHhMAy37xwlg5sSTD+/+zOwzvn+/eKU+fnW7CHUU0XJBU0Xa4gNYPB61yxT5SsIABmjWLRVeVH5wADZrfQkzo0D2Xd24QhnopIhqwGq19AAWSgdWAaZbPoAlGgLD7z6ZFsCsdwBeAUnXLkwAIzbPk3T9g0nLX413pxObaBitXykX44TtTLLZ7MghkiegDKwKD856ZQOwAwGw0Uylrk+hP0udABWe0tYrBq1E3QCVMwDYHN1DofPmjv/wd/WuG2+cK/X97XqDoHonjm6qNDgX8AZy2QBWBnNozcLgXEeYn3C9pjIAFrmao9sEUciOv1LDPeSANUoOWoE81pUAGLmEzgVgrPg12NRwLGMBzNlwy73gvSVoxsQrF2a3KrV/dHxjswJY6S5FfPDqTcmoi96LJDd2XDXmeHpS+08sWI4FwKwAzX7zgGEbLjNOKgtOtog4AcepRwVg/W3d39e7bvxh9c9LX5fP77z+oDr25E5RvPUXZssgZDag2U7HX1IA5u0B1LYm6EKSEIHFO03ZAGa9yTAo2gBM0ooC2HedJ+RJYOgfUK3ND2D0ArpGAONSgV8I9zElgPXl156CN/5EAUbwOSCfBGlAg4ZsUxA7L+OqQylB1zIvi+ydSHFfk74cPjn8AbDjAbBAH64/ThXNjB/LwlYm9YdfddUAFuVQ75+nRfH752V5db94qxnwuvqgKE7ulRdF/7kMsXy0WpKt8+hvMQDzJBsGzlJd2EOcQq8N4gHYeDNEj0gCWNoWaO76EmQlHsqEql2BDDSSG8Aihdt/NhZJM8qnIynt2eNS8erBxQyBlaljzA7A6h/Ej3R3OSIgG7NZDwNcLIbp2oPk3wHzuO//6P6RsS2gnm+7FUmTQmOIfjmCAbD1ARjdTKArljwDkZyZ3kR0LNwxsuBLcDwAZt+Ci3a3w2ePmiOXu6J44xFtGw5odZiicW5FEsCmi5HHqCI2CX4AY9hFA+wX0FznBmBp7PgALFp5CQFYJAUfwJyP1A9gdE8TXTsU5xRPKQDLyV+xsH7oCdbH0giebxJ38ncEZv4gPmlGjsLhbNxwQAPYYponWAyPQBKKSFN8gDMlMIbZ6tI3Qf6m+iymfX5HMAB2VADmLfrDAYEsmQGbcRbx+QAMFVrIuR2fHqiejilLb0SvpyGauYnTnejjGUFuH8GWpAA2TuuINTkNYdugDV6iuqkLTr4dH6XkYGIjeh0bgElbQfYeAdK4EqQGMOcjpfIX4Z7X0TGrdM4EYBQX0PR2J1FXpYynknhTLxXAFG3wa2CEIFwAI9cBQ9XhlWcksGF3kdQaMIkwZ/EAFnjyCR/gMQyAHROAeYt+/Y9EAEgUNPAcO4ANNCvKGm21cdou/apIjDIE5r3tdgU+PBxf6Ifx5n6OrKI/ySIw1gSb1ia63aV8vjv/01tFyAFh2fwGYNJW8gBY5DzsscY7umAvRTT7hHteheTkqLyxIJKPzAAGqfMjC6i95xYGYF1XFecxc3IU9JsLwMa5oLeaFBFr9BhmDgrAUiLYMQyAHRmATRJb3fZLBDBv2fYcL2Rqs6UDWL3zxpN3iuLGAweA9QfOiuuEjRBLx43v1+v1uDHNNnUrzfEReQCLWZN4rZTdoRHy6yyknlT+W4oaG25lAzBpK5R+dWzIIjSwGc/qoHB6K98UAMYvmqyPt5PT8IpX7FYC6hO/jcUBGL2nt7fBSMsFMPqIcCwXuSBs5GSmMTBnFCopOe2le3RHwV/5AGwR8YSr270LHyld8JzMxM3Aoh5rYKGw/mYMysgAmHQ3txr8uLxTryx+bzgFcf/krzSA1Qcudy/+4/3r9wgAFq2qPfdXKODxvHOiNZ1gZ2AfsVodGn5tTFTun8HoORxxvgGYuJVg84xP4tCOqwsAWLM5mrLebZIZmIZcqk6k5i0vCcBAAyQ+haUBWF07Mm8YIy0bwOhDwvFc5CCwKfksg8AWDmDRfqs0z24DMKgGSJYRT0yLvnNSWZ7MxMxAIhtrTlfpa4OnQstPLE90VVCCQce0nlz42t1XzQzDs/57y/Xg12lR3Dwvnz0kfIfZnRfwpVDKEALAJCs6QQAbEBjZ0ghlu6LqmmhIDY6FppVvAAY8JwFgkFIvAWClAhbfVADGfFSeyj9xLpjFi9EUWUqq/ndULSwEwIbtbuL7FZJkAIbKlC8X9uz0NDWrw+o8ADZue5cNYFH+KkMNMlmOg7+OHsDG25PmzEzMTDDoUf2/3RFweBnhsVBEBb9kXq3kP6/2D3fvnRYv6y0PT/UmG5e7frP5Uw1gl/VnmfVG9EQZswApHT2F950TrOYkAayrh1mGBi+jch8vIw10RgBL3GW7jAoTYgU5SzRijaQsAmDQ9ShrAjD5fjCueVxyoPkOb2z9GQHM3boq6m7LAjkqRQBMYFPTUC76xygf0Evtx+o2zlGXv9RsxoaFW1Q2AANrgGQZ8cQEwOw6JXVnIdpMNOoZvcjkrZUQI2TpYATYFis9zHV9V6/sMvvMV39331s2AFZeGAJ745HHEiAvNgpgLkQm3PEDmFwtJ9sIdD1hPCu+LULAFf0GYGns0G46pWIjKQNrvqgpzjgA1oxgOiPZAAw1myJui5eZqcHuJ7I5dLiCeHdMMAmMz6tBGlwGoTnyCR/AaOtuEbmwPclihERXUMg+XV2+HUF0kjNrxFHPKNhvXI6Ev+LXubA9OLgANiWw0hP0pc9M1Ewk1hzPl2Ftr9TanHIdLleWHtY37LzSe2t0u8ub6YcX3U4bl7vmxG8++wfK0q/emRr/gCaUUPc/yKUCmFTt61uoDn1JYwBGypTbywZg0ZMLAzAhckoHYCniazkAc2ID+70XBjArdEfuKhkHMA9rIfKoBjXH+gDMNUqIzBL8ocgxGH3KBNgDXX9GAFPRxxeLQImOoyIQy65Djg3AxoXG8Aa5Qkz99iLfDuaz6hpXXpCLVIPq73/1TlFcv6e19AKwZnVX9fPlc/25r5uat67u0Hadd+Wm5z1kQoKTsawQwMTeBU+jACuaeQAMlhURJ0uxQ2umJQAMNCnwUACM87T8i2v4WQiM1ywOwMoudEcCWFT4eRyEG4sCMN8UvfF1s2NuzEORGGMtw3mcqZpVjl9iAjWporrYs2LIfCz8dXwA1lW0lrl+EW/WzADM4BywnpVv+pdLE2YQm4HQyf2nzbquN/Vfp/33lc03vzSRnbx+tyK0E/K2G5PsDDorMQlxHlwSADCx+loawOTecLchyODsBmDzmEkIYKACfzgAxkgqtXDU1fsRUme2yCkArLErGcUFMoICsMFsatFsRCVyO7y9XoNH5OjAFs2FQ5KWsLkAzKZw6ZYE0mXV97bTS7a7qybuPC5Hw1/HCGDDn8MOLoHOQl6uWA6EvvAYdZofwK70pvOGsF7QI1zWthv1Tz0GpuUGfdnXJDuJASxUUwUe5HIBTH43p6kDbtHcACyJmXQABitTBwRgg7SoNyoZgKViJEhqDsqqgvlpa2BGQF0ErlG0hQHYJHvOefjKOkep8QmPhFXC0hUwliF7BEB4vRuoglIDBCQZ8pyVuJYNwOAKQFksgHHMp49uUB4YzwrVMZcIwPwJnt8uTv5M/zirh76sbTfaj3799sNbt37yBdJlNDukBwyaNBXUCe4UhcwN0I6I2fQIxlQQBLAU1wqZ8IQ1mcMMdnoaAsBAWlJ7cCwBwKzEuEKQCMAAnR6cwhoenuC+BUKb7mgh95KO59uAExJ8BSUKYIMb7mPFrl0kPhxKFcwoXoDSSzY+MkTSb+6maGuCbSWJs4u9tTv7Wo6HvzYAY1WKGaIbkSY45h/ZMZcGwKwxyfEZPc/QHDS7Hlb/vejnIJ71P2VFkZ8vIH4On54DwFZBYDHzsfOSACZiymF4kSZpxZkNYEDFgwKwlu/VKCKOSRoAAw5B4lxAU/IjOalVREEjEYj0Ki4LwBoH7g6g4XhJ8lxMc0VzBkk2F4ENAEy06cS3ks4U0Y4FyimQbACGUADKMjp07crQR2AIFzkADOGDWLeNPXCjXJyanZFuJugw7f5+cbM2d9Vsv9GCWNlsw4F0BcsP6/kGn1vsoUrsVxWTiRkZApMwQje/AVgik7QbvwEYSQY94uAGQGrnnmFnXKLONlhKwJ0OA1YBNBN1Qz/r569FApjXsep/sO4nLRfEkB5YemUqSXTA0/7T9TyLZIPUW+bwzWprWddyRPx1ZADmbFsY1WKOi0oJYO7Gi9bbTdHzT7kyf+5/81n7tS8DYPVOHAa3zrp9EPWOHGI7bwyywHu8gV2lonYj+1URcxSzImE38RDYBmAzmZwHwPjsgbS3DAAbb3sAG8NhAdh0XyrU2BGIlDzHIXmyjwzFo9lIEfMBFBEAm/Z04sfn5otrrfIxA4BRr3zxBGYBWKZ8eM6OfXPrSsa1bACGUQDK8gCs+2taL+bJDcxMIgALVP05AKxverxB2n/6N3pbjbeqn8/vvP6g0ntypyh+/Bf16q/LXTfudbm7/gCYJSgA1
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BuxCd5x8lJRJtEfz80
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class="img-fluid quarto-figure quarto-figure-center figure-img" style="width:1728cm"></p> 3304</figure> 3305</div> 3306</div> 3307</div> 3308<div style="page-break-after: always;"></div> 3309</section> 3310<section id="download-and-process-all-required-data" class="level2"> 3311<h2 class="anchored" data-anchor-id="download-and-process-all-required-data">Download and Process All Required Data</h2> 3312<p>Data at the U.S. level is pulled first. This data is used in determining distress status of each block group. Next, block group-level data is pulled and pre-processed.</p> 3313<section id="acs-data" class="level3"> 3314<h3 class="anchored" data-anchor-id="acs-data">ACS Data</h3> 3315<section id="u.s.-data" class="level4"> 3316<h4 class="anchored" data-anchor-id="u.s.-data">U.S. Data</h4> 3317<p>Create collection of distressed indicator-related data at U.S. level for each year between 2009 and 2023.</p> 3318<div class="cell"> 3319<details class="code-fold"> 3320<summary>Code</summary> 3321<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="co"># set US data for 2009 and 2010</span></span> 3322<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>us_data<span class="fl">.2009</span> <span class="ot"><-</span> <span class="fu">data.frame</span>(</span> 3323<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a> <span class="at">Name =</span> <span class="st">'United States'</span>,</span> 3324<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a> <span class="at">year =</span> <span class="dv">
33242009</span>,</span> 3325<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a> <span class="at">tot_pop =</span> <span class="dv">301461533</span>,</span> 3326<span id="cb2-6"><a href="#cb2-6" aria-hidden="true" tabindex="-1"></a> <span class="at">pct_below_poverty =</span> <span class="fl">0.143</span>,</span> 3327<span id="cb2-7"><a href="#cb2-7" aria-hidden="true" tabindex="-1"></a> <span class="at">median_income =</span> <span class="dv">64000</span>,</span> 3328<span id="cb2-8"><a href="#cb2-8" aria-hidden="true" tabindex="-1"></a> <span class="at">per_capita_income =</span> <span class="dv">39307</span>,</span> 3329<span id="cb2-9"><a href="#cb2-9" aria-hidden="true" tabindex="-1"></a> <span class="at">pct_over_16_didnt_work =</span> <span class="fl">0.2133001</span></span> 3330<span id="cb2-10"><a href="#cb2-10" aria-hidden="true" tabindex="-1"></a>) <span class="sc">%>%</span></span> 3331<span id="cb2-11"><a href="#cb2-11" aria-hidden="true" tabindex="-1"></a> <span class="fu">as_tibble</span>()</span> 3332<span id="cb2-12"><a href="#cb2-12" aria-hidden="true" tabindex="-1"></a></span> 3333<span id="cb2-13"><a href="#cb2-13" aria-hidden="true" tabindex="-1"></a>us_data<span class="fl">.2010</span> <span class="ot"><-</span> <span class="fu">data.frame</span>(</span> 3334<span id="cb2-14"><a href="#cb2-14" aria-hidden="true" tabindex="-1"></a> <span class="at">Name =</span> <span class="st">'United States'</span>,</span> 3335<span id="cb2-15"><a href="#cb2-15" aria-hidden="true" tabindex="-1"></a> <span class="at">year =</span> <span class="dv">2010</span>,</span> 3336<span id="cb2-16"><a href="#cb2-16" aria-hidden="true" tabindex="-1"></a> <span class="at">tot_pop =</span> <span class="dv">312471327</span>,</span> 3337<span id="cb2-17"><a href="#cb2-17" aria-hidden="true" tabindex="-1"></a> <span class="at">pct_below_poverty =</span> <span class="fl">0.153</span>,</span> 3338<span id="cb2-18"><a href="#cb2-18" aria-hidden="true" tabindex="-1"></a> <span class="at">median_income =</span> <span class="dv">64400</span>,</span> 3339<span id="cb2-19"><a href="#cb2-19" aria-hidden="true" tabindex="-1"></a> <span class="at">per_capita_income =</span> <span class="dv">40557</span>,</span> 3340<span id="cb2-20"><a href="#cb2-20" aria-hidden="true" tabindex="-1"></a> <span class="at">pct_over_16_didnt_work =</span> <span class="fl">0.223817</span></span> 3341<span id="cb2-21"><a href="#cb2-21" aria-hidden="true" tabindex="-1"></a>) <span class="sc">%>%</span></span> 3342<span id="cb2-22"><a href="#cb2-22" aria-hidden="true" tabindex="-1"></a> <span class="fu">as_tibble</span>()</span> 3343<span id="cb2-23"><a href="#cb2-23" aria-hidden="true" tabindex="-1"></a></span> 3344<span id="cb2-24"><a href="#cb2-24" aria-hidden="true" tabindex="-1"></a><span class="co"># pull US data from ACS for 2011-2023</span></span> 3345<span id="cb2-25"><a href="#cb2-25" aria-hidden="true" tabindex="-1"></a>us_data<span class="fl">.2011</span>_2023 <span class="ot"><-</span> </span> 3346<span id="cb2-26"><a href="#cb2-26" aria-hidden="true" tabindex="-1"></a> <span class="fu">map2</span>(<span class="dv">2011</span><span class="sc">:</span><span class="dv">2023</span>, <span class="fu">rep</span>(<span class="st">'us'</span>, <span class="at">times =</span> <span class="dv">13</span>), get_acs_data) <span class="sc">%>%</span> </span> 3347<span id="cb2-27"><a href="#cb2-27" aria-hidden="true" tabindex="-1"></a> <span class="fu">bind_rows</span>() <span class="sc">%>%</span></span> 3348<span id="cb2-28"><a href="#cb2-28" aria-hidden="true" tabindex="-1"></a> <span class="fu">rename_with</span>(<span class="sc">~</span><span class="fu">str_replace</span>(., <span class="st">'E$'</span>, <span class="st">''</span>), <span class="at">.cols =</span> <span class="fu">ends_with</span>(<span class="st">'E'</span>)) <span class="sc">%>%</span></span> 3349<span id="cb2-29"><a href="#cb2-29" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(<span class="sc">-</span><span class="fu">ends_with</span>(<span class="st">'M'</span>)) <span class="sc">%>%</span></span> 3350<span id="cb2-30"><a href="#cb2-30" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(</span> 3351<span id="cb2-31"><a href="#cb2-31" aria-hidden="true" tabindex="-1"></a> <span class="at">Name =</span> <span class="st">'United States'</span>,</span> 3352<span id="cb2-32"><a href="#cb2-32" aria-hidden="true" tabindex="-1"></a> <span class="at">pct_below_poverty =</span> tot_below_poverty <span class="sc">/</span> tot_pop,</span> 3353<span id="cb2-33"><a href="#cb2-33" aria-hidden="true" tabindex="-1"></a> <span class="at">pop_over_16_didnt_work =</span> males_didnt_work <span class="sc">+</span> females_didnt_work,</span> 3354<span id="cb2-34"><a href="#cb2-34" aria-hidden="true" tabindex="-1"></a> <span class="at">pct_over_16_didnt_work =</span> pop_over_16_didnt_work <span class="sc">/</span> tot_pop_over_16,</span> 3355<span id="cb2-35"><a href="#cb2-35" aria-hidden="true" tabindex="-1"></a> <span class="at">pop_over_15_divorced =</span> males_divorced <span class="sc">+</span> females_divorced,</span> 3356<span id="cb2-36"><a href="#cb2-36" aria-hidden="true" tabindex="-1"></a> <span class="at">pct_divorced =</span> pop_over_15_divorced <span class="sc">/</span> tot_pop_over_15</span> 3357<span id="cb2-37"><a href="#cb2-37" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">%>%</span></span> 3358<span id="cb2-38"><a href="#cb2-38" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(Name, year, tot_pop, per_capita_income, </span> 3359<span id="cb2-39"><a href="#cb2-39" aria-hidden="true" tabindex="-1"></a> pct_over_16_didnt_work, median_income, pct_below_poverty, pct_divorced) <span class="sc">%>%</span></span> 3360<span id="cb2-40"><a href="#cb2-40" aria-hidden="true" tabindex="-1"></a> <span class="fu">
3360as_tibble</span>()</span> 3361<span id="cb2-41"><a href="#cb2-41" aria-hidden="true" tabindex="-1"></a></span> 3362<span id="cb2-42"><a href="#cb2-42" aria-hidden="true" tabindex="-1"></a><span class="co"># combine data into single tibble</span></span> 3363<span id="cb2-43"><a href="#cb2-43" aria-hidden="true" tabindex="-1"></a>us_data <span class="ot"><-</span> <span class="fu">bind_rows</span>(us_data<span class="fl">.2009</span>, us_data<span class="fl">.2010</span>, us_data<span class="fl">.2011</span>_2023)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div> 3364</details> 3365</div> 3366</section> 3367<section id="block-group-data" class="level4"> 3368<h4 class="anchored" data-anchor-id="block-group-data">Block Group Data</h4> 3369<p>Define an extract to submit to NHGIS, then download, load, and process data.</p> 3370<section id="download-set-up" class="level5"> 3371<h5 class="anchored" data-anchor-id="download-set-up">Download Set-Up</h5> 3372<div class="cell"> 3373<details class="code-fold"> 3374<summary>Code</summary> 3375<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="co"># set datasets to pull block and block group data from (2009-2023)</span></span> 3376<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>datasets <span class="ot"><-</span> <span class="fu">c</span>(<span class="st">'2005_2009_ACS5a'</span>, <span class="st">'2006_2010_ACS5a'</span>, <span class="st">'2007_2011_ACS5a'</span>, </span> 3377<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a> <span class="st">'2008_2012_ACS5a'</span>, <span class="st">'2009_2013_ACS5a'</span>, <span class="st">'2010_2014_ACS5a'</span>,</span> 3378<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a> <span class="st">'2011_2015_ACS5a'</span>, <span class="st">'2012_2016_ACS5a'</span>, <span class="st">'2013_2017_ACS5a'</span>,</span> 3379<span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a> <span class="st">'2014_2018_ACS5a'</span>, <span class="st">'2015_2019_ACS5a'</span>, <span class="st">'2016_2020_ACS5a'</span>,</span> 3380<span id="cb3-6"><a href="#cb3-6" aria-hidden="true" tabindex="-1"></a> <span class="st">'2017_2021_ACS5a'</span>, <span class="st">'2018_2022_ACS5a'</span>, <span class="st">'2019_2023_ACS5a'</span>)</span> 3381<span id="cb3-7"><a href="#cb3-7" aria-hidden="true" tabindex="-1"></a></span> 3382<span id="cb3-8"><a href="#cb3-8" aria-hidden="true" tabindex="-1"></a><span class="co"># extract specifications with variables and geographic level (block group) for each dataset</span></span> 3383<span id="cb3-9"><a href="#cb3-9" aria-hidden="true" tabindex="-1"></a>bg_dataset_spec <span class="ot"><-</span> <span class="fu">map</span>(</span> 3384<span id="cb3-10"><a href="#cb3-10" aria-hidden="true" tabindex="-1"></a> datasets,</span> 3385<span id="cb3-11"><a href="#cb3-11" aria-hidden="true" tabindex="-1"></a> <span class="sc">~</span> <span class="fu">ds_spec</span>(</span> 3386<span id="cb3-12"><a href="#cb3-12" aria-hidden="true" tabindex="-1"></a> .x,</span> 3387<span id="cb3-13"><a href="#cb3-13" aria-hidden="true" tabindex="-1"></a> <span class="co">#data_tables = c('B01003', 'B11001', 'B17021', 'B19127'),</span></span> 3388<span id="cb3-14"><a href="#cb3-14" aria-hidden="true" tabindex="-1"></a> <span class="at">data_tables =</span> <span class="fu">c</span>(<span class="st">'B01003'</span>, <span class="st">'B17021'</span>, <span class="st">'B19101'</span>, <span class="st">'B19127'</span>, </span> 3389<span id="cb3-15"><a href="#cb3-15" aria-hidden="true" tabindex="-1"></a> <span class="st">'B19301'</span>, <span class="st">'B23022'</span>, <span class="st">'B25001'</span>, <span class="st">'B25004'</span>, </span> 3390<span id="cb3-16"><a href="#cb3-16" aria-hidden="true" tabindex="-1"></a>
3390 <span class="st">'B25008'</span>, <span class="st">'B25065'</span>, <span class="st">'B11001'</span>, <span class="st">'B25010'</span>, <span class="st">'B12001'</span>),</span> 3391<span id="cb3-17"><a href="#cb3-17" aria-hidden="true" tabindex="-1"></a> <span class="at">geog_levels =</span> <span class="st">'blck_grp'</span></span> 3392<span id="cb3-18"><a href="#cb3-18" aria-hidden="true" tabindex="-1"></a> )</span> 3393<span id="cb3-19"><a href="#cb3-19" aria-hidden="true" tabindex="-1"></a>)</span> 3394<span id="cb3-20"><a href="#cb3-20" aria-hidden="true" tabindex="-1"></a></span> 3395<span id="cb3-21"><a href="#cb3-21" aria-hidden="true" tabindex="-1"></a><span class="co"># set list of shapefiles to include in extract</span></span> 3396<span id="cb3-22"><a href="#cb3-22" aria-hidden="true" tabindex="-1"></a>bg_shps <span class="ot"><-</span> <span class="fu">c</span>(<span class="st">'360_blck_grp_2000_tl2009'</span>, <span class="st">'360_blck_grp_2010_tl2010'</span>,</span> 3397<span id="cb3-23"><a href="#cb3-23" aria-hidden="true" tabindex="-1"></a> <span class="st">'360_blck_grp_2011_tl2011'</span>, <span class="st">'360_blck_grp_2012_tl2012'</span>,</span> 3398<span id="cb3-24"><a href="#cb3-24" aria-hidden="true" tabindex="-1"></a> <span class="st">'360_blck_grp_2013_tl2013'</span>, <span class="st">'360_blck_grp_2014_tl2014'</span>,</span> 3399<span id="cb3-25"><a href="#cb3-25" aria-hidden="true" tabindex="-1"></a> <span class="st">'360_blck_grp_2015_tl2015'</span>, <span class="st">'360_blck_grp_2016_tl2016'</span>,</span> 3400<span id="cb3-26"><a href="#cb3-26" aria-hidden="true" tabindex="-1"></a> <span class="st">'360_blck_grp_2017_tl2017'</span>, <span class="st">'360_blck_grp_2018_tl2018'</span>,</span> 3401<span id="cb3-27"><a href="#cb3-27" aria-hidden="true" tabindex="-1"></a> <span class="st">'360_blck_grp_2019_tl2019'</span>, <span class="st">'360_blck_grp_2020_tl2020'</span>,</span> 3402<span id="cb3-28"><a href="#cb3-28" aria-hidden="true" tabindex="-1"></a> <span class="st">'360_blck_grp_2021_tl2021'</span>, <span class="st">'360_blck_grp_2022_tl2022'</span>,</span> 3403<span id="cb3-29"><a href="#cb3-29" aria-hidden="true" tabindex="-1"></a> <span class="st">'360_blck_grp_2023_tl2023'</span>)</span> 3404<span id="cb3-30"><a href="#cb3-30" aria-hidden="true" tabindex="-1"></a></span> 3405<span id="cb3-31"><a href="#cb3-31" aria-hidden="true" tabindex="-1"></a><span class="do">#########################################################</span></span> 3406<span id="cb3-32"><a href="#cb3-32" aria-hidden="true" tabindex="-1"></a><span class="co"># RUN THIS CODE THE FIRST TIME, COMMENT IT OUT AFTERWARDS</span></span> 3407<span id="cb3-33"><a href="#cb3-33" aria-hidden="true" tabindex="-1"></a><span class="do">#########################################################</span></span> 3408<span id="cb3-34"><a href="#cb3-34" aria-hidden="true" tabindex="-1"></a></span> 3409<span id="cb3-35"><a href="#cb3-35" aria-hidden="true" tabindex="-1"></a><span class="co"># # define extract used to pull from NHGIS</span></span> 3410<span id="cb3-36"><a href="#cb3-36" aria-hidden="true" tabindex="-1"></a><span class="co"># bg_extract <- define_extract_nhgis(</span></span> 3411<span id="cb3-37"><a href="#cb3-37" aria-hidden="true" tabindex="-1"></a><span class="co"># description = 'Block Groups ACS Data and NY Shapefiles (2009-2023)',</span></span> 3412<span id="cb3-38"><a href="#cb3-38" aria-hidden="true" tabindex="-1"></a><span class="co"># datasets = bg_dataset_spec,</span></span> 3413<span id="cb3-39"><a href="#cb3-39" aria-hidden="true" tabindex="-1"></a><span class="co"># geographic_extents = '360',</span></span> 3414<span id="cb3-40"><a href="#cb3-40" aria-hidden="true" tabindex="-1"></a><span class="co"># shapefiles = bg_shps</span></span> 3415<span id="cb3-41"><a href="#cb3-41" aria-hidden="true" tabindex="-1"></a><span class="co"># )</span></span> 3416<span id="cb3-42"><a href="#cb3-42" aria-hidden="true" tabindex="-1"></a><span class="co"># </span></span> 3417<span id="cb3-43"><a href="#cb3-43" aria-hidden="true" tabindex="-1"></a><span class="co"># # create data pull and store it in your account</span></span> 3418<span id="cb3-44"><a href="#cb3-44" aria-hidden="true" tabindex="-1"></a><span class="co"># final_bg_extract <- wait_for_extract(submit_extract(bg_extract))</span></span> 3419<span id="cb3-45"><a href="#cb3-45" aria-hidden="true" tabindex="-1"></a><span class="co"># </span></span> 3420<span id="cb3-46"><a href="#cb3-46" aria-hidden="true" tabindex="-1"></a><span class="co"># # download files to computer</span></span> 3421<span id="cb3-47"><a href="#cb3-47" aria-hidden="true" tabindex="-1"></a><span class="co"># bg_nhgis_files <- download_extract(final_bg_extract, download_dir = nhgis_data_dir)</span></span> 3422<span id="cb3-48"><a href="#cb3-48" aria-hidden="true" tabindex="-1"></a></span> 3423<span id="cb3-49"><a href="#cb3-49" aria-hidden="true" tabindex="-1"></a><span class="do">#########################################################</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div> 3424</details> 3425</div> 3426</section> 3427<section id="load-and-process-block-group-data" class="level5"> 3428<h5 class="anchored" data-anchor-id="load-and-process-block-group-data">Load and Process Block Group Data</h5> 3429<div class="cell"> 3430<details class="code-fold"> 3431<summary>Code</summary> 3432<div class="sourceCode cell-code" id="cb4"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="co"># get zip files</span></span> 3433<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a><span class="co"># CHANGE THIS ACCORDINGLY TO REFLECT YOUR FILE STRUCTURE</span></span> 3434<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a>nhgis_bg_data <span class="ot"><-</span> <span class="fu">list.files</span>(<span class="at">path =</span> nhgis_data_dir, <span class="at">pattern =</span> <span class="st">'csv'</span>, <span class="at">full.names =</span> <span class="cn">TRUE</span>)[[<span class="dv">4</span>]]</span> 3435<span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a>nhgis_bg_shps <span class="ot"><-</span> <span class="fu">list.files</span>(<span class="at">path =</span> nhgis_data_dir, <span class="at">pattern =</span> <span class="st">'shape'</span>, <span class="at">full.names =</span> <span class="cn">TRUE</span>)[[<span class="dv">4</span>]]</span> 3436<span id="cb4-5"><a href="#cb4-5" aria-hidden="true" tabindex="-1"></a></span> 3437<span id="cb4-6"><a href="#cb4-6" aria-hidden="true" tabindex="-1"></a><span class="do">#########################################</span></span> 3438<span id="cb4-7"><a href="#cb4-7" aria-hidden="true" tabindex="-1"></a><span class="co"># load tabular data for all block groups</span></span> 3439<span id="cb4-8"><a href="#cb4-8" aria-hidden="true" tabindex="-1"></a><span class="do">#########################################</span></span> 3440<span id="cb4-9"><a href="#cb4-9" aria-hidden="true" tabindex="-1"></a></span> 3441<span id="cb4-10"><a href="#cb4-10" aria-hidden="true" tabindex="-1"></a><span class="co"># initiatlize list of block group data for each year</span></span> 3442<span id="cb4-11"><a href="#cb4-11" aria-hidden="true" tabindex="-1"></a>bg_data_yrs <span class="ot"><-</span> <span class="fu">c</span>()</span> 3443<span id="cb4-12"><a href="#cb4-12" aria-hidden="true" tabindex="-1"></a></span> 3444<span id="cb4-13"><a href="#cb4-13" aria-hidden="true" tabindex="-1"></a><span class="co"># iterate over each year</span></span> 3445<span id="cb4-14"><a href="#cb4-14" aria-hidden="true" tabindex="-1"></a><span class="co"># filter for relevant study variables, process the data, then merge data</span></span> 3446<span id="cb4-15"><a href="#cb4-15" aria-hidden="true" tabindex="-1"></a><span class="co"># with respective spatial file</span></span> 3447<span id="cb4-16"><a href="#cb4-16" aria-hidden="true" tabindex="-1"></a><span class="cf">for</span> (yr <span class="cf">in</span> <span class="dv">
34472009</span><span class="sc">:</span><span class="dv">2023</span>) {</span> 3448<span id="cb4-17"><a href="#cb4-17" aria-hidden="true" tabindex="-1"></a> <span class="co">#print(as.character(yr))</span></span> 3449<span id="cb4-18"><a href="#cb4-18" aria-hidden="true" tabindex="-1"></a> </span> 3450<span id="cb4-19"><a href="#cb4-19" aria-hidden="true" tabindex="-1"></a> <span class="co"># load and pre-process data</span></span> 3451<span id="cb4-20"><a href="#cb4-20" aria-hidden="true" tabindex="-1"></a> d <span class="ot"><-</span> </span> 3452<span id="cb4-21"><a href="#cb4-21" aria-hidden="true" tabindex="-1"></a> <span class="fu">read_nhgis</span>(nhgis_bg_data, <span class="at">file_select =</span> <span class="fu">matches</span>(<span class="fu">as.character</span>(yr))) <span class="sc">%>%</span></span> 3453<span id="cb4-22"><a href="#cb4-22" aria-hidden="true" tabindex="-1"></a> .[<span class="fu">cumall</span>(<span class="sc">!</span>(<span class="st">'NAME_M'</span> <span class="sc">==</span> <span class="fu">colnames</span>(.)))] <span class="sc">%>%</span></span> 3454<span id="cb4-23"><a href="#cb4-23" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">YEAR =</span> <span class="fu">str_split</span>(YEAR, <span class="st">'-'</span>, <span class="at">simplify =</span> <span class="cn">TRUE</span>)[,<span class="dv">2</span>],</span> 3455<span id="cb4-24"><a href="#cb4-24" aria-hidden="true" tabindex="-1"></a> <span class="at">COUNTY =</span> <span class="fu">str_split</span>(COUNTY, <span class="st">' '</span>, <span class="at">simplify =</span> <span class="cn">TRUE</span>)[,<span class="dv">1</span>]) <span class="sc">%>%</span></span> 3456<span id="cb4-25"><a href="#cb4-25" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(COUNTY <span class="sc">%in%</span> southern_tier_counties)</span> 3457<span id="cb4-26"><a href="#cb4-26" aria-hidden="true" tabindex="-1"></a> </span> 3458<span id="cb4-27"><a href="#cb4-27" aria-hidden="true" tabindex="-1"></a> <span class="co"># load shapefile</span></span> 3459<span id="cb4-28"><a href="#cb4-28" aria-hidden="true" tabindex="-1"></a> s <span class="ot"><-</span> <span class="fu">read_ipums_sf</span>(nhgis_bg_shps, <span class="at">file_select =</span> <span class="fu">matches</span>(<span class="fu">as.character</span>(yr)))</span> 3460<span id="cb4-29"><a href="#cb4-29" aria-hidden="true" tabindex="-1"></a> </span> 3461<span id="cb4-30"><a href="#cb4-30" aria-hidden="true" tabindex="-1"></a> <span class="co"># 2009</span></span> 3462<span id="cb4-31"><a href="#cb4-31" aria-hidden="true" tabindex="-1"></a> <span class="cf">if</span> (yr <span class="sc">==</span> <span class="dv">2009</span>) {</span> 3463<span id="cb4-32"><a href="#cb4-32" aria-hidden="true" tabindex="-1"></a> d <span class="ot"><-</span> </span> 3464<span id="cb4-33"><a href="#cb4-33" aria-hidden="true" tabindex="-1"></a> d <span class="sc">%>%</span></span> 3465<span id="cb4-34"><a href="#cb4-34" aria-hidden="true" tabindex="-1"></a>
3465 <span class="fu">mutate</span>(<span class="at">GEOID =</span> <span class="fu">str_split</span>(GEOID, <span class="st">'US'</span>, <span class="at">simplify =</span> <span class="cn">TRUE</span>)[,<span class="dv">2</span>]) <span class="sc">%>%</span></span> 3466<span id="cb4-35"><a href="#cb4-35" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate_at</span>(<span class="fu">vars</span>(GEOID), as.numeric) <span class="sc">%>%</span></span> 3467<span id="cb4-36"><a href="#cb4-36" aria-hidden="true" tabindex="-1"></a> <span class="fu">rename</span>(</span> 3468<span id="cb4-37"><a href="#cb4-37" aria-hidden="true" tabindex="-1"></a> <span class="at">year =</span> YEAR, <span class="at">county =</span> COUNTY, <span class="at">name =</span> NAME_E,</span> 3469<span id="cb4-38"><a href="#cb4-38" aria-hidden="true" tabindex="-1"></a> <span class="at">tot_pop =</span> <span class="dv">39</span>, <span class="at">tot_hhs =</span> <span class="dv">40</span>, <span class="at">tot_families =</span> <span class="dv">41</span>,</span> 3470<span id="cb4-39"><a href="#cb4-39" aria-hidden="true" tabindex="-1"></a> <span class="at">tot_pop_over_15 =</span> <span class="dv">49</span>, <span class="at">males_divorced =</span> <span class="dv">58</span>, <span class="at">females_divorced =</span> <span class="dv">67</span>,</span> 3471<span id="cb4-40"><a href="#cb4-40" aria-hidden="true" tabindex="-1"></a> <span class="at">tot_pop_pov_count =</span> <span class="dv">68</span>, <span class="at">pop_in_poverty =</span> <span class="dv">69</span>, <span class="at">agg_fam_income =</span> <span class="dv">120</span>,</span> 3472<span id="cb4-41"><a href="#cb4-41" aria-hidden="true" tabindex="-1"></a> <span class="at">per_capita_income =</span> <span class="dv">121</span>, <span class="at">pop_over_16 =</span> <span class="dv">122</span>, <span class="at">males_didnt_work =</span> <span class="dv">146</span>,</span> 3473<span id="cb4-42"><a href="#cb4-42" aria-hidden="true" tabindex="-1"></a> <span class="at">females_didnt_work =</span> <span class="dv">170</span>, <span class="at">tot_housing_units =</span> <span class="dv">171</span>, <span class="at">vacant_housing_units =</span> <span class="dv">172</span>,</span> 3474<span id="cb4-43"><a href="#cb4-43" aria-hidden="true" tabindex="-1"></a> <span class="at">renter_occ_housing_units =</span> <span class="dv">182</span>, <span class="at">agg_gross_rent =</span> <span class="dv">186</span></span> 3475<span id="cb4-44"><a href="#cb4-44" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">%>%</span></span> 3476<span id="cb4-45"><a href="#cb4-45" aria-hidden="true" tabindex="-1"></a> .[,<span class="fu">c</span>(<span class="dv">2</span>, <span class="dv">8</span>, <span class="dv">37</span><span class="sc">:</span><span class="dv">41</span>, <span class="dv">49</span>, <span class="dv">58</span>, <span class="dv">67</span><span class="sc">:</span><span class="dv">69</span>, <span class="dv">120</span><span class="sc">:</span><span class="dv">122</span>, <span class="dv">146</span>, <span class="dv">170</span><span class="sc">:</span><span class="dv">172</span>, <span class="dv">182</span>, <span class="dv">186</span>)] <span class="sc">%>%</span></span> 3477<span id="cb4-46"><a href="#cb4-46" aria-hidden="true" tabindex="-1"></a>
3477 <span class="fu">relocate</span>(GEOID, <span class="at">.before =</span> year) <span class="sc">%>%</span></span> 3478<span id="cb4-47"><a href="#cb4-47" aria-hidden="true" tabindex="-1"></a> <span class="fu">relocate</span>(<span class="fu">c</span>(county, year), <span class="at">.after =</span> name)</span> 3479<span id="cb4-48"><a href="#cb4-48" aria-hidden="true" tabindex="-1"></a> </span> 3480<span id="cb4-49"><a href="#cb4-49" aria-hidden="true" tabindex="-1"></a> s <span class="ot"><-</span></span> 3481<span id="cb4-50"><a href="#cb4-50" aria-hidden="true" tabindex="-1"></a> s <span class="sc">%>%</span></span> 3482<span id="cb4-51"><a href="#cb4-51" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">GEOID =</span> <span class="fu">as.numeric</span>(<span class="fu">paste</span>(STATEFP00, COUNTYFP00, TRACTCE00, BLKGRPCE00, <span class="at">sep=</span><span class="st">''</span>))) <span class="sc">%>%</span></span> 3483<span id="cb4-52"><a href="#cb4-52" aria-hidden="true" tabindex="-1"></a>
3483 <span class="fu">filter</span>(GEOID <span class="sc">%in%</span> (d<span class="sc">$</span>GEOID)) <span class="sc">%>%</span></span> 3484<span id="cb4-53"><a href="#cb4-53" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(GEOID, geometry) <span class="sc">%>%</span></span> 3485<span id="cb4-54"><a href="#cb4-54" aria-hidden="true" tabindex="-1"></a> <span class="fu">st_transform</span>(<span class="at">crs =</span> <span class="fu">st_crs</span>(study_bgs))</span> 3486<span id="cb4-55"><a href="#cb4-55" aria-hidden="true" tabindex="-1"></a> } <span class="cf">else</span> <span class="cf">if</span> (yr <span class="sc">==</span> <span class="dv">2010</span>) { <span class="co"># 2010</span></span> 3487<span id="cb4-56"><a href="#cb4-56" aria-hidden="true" tabindex="-1"></a> d <span class="ot"><-</span></span> 3488<span id="cb4-57"><a href="#cb4-57" aria-hidden="true" tabindex="-1"></a> d <span class="sc">%>%</span></span> 3489<span id="cb4-58"><a href="#cb4-58" aria-hidden="true" tabindex="-1"></a>
3489 <span class="fu">mutate</span>(<span class="at">GEOID =</span> <span class="fu">str_split</span>(GEOID, <span class="st">'US'</span>, <span class="at">simplify =</span> <span class="cn">TRUE</span>)[,<span class="dv">2</span>]) <span class="sc">%>%</span></span> 3490<span id="cb4-59"><a href="#cb4-59" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate_at</span>(<span class="fu">vars</span>(GEOID), as.numeric) <span class="sc">%>%</span></span> 3491<span id="cb4-60"><a href="#cb4-60" aria-hidden="true" tabindex="-1"></a> <span class="fu">rename</span>(</span> 3492<span id="cb4-61"><a href="#cb4-61" aria-hidden="true" tabindex="-1"></a> <span class="at">year =</span> YEAR, <span class="at">county =</span> COUNTY, <span class="at">name =</span> NAME_E,</span> 3493<span id="cb4-62"><a href="#cb4-62" aria-hidden="true" tabindex="-1"></a> <span class="at">tot_pop =</span> <span class="dv">41</span>, <span class="at">tot_hhs =</span> <span class="dv">42</span>, <span class="at">tot_families =</span> <span class="dv">43</span>,</span> 3494<span id="cb4-63"><a href="#cb4-63" aria-hidden="true" tabindex="-1"></a> <span class="at">tot_pop_over_15 =</span> <span class="dv">51</span>, <span class="at">males_divorced =</span> <span class="dv">60</span>, <span class="at">females_divorced =</span> <span class="dv">69</span>,</span> 3495<span id="cb4-64"><a href="#cb4-64" aria-hidden="true" tabindex="-1"></a> <span class="at">tot_pop_pov_count =</span> <span class="dv">70</span>, <span class="at">pop_in_poverty =</span> <span class="dv">71</span>, <span class="at">agg_fam_income =</span> <span class="dv">122</span>,</span> 3496<span id="cb4-65"><a href="#cb4-65" aria-hidden="true" tabindex="-1"></a> <span class="at">per_capita_income =</span> <span class="dv">123</span>, <span class="at">pop_over_16 =</span> <span class="dv">124</span>, <span class="at">males_didnt_work =</span> <span class="dv">148</span>,</span> 3497<span id="cb4-66"><a href="#cb4-66" aria-hidden="true" tabindex="-1"></a> <span class="at">females_didnt_work =</span> <span class="dv">172</span>, <span class="at">tot_housing_units =</span> <span class="dv">173</span>, <span class="at">vacant_housing_units =</span> <span class="dv">174</span>,</span> 3498<span id="cb4-67"><a href="#cb4-67" aria-hidden="true" tabindex="-1"></a> <span class="at">renter_occ_housing_units =</span> <span class="dv">184</span>, <span class="at">agg_gross_rent =</span> <span class="dv">188</span></span> 3499<span id="cb4-68"><a href="#cb4-68" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">%>%</span></span> 3500<span id="cb4-69"><a href="#cb4-69" aria-hidden="true" tabindex="-1"></a> .[,<span class="fu">c</span>(<span class="dv">2</span>, <span class="dv">8</span>, <span class="dv">37</span>, <span class="dv">40</span><span class="sc">:</span><span class="dv">43</span>, <span class="dv">51</span>, <span class="dv">60</span>, <span class="dv">69</span><span class="sc">:</span><span class="dv">71</span>, <span class="dv">122</span><span class="sc">:</span><span class="dv">124</span>, <span class="dv">148</span>, <span class="dv">172</span><span class="sc">:</span><span class="dv">174</span>, <span class="dv">184</span>, <span class="dv">188</span>)] <span class="sc">%>%</span></span> 3501<span id="cb4-70"><a href="#cb4-70" aria-hidden="true" tabindex="-1"></a>
3501 <span class="fu">relocate</span>(GEOID, <span class="at">.before =</span> year) <span class="sc">%>%</span></span> 3502<span id="cb4-71"><a href="#cb4-71" aria-hidden="true" tabindex="-1"></a> <span class="fu">relocate</span>(<span class="fu">c</span>(county, year), <span class="at">.after =</span> name)</span> 3503<span id="cb4-72"><a href="#cb4-72" aria-hidden="true" tabindex="-1"></a> </span> 3504<span id="cb4-73"><a href="#cb4-73" aria-hidden="true" tabindex="-1"></a> s <span class="ot"><-</span> s <span class="sc">%>%</span></span> 3505<span id="cb4-74"><a href="#cb4-74" aria-hidden="true" tabindex="-1"></a> <span class="fu">rename</span>(<span class="at">GEOID =</span> <span class="st">`</span><span class="at">GEOID10</span><span class="st">`</span>) <span class="sc">%>%</span></span> 3506<span id="cb4-75"><a href="#cb4-75" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="fu">across</span>(GEOID, as.numeric)) <span class="sc">%>%</span></span> 3507<span id="cb4-76"><a href="#cb4-76" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(GEOID <span class="sc">%in%</span> (d<span class="sc">$</span>GEOID)) <span class="sc">%>%</span></span> 3508<span id="cb4-77"><a href="#cb4-77" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(GEOID, geometry) <span class="sc">%>%</span></span> 3509<span id="cb4-78"><a href="#cb4-78" aria-hidden="true" tabindex="-1"></a> <span class="fu">st_transform</span>(<span class="at">crs =</span> <span class="fu">st_crs</span>(study_bgs))</span> 3510<span id="cb4-79"><a href="#cb4-79" aria-hidden="true" tabindex="-1"></a> } <span class="cf">else</span> <span class="cf">if</span> (yr <span class="sc">>=</span> <span class="dv">2011</span> <span class="sc">&</span> yr <span class="sc"><=</span> <span class="dv">2020</span>) { <span class="co"># 2011 to 2020</span></span> 3511<span id="cb4-80"><a href="#cb4-80" aria-hidden="true" tabindex="-1"></a> d <span class="ot"><-</span></span> 3512<span id="cb4-81"><a href="#cb4-81" aria-hidden="true" tabindex="-1"></a> d <span class="sc">%>%</span></span> 3513<span id="cb4-82"><a href="#cb4-82" aria-hidden="true" tabindex="-1"></a>
3513 <span class="fu">mutate</span>(<span class="at">GEOID =</span> <span class="fu">str_split</span>(GEOID, <span class="st">'US'</span>, <span class="at">simplify =</span> <span class="cn">TRUE</span>)[,<span class="dv">2</span>]) <span class="sc">%>%</span></span> 3514<span id="cb4-83"><a href="#cb4-83" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="fu">across</span>(GEOID, as.numeric)) <span class="sc">%>%</span></span> 3515<span id="cb4-84"><a href="#cb4-84" aria-hidden="true" tabindex="-1"></a> <span class="fu">rename</span>(</span> 3516<span id="cb4-85"><a href="#cb4-85" aria-hidden="true" tabindex="-1"></a> <span class="at">year =</span> YEAR, <span class="at">county =</span> COUNTY, <span class="at">name =</span> NAME_E,</span> 3517<span id="cb4-86"><a href="#cb4-86" aria-hidden="true" tabindex="-1"></a> <span class="at">tot_pop =</span> <span class="dv">42</span>, <span class="at">tot_hhs =</span> <span class="dv">43</span>, <span class="at">tot_families =</span> <span class="dv">44</span>,</span> 3518<span id="cb4-87"><a href="#cb4-87" aria-hidden="true" tabindex="-1"></a> <span class="at">tot_pop_over_15 =</span> <span class="dv">52</span>, <span class="at">males_divorced =</span> <span class="dv">61</span>, <span class="at">females_divorced =</span> <span class="dv">70</span>,</span> 3519<span id="cb4-88"><a href="#cb4-88" aria-hidden="true" tabindex="-1"></a> <span class="at">tot_pop_pov_count =</span> <span class="dv">71</span>, <span class="at">pop_in_poverty =</span> <span class="dv">72</span>, <span class="at">agg_fam_income =</span> <span class="dv">123</span>,</span> 3520<span id="cb4-89"><a href="#cb4-89" aria-hidden="true" tabindex="-1"></a> <span class="at">per_capita_income =</span> <span class="dv">124</span>, <span class="at">pop_over_16 =</span> <span class="dv">125</span>, <span class="at">males_didnt_work =</span> <span class="dv">149</span>,</span> 3521<span id="cb4-90"><a href="#cb4-90" aria-hidden="true" tabindex="-1"></a> <span class="at">females_didnt_work =</span> <span class="dv">173</span>, <span class="at">tot_housing_units =</span> <span class="dv">174</span>, <span class="at">vacant_housing_units =</span> <span class="dv">175</span>,</span> 3522<span id="cb4-91"><a href="#cb4-91" aria-hidden="true" tabindex="-1"></a> <span class="at">renter_occ_housing_units =</span> <span class="dv">185</span>, <span class="at">agg_gross_rent =</span> <span class="dv">189</span></span> 3523<span id="cb4-92"><a href="#cb4-92" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">%>%</span></span> 3524<span id="cb4-93"><a href="#cb4-93" aria-hidden="true" tabindex="-1"></a> .[,<span class="fu">c</span>(<span class="dv">2</span>, <span class="dv">8</span>, <span class="dv">38</span>, <span class="dv">41</span><span class="sc">:</span><span class="dv">44</span>, <span class="dv">52</span>, <span class="dv">61</span>, <span class="dv">70</span><span class="sc">:</span><span class="dv">72</span>, <span class="dv">123</span><span class="sc">:</span><span class="dv">125</span>, <span class="dv">149</span>, <span class="dv">173</span><span class="sc">:</span><span class="dv">175</span>, <span class="dv">185</span>, <span class="dv">189</span>)] <span class="sc">%>%</span></span> 3525<span id="cb4-94"><a href="#cb4-94" aria-hidden="true" tabindex="-1"></a>
3525 <span class="fu">relocate</span>(GEOID, <span class="at">.before =</span> year) <span class="sc">%>%</span></span> 3526<span id="cb4-95"><a href="#cb4-95" aria-hidden="true" tabindex="-1"></a> <span class="fu">relocate</span>(<span class="fu">c</span>(county, year), <span class="at">.after =</span> name)</span> 3527<span id="cb4-96"><a href="#cb4-96" aria-hidden="true" tabindex="-1"></a> </span> 3528<span id="cb4-97"><a href="#cb4-97" aria-hidden="true" tabindex="-1"></a> s <span class="ot"><-</span> s <span class="sc">%>%</span></span> 3529<span id="cb4-98"><a href="#cb4-98" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(GEOID <span class="sc">%in%</span> (d<span class="sc">$</span>GEOID)) <span class="sc">%>%</span></span> 3530<span id="cb4-99"><a href="#cb4-99" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="fu">across</span>(GEOID, as.numeric)) <span class="sc">%>%</span></span> 3531<span id="cb4-100"><a href="#cb4-100" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(GEOID, geometry) <span class="sc">%>%</span></span> 3532<span id="cb4-101"><a href="#cb4-101" aria-hidden="true" tabindex="-1"></a> <span class="fu">st_transform</span>(<span class="at">crs =</span> <span class="fu">st_crs</span>(study_bgs))</span> 3533<span id="cb4-102"><a href="#cb4-102" aria-hidden="true" tabindex="-1"></a> } <span class="cf">else</span> <span class="cf">if</span> (yr <span class="sc">>=</span> <span class="dv">2021</span> <span class="sc">&</span> yr <span class="sc"><=</span> <span class="dv">2022</span>) { <span class="co"># 2021 to 2022</span></span> 3534<span id="cb4-103"><a href="#cb4-103" aria-hidden="true" tabindex="-1"></a> d <span class="ot"><-</span></span> 3535<span id="cb4-104"><a href="#cb4-104" aria-hidden="true" tabindex="-1"></a> d <span class="sc">%>%</span></span> 3536<span id="cb4-105"><a href="#cb4-105" aria-hidden="true" tabindex="-1"></a>
3536 <span class="fu">mutate</span>(<span class="at">GEO_ID =</span> <span class="fu">str_split</span>(GEO_ID, <span class="st">'US'</span>, <span class="at">simplify =</span> <span class="cn">TRUE</span>)[,<span class="dv">2</span>]) <span class="sc">%>%</span></span> 3537<span id="cb4-106"><a href="#cb4-106" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate_at</span>(<span class="fu">vars</span>(GEO_ID), as.numeric) <span class="sc">%>%</span></span> 3538<span id="cb4-107"><a href="#cb4-107" aria-hidden="true" tabindex="-1"></a> <span class="fu">rename</span>(</span> 3539<span id="cb4-108"><a href="#cb4-108" aria-hidden="true" tabindex="-1"></a> <span class="at">year =</span> YEAR, <span class="at">county =</span> COUNTY, <span class="at">name =</span> NAME_E,</span> 3540<span id="cb4-109"><a href="#cb4-109" aria-hidden="true" tabindex="-1"></a> <span class="at">tot_pop =</span> <span class="dv">43</span>, <span class="at">tot_hhs =</span> <span class="dv">44</span>, <span class="at">tot_families =</span> <span class="dv">45</span>,</span> 3541<span id="cb4-110"><a href="#cb4-110" aria-hidden="true" tabindex="-1"></a> <span class="at">tot_pop_over_15 =</span> <span class="dv">
354153</span>, <span class="at">males_divorced =</span> <span class="dv">62</span>, <span class="at">females_divorced =</span> <span class="dv">71</span>,</span> 3542<span id="cb4-111"><a href="#cb4-111" aria-hidden="true" tabindex="-1"></a> <span class="at">tot_pop_pov_count =</span> <span class="dv">72</span>, <span class="at">pop_in_poverty =</span> <span class="dv">73</span>, <span class="at">agg_fam_income =</span> <span class="dv">124</span>,</span> 3543<span id="cb4-112"><a href="#cb4-112" aria-hidden="true" tabindex="-1"></a> <span class="at">per_capita_income =</span> <span class="dv">125</span>, <span class="at">pop_over_16 =</span> <span class="dv">126</span>, <span class="at">males_didnt_work =</span> <span class="dv">150</span>,</span> 3544<span id="cb4-113"><a href="#cb4-113" aria-hidden="true" tabindex="-1"></a> <span class="at">females_didnt_work =</span> <span class="dv">174</span>, <span class="at">tot_housing_units =</span> <span class="dv">175</span>, <span class="at">vacant_housing_units =</span> <span class="dv">176</span>,</span> 3545<span id="cb4-114"><a href="#cb4-114" aria-hidden="true" tabindex="-1"></a> <span class="at">renter_occ_housing_units =</span> <span class="dv">186</span>, <span class="at">agg_gross_rent =</span> <span class="dv">190</span></span> 3546<span id="cb4-115"><a href="#cb4-115" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">%>%</span></span> 3547<span id="cb4-116"><a href="#cb4-116" aria-hidden="true" tabindex="-1"></a> .[,<span class="fu">c</span>(<span class="dv">2</span>, <span class="dv">8</span>, <span class="dv">38</span>, <span class="dv">42</span><span class="sc">:</span><span class="dv">45</span>, <span class="dv">
354753</span>, <span class="dv">62</span>, <span class="dv">71</span><span class="sc">:</span><span class="dv">73</span>, <span class="dv">124</span><span class="sc">:</span><span class="dv">126</span>, <span class="dv">150</span>, <span class="dv">174</span><span class="sc">:</span><span class="dv">176</span>, <span class="dv">186</span>, <span class="dv">190</span>)] <span class="sc">%>%</span></span> 3548<span id="cb4-117"><a href="#cb4-117" aria-hidden="true" tabindex="-1"></a> <span class="fu">rename</span>(<span class="at">GEOID =</span> GEO_ID) <span class="sc">%>%</span></span> 3549<span id="cb4-118"><a href="#cb4-118" aria-hidden="true" tabindex="-1"></a> <span class="fu">relocate</span>(GEOID, <span class="at">.before =</span> year) <span class="sc">%>%</span></span> 3550<span id="cb4-119"><a href="#cb4-119" aria-hidden="true" tabindex="-1"></a> <span class="fu">relocate</span>(<span class="fu">c</span>(county, year), <span class="at">.after =</span> name)</span> 3551<span id="cb4-120"><a href="#cb4-120" aria-hidden="true" tabindex="-1"></a> </span> 3552<span id="cb4-121"><a href="#cb4-121" aria-hidden="true" tabindex="-1"></a> s <span class="ot"><-</span> s <span class="sc">%>%</span></span> 3553<span id="cb4-122"><a href="#cb4-122" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(GEOID <span class="sc">%in%</span> (d<span class="sc">$</span>GEOID)) <span class="sc">%>%</span></span> 3554<span id="cb4-123"><a href="#cb4-123" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="fu">across</span>(GEOID, as.numeric)) <span class="sc">%>%</span></span> 3555<span id="cb4-124"><a href="#cb4-124" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(GEOID, geometry) <span class="sc">%>%</span></span> 3556<span id="cb4-125"><a href="#cb4-125" aria-hidden="true" tabindex="-1"></a> <span class="fu">st_transform</span>(<span class="at">crs =</span> <span class="fu">st_crs</span>(study_bgs))</span> 3557<span id="cb4-126"><a href="#cb4-126" aria-hidden="true" tabindex="-1"></a> } <span class="cf">else</span> { <span class="co"># 2023</span></span> 3558<span id="cb4-127"><a href="#cb4-127" aria-hidden="true" tabindex="-1"></a> d <span class="ot"><-</span></span> 3559<span id="cb4-128"><a href="#cb4-128" aria-hidden="true" tabindex="-1"></a> d <span class="sc">%>%</span></span> 3560<span id="cb4-129"><a href="#cb4-129" aria-hidden="true" tabindex="-1"></a>
3560 <span class="fu">mutate</span>(<span class="at">GEO_ID =</span> <span class="fu">str_split</span>(GEO_ID, <span class="st">'US'</span>, <span class="at">simplify =</span> <span class="cn">TRUE</span>)[,<span class="dv">2</span>]) <span class="sc">%>%</span></span> 3561<span id="cb4-130"><a href="#cb4-130" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate_at</span>(<span class="fu">vars</span>(GEO_ID), as.numeric) <span class="sc">%>%</span></span> 3562<span id="cb4-131"><a href="#cb4-131" aria-hidden="true" tabindex="-1"></a> <span class="fu">rename</span>(</span> 3563<span id="cb4-132"><a href="#cb4-132" aria-hidden="true" tabindex="-1"></a> <span class="at">year =</span> YEAR, <span class="at">county =</span> COUNTY, <span class="at">name =</span> NAME_E,</span> 3564<span id="cb4-133"><a href="#cb4-133" aria-hidden="true" tabindex="-1"></a> <span class="at">tot_pop =</span> <span class="dv">42</span>, <span class="at">tot_hhs =</span> <span class="dv">43</span>, <span class="at">tot_families =</span> <span class="dv">44</span>,</span> 3565<span id="cb4-134"><a href="#cb4-134" aria-hidden="true" tabindex="-1"></a> <span class="at">tot_pop_over_15 =</span> <span class="dv">52</span>, <span class="at">males_divorced =</span> <span class="dv">61</span>, <span class="at">females_divorced =</span> <span class="dv">70</span>,</span> 3566<span id="cb4-135"><a href="#cb4-135" aria-hidden="true" tabindex="-1"></a> <span class="at">tot_pop_pov_count =</span> <span class="dv">71</span>, <span class="at">pop_in_poverty =</span> <span class="dv">72</span>, <span class="at">agg_fam_income =</span> <span class="dv">123</span>,</span> 3567<span id="cb4-136"><a href="#cb4-136" aria-hidden="true" tabindex="-1"></a> <span class="at">per_capita_income =</span> <span class="dv">124</span>, <span class="at">pop_over_16 =</span> <span class="dv">125</span>, <span class="at">males_didnt_work =</span> <span class="dv">149</span>,</span> 3568<span id="cb4-137"><a href="#cb4-137" aria-hidden="true" tabindex="-1"></a> <span class="at">females_didnt_work =</span> <span class="dv">173</span>, <span class="at">tot_housing_units =</span> <span class="dv">174</span>, <span class="at">vacant_housing_units =</span> <span class="dv">175</span>,</span> 3569<span id="cb4-138"><a href="#cb4-138" aria-hidden="true" tabindex="-1"></a> <span class="at">renter_occ_housing_units =</span> <span class="dv">185</span>, <span class="at">agg_gross_rent =</span> <span class="dv">189</span></span> 3570<span id="cb4-139"><a href="#cb4-139" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">%>%</span></span> 3571<span id="cb4-140"><a href="#cb4-140" aria-hidden="true" tabindex="-1"></a> .[,<span class="fu">c</span>(<span class="dv">2</span>, <span class="dv">8</span>, <span class="dv">37</span>, <span class="dv">41</span><span class="sc">:</span><span class="dv">44</span>, <span class="dv">52</span>, <span class="dv">61</span>, <span class="dv">70</span><span class="sc">:</span><span class="dv">72</span>, <span class="dv">123</span><span class="sc">:</span><span class="dv">125</span>, <span class="dv">149</span>, <span class="dv">173</span><span class="sc">:</span><span class="dv">175</span>, <span class="dv">185</span>, <span class="dv">189</span>)] <span class="sc">%>%</span></span> 3572<span id="cb4-141"><a href="#cb4-141" aria-hidden="true" tabindex="-1"></a>
3572 <span class="fu">rename</span>(<span class="at">GEOID =</span> GEO_ID) <span class="sc">%>%</span></span> 3573<span id="cb4-142"><a href="#cb4-142" aria-hidden="true" tabindex="-1"></a> <span class="fu">relocate</span>(GEOID, <span class="at">.before =</span> year) <span class="sc">%>%</span></span> 3574<span id="cb4-143"><a href="#cb4-143" aria-hidden="true" tabindex="-1"></a> <span class="fu">relocate</span>(<span class="fu">c</span>(county, year), <span class="at">.after =</span> name)</span> 3575<span id="cb4-144"><a href="#cb4-144" aria-hidden="true" tabindex="-1"></a> </span> 3576<span id="cb4-145"><a href="#cb4-145" aria-hidden="true" tabindex="-1"></a> s <span class="ot"><-</span> </span> 3577<span id="cb4-146"><a href="#cb4-146" aria-hidden="true" tabindex="-1"></a> s <span class="sc">%>%</span></span> 3578<span id="cb4-147"><a href="#cb4-147" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(GEOID <span class="sc">%in%</span> (d<span class="sc">$</span>GEOID)) <span class="sc">%>%</span></span> 3579<span id="cb4-148"><a href="#cb4-148" aria-hidden="true" tabindex="-1"></a>
3579 <span class="fu">mutate</span>(<span class="fu">across</span>(GEOID, as.numeric)) <span class="sc">%>%</span></span> 3580<span id="cb4-149"><a href="#cb4-149" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(GEOID, geometry) <span class="sc">%>%</span></span> 3581<span id="cb4-150"><a href="#cb4-150" aria-hidden="true" tabindex="-1"></a> <span class="fu">st_transform</span>(<span class="at">crs =</span> <span class="fu">st_crs</span>(study_bgs))</span> 3582<span id="cb4-151"><a href="#cb4-151" aria-hidden="true" tabindex="-1"></a> }</span> 3583<span id="cb4-152"><a href="#cb4-152" aria-hidden="true" tabindex="-1"></a> </span> 3584<span id="cb4-153"><a href="#cb4-153" aria-hidden="true" tabindex="-1"></a> <span class="co"># convert year and data columns to numeric, then change negative and NA values to 0</span></span> 3585<span id="cb4-154"><a href="#cb4-154" aria-hidden="true" tabindex="-1"></a> data <span class="ot"><-</span> </span> 3586<span id="cb4-155"><a href="#cb4-155" aria-hidden="true" tabindex="-1"></a> d <span class="sc">%>%</span></span> 3587<span id="cb4-156"><a href="#cb4-156" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="fu">across</span>(<span class="fu">c</span>(tot_pop<span class="sc">:</span>agg_gross_rent), as.numeric),</span> 3588<span id="cb4-157"><a href="#cb4-157" aria-hidden="true" tabindex="-1"></a> <span class="fu">across</span>(<span class="fu">c</span>(tot_pop<span class="sc">:</span>agg_gross_rent), <span class="sc">~</span> <span class="fu">ifelse</span>(<span class="fu">is.na</span>(.) <span class="sc">|</span> . <span class="sc"><</span> <span class="dv">0</span> <span class="sc">|</span> . <span class="sc">==</span> <span class="st">'.'</span>, <span class="st">'0'</span>, .)))</span> 3589<span id="cb4-158"><a href="#cb4-158" aria-hidden="true" tabindex="-1"></a> </span> 3590<span id="cb4-159"><a href="#cb4-159" aria-hidden="true" tabindex="-1"></a> data[<span class="fu">is.na</span>(data)] <span class="ot"><-</span> <span class="dv">0</span></span> 3591<span id="cb4-160"><a href="#cb4-160" aria-hidden="true" tabindex="-1"></a> </span> 3592<span id="cb4-161"><a href="#cb4-161" aria-hidden="true" tabindex="-1"></a> <span class="co"># join spatial features with data then transform to NAD83</span></span> 3593<span id="cb4-162"><a href="#cb4-162" aria-hidden="true" tabindex="-1"></a> full_data <span class="ot"><-</span> </span> 3594<span id="cb4-163"><a href="#cb4-163" aria-hidden="true" tabindex="-1"></a> <span class="fu">inner_join</span>(s, data, <span class="at">by =</span> <span class="st">'GEOID'</span>) <span class="sc">%>%</span> </span> 3595<span id="cb4-164"><a href="#cb4-164" aria-hidden="true" tabindex="-1"></a> <span class="fu">relocate</span>(geometry, <span class="at">.after =</span> <span class="fu">last_col</span>()) <span class="sc">%>%</span></span> 3596<span id="cb4-165"><a href="#cb4-165" aria-hidden="true" tabindex="-1"></a> <span class="fu">st_transform</span>(<span class="at">crs =</span> <span class="fu">st_crs</span>(study_bgs))</span> 3597<span id="cb4-166"><a href="#cb4-166" aria-hidden="true" tabindex="-1"></a> </span> 3598<span id="cb4-167"><a href="#cb4-167" aria-hidden="true" tabindex="-1"></a> <span class="co"># add to bg_data_yrs and move onto the next year after doing this</span></span> 3599<span id="cb4-168"><a href="#cb4-168" aria-hidden="true" tabindex="-1"></a> bg_data_yrs[[<span class="fu">as.character</span>(yr)]] <span class="ot"><-</span> full_data</span> 3600<span id="cb4-169"><a href="#cb4-169" aria-hidden="true" tabindex="-1"></a>}</span> 3601<span id="cb4-170"><a href="#cb4-170" aria-hidden="true" tabindex="-1"></a></span> 3602<span id="cb4-171"><a href="#cb4-171" aria-hidden="true" tabindex="-1"></a><span class="co"># combine block group data into single dataframe</span></span> 3603<span id="cb4-172"><a href="#cb4-172" aria-hidden="true" tabindex="-1"></a>st_bgs <span class="ot"><-</span> </span> 3604<span id="cb4-173"><a href="#cb4-173" aria-hidden="true" tabindex="-1"></a> <span class="fu">bind_rows</span>(bg_data_yrs) <span class="sc">%>%</span></span> 3605<span id="cb4-174"><a href="#cb4-174" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(</span> 3606<span id="cb4-175"><a href="#cb4-175" aria-hidden="true" tabindex="-1"></a> <span class="fu">across</span>(<span class="fu">c</span>(GEOID, year, agg_fam_income, per_capita_income, agg_gross_rent), as.numeric),</span> 3607<span id="cb4-176"><a href="#cb4-176" aria-hidden="true" tabindex="-1"></a> <span class="at">pop_over_16_didnt_work =</span> males_didnt_work <span class="sc">+</span> females_didnt_work</span> 3608<span id="cb4-177"><a href="#cb4-177" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">%>%</span></span> 3609<span id="cb4-178"><a href="#cb4-178" aria-hidden="true" tabindex="-1"></a> <span class="fu">relocate</span>(geometry, <span class="at">.after =</span> <span class="fu">
3609last_col</span>())</span> 3610<span id="cb4-179"><a href="#cb4-179" aria-hidden="true" tabindex="-1"></a>st_bgs[<span class="fu">is.na</span>(st_bgs)] <span class="ot"><-</span> <span class="dv">0</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div> 3611</details> 3612</div> 3613<div style="page-break-after: always;"></div> 3614</section> 3615</section> 3616</section> 3617<section id="population-weighted-areal-interpolation-and-distressed-status-determination" class="level3"> 3618<h3 class="anchored" data-anchor-id="population-weighted-areal-interpolation-and-distressed-status-determination">Population-Weighted Areal Interpolation and Distressed Status Determination</h3> 3619<p>Apply population-weighted areal interpolation using centroid assignment to the 2009 - 2019 block group boundaries and ensure that they follow the 2020 boundaries. Estimate all data by applying a scaling factor to each variable after interpolation. Merge all the data together, recombine with 2020-2023 data, then calculate remaining variables.</p> 3620<div class="cell"> 3621<details class="code-fold"> 3622<summary>Code</summary> 3623<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="co"># isolate post-2020 block group data</span></span> 3624<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>st_bgs.post_2020 <span class="ot"><-</span> </span> 3625<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a> st_bgs <span class="sc">%>%</span></span> 3626<span id="cb5-4"><a href="#cb5-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(year <span class="sc">>=</span> <span class="dv">2020</span>) <span class="sc">%>%</span></span> 3627<span id="cb5-5"><a href="#cb5-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(<span class="sc">-</span>name) <span class="sc">%>%</span></span> 3628<span id="cb5-6"><a href="#cb5-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">st_make_valid</span>()</span> 3629<span id="cb5-7"><a href="#cb5-7" aria-hidden="true" tabindex="-1"></a></span> 3630<span id="cb5-8"><a href="#cb5-8" aria-hidden="true" tabindex="-1"></a><span class="co"># get only 2020 block groups</span></span> 3631<span id="cb5-9"><a href="#cb5-9" aria-hidden="true" tabindex="-1"></a>st_bgs<span class="fl">.2020</span> <span class="ot"><-</span></span> 3632<span id="cb5-10"><a href="#cb5-10" aria-hidden="true" tabindex="-1"></a> st_bgs <span class="sc">%>%</span></span> 3633<span id="cb5-11"><a href="#cb5-11" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(year <span class="sc">==</span> <span class="dv">2020</span>) <span class="sc">%>%</span></span> 3634<span id="cb5-12"><a href="#cb5-12" aria-hidden="true" tabindex="-1"></a> <span class="fu">st_make_valid</span>()</span> 3635<span id="cb5-13"><a href="#cb5-13" aria-hidden="true" tabindex="-1"></a></span> 3636<span id="cb5-14"><a href="#cb5-14" aria-hidden="true" tabindex="-1"></a><span class="co"># get 2020 blocks to use as weights in interpolation</span></span> 3637<span id="cb5-15"><a href="#cb5-15" aria-hidden="true" tabindex="-1"></a>st_blocks<span class="fl">.2020</span> <span class="ot"><-</span> </span> 3638<span id="cb5-16"><a href="#cb5-16" aria-hidden="true" tabindex="-1"></a> tigris<span class="sc">::</span><span class="fu">blocks</span>(<span class="at">state =</span> <span class="st">'NY'</span>, <span class="at">year =</span> <span class="dv">2020</span>) <span class="sc">%>%</span></span> 3639<span id="cb5-17"><a href="#cb5-17" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(COUNTYFP20 <span class="sc">%in%</span> county_fips) <span class="sc">%>%</span></span> 3640<span id="cb5-18"><a href="#cb5-18" aria-hidden="true" tabindex="-1"></a> <span class="fu">st_make_valid</span>()</span> 3641<span id="cb5-19"><a href="#cb5-19" aria-hidden="true" tabindex="-1"></a></span> 3642<span id="cb5-20"><a href="#cb5-20" aria-hidden="true" tabindex="-1"></a><span class="co"># get list of variables to scale</span></span> 3643<span id="cb5-21"><a href="#cb5-21" aria-hidden="true" tabindex="-1"></a>vars_to_scale <span class="ot"><-</span> <span class="fu">c</span>(</span> 3644<span id="cb5-22"><a href="#cb5-22" aria-hidden="true" tabindex="-1"></a> <span class="st">"tot_pop"</span>, <span class="st">"tot_hhs"</span>, <span class="st">"tot_families"</span>, <span class="st">"tot_pop_over_15"</span>, </span> 3645<span id="cb5-23"><a href="#cb5-23" aria-hidden="true" tabindex="-1"></a> <span class="st">"males_divorced"</span>, <span class="st">"females_divorced"</span>, <span class="st">"tot_pop_pov_count"</span>, </span> 3646<span id="cb5-24"><a href="#cb5-24" aria-hidden="true" tabindex="-1"></a> <span class="st">"pop_in_poverty"</span>, <span class="st">"agg_fam_income"</span>, <span class="st">"agg_income"</span>, </span> 3647<span id="cb5-25"><a href="#cb5-25" aria-hidden="true" tabindex="-1"></a> <span class="st">"pop_over_16"</span>, <span class="st">"males_didnt_work"</span>, <span class="st">"females_didnt_work"</span>, </span> 3648<span id="cb5-26"><a href="#cb5-26" aria-hidden="true" tabindex="-1"></a> <span class="st">"tot_housing_units"</span>, <span class="st">"vacant_housing_units"</span>, </span> 3649<span id="cb5-27"><a href="#cb5-27" aria-hidden="true" tabindex="-1"></a> <span class="st">"renter_occ_housing_units"</span>, <span class="st">"agg_gross_rent"</span>, <span class="st">"pop_over_16_didnt_work"</span></span> 3650<span id="cb5-28"><a href="#cb5-28" aria-hidden="true" tabindex="-1"></a>)</span> 3651<span id="cb5-29"><a href="#cb5-29" aria-hidden="true" tabindex="-1"></a></span> 3652<span id="cb5-30"><a href="#cb5-30" aria-hidden="true" tabindex="-1"></a><span class="co">
3652# interpolate block groups boundaries and new boundary data for each year,</span></span> 3653<span id="cb5-31"><a href="#cb5-31" aria-hidden="true" tabindex="-1"></a><span class="co"># then store in interpolated data list</span></span> 3654<span id="cb5-32"><a href="#cb5-32" aria-hidden="true" tabindex="-1"></a>interpolated_data <span class="ot"><-</span> <span class="fu">lapply</span>(<span class="dv">2009</span><span class="sc">:</span><span class="dv">2019</span>, interpolate_data)</span> 3655<span id="cb5-33"><a href="#cb5-33" aria-hidden="true" tabindex="-1"></a></span> 3656<span id="cb5-34"><a href="#cb5-34" aria-hidden="true" tabindex="-1"></a><span class="co"># bind interpolated data together</span></span> 3657<span id="cb5-35"><a href="#cb5-35" aria-hidden="true" tabindex="-1"></a>st_interpolated.pre_2020 <span class="ot"><-</span> </span> 3658<span id="cb5-36"><a href="#cb5-36" aria-hidden="true" tabindex="-1"></a> <span class="fu">bind_rows</span>(interpolated_data) <span class="sc">%>%</span></span> 3659<span id="cb5-37"><a href="#cb5-37" aria-hidden="true" tabindex="-1"></a> <span class="fu">relocate</span>(geometry, <span class="at">.after =</span> <span class="fu">last_col</span>())</span> 3660<span id="cb5-38"><a href="#cb5-38" aria-hidden="true" tabindex="-1"></a></span> 3661<span id="cb5-39"><a href="#cb5-39" aria-hidden="true" tabindex="-1"></a><span class="co"># bind yearly interpolated data into single dataframe</span></span> 3662<span id="cb5-40"><a href="#cb5-40" aria-hidden="true" tabindex="-1"></a><span class="co"># calculate total divorced population and determine distressed status</span></span> 3663<span id="cb5-41"><a href="#cb5-41" aria-hidden="true" tabindex="-1"></a>st_bgs_final <span class="ot"><-</span> </span> 3664<span id="cb5-42"><a href="#cb5-42" aria-hidden="true" tabindex="-1"></a> <span class="fu">bind_rows</span>(st_interpolated.pre_2020,</span> 3665<span id="cb5-43"><a href="#cb5-43" aria-hidden="true" tabindex="-1"></a> st_bgs.post_2020 <span class="sc">%>%</span> <span class="fu">mutate</span>(<span class="at">year =</span> <span class="fu">as.numeric</span>(year))) <span class="sc">%>%</span></span> 3666<span id="cb5-44"><a href="#cb5-44" aria-hidden="true" tabindex="-1"></a> <span class="fu">left_join</span>(us_data <span class="sc">%>%</span> <span class="fu">select</span>(year, median_income, pct_below_poverty),</span> 3667<span id="cb5-45"><a href="#cb5-45" aria-hidden="true" tabindex="-1"></a> <span class="at">by =</span> <span class="st">'year'</span>,</span> 3668<span id="cb5-46"><a href="#cb5-46" aria-hidden="true" tabindex="-1"></a> <span class="at">suffix =</span> <span class="fu">c</span>(<span class="st">''</span>, <span class="st">''</span>)) <span class="sc">%>%</span></span> 3669<span id="cb5-47"><a href="#cb5-47" aria-hidden="true" tabindex="-1"></a> <span class="fu">rename</span>(<span class="at">median_income_us =</span> median_income,</span> 3670<span id="cb5-48"><a href="#cb5-48" aria-hidden="true" tabindex="-1"></a> <span class="at">pct_below_poverty_us =</span> pct_below_poverty) <span class="sc">%>%</span></span> 3671<span id="cb5-49"><a href="#cb5-49" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(</span> 3672<span id="cb5-50"><a href="#cb5-50" aria-hidden="true" tabindex="-1"></a> <span class="at">county =</span> study_counties[<span class="fu">data.frame</span>(</span> 3673<span id="cb5-51"><a href="#cb5-51" aria-hidden="true" tabindex="-1"></a>
3673 <span class="fu">st_intersects</span>(<span class="fu">st_centroid</span>(.), study_counties <span class="sc">%>%</span> <span class="fu">select</span>(NAME))</span> 3674<span id="cb5-52"><a href="#cb5-52" aria-hidden="true" tabindex="-1"></a> )<span class="sc">$</span>col.id,]<span class="sc">$</span>NAME,</span> 3675<span id="cb5-53"><a href="#cb5-53" aria-hidden="true" tabindex="-1"></a> <span class="at">area_sqmi =</span> <span class="fu">as.numeric</span>(<span class="fu">st_area</span>(.) <span class="sc">*</span> <span class="fl">3.861E-7</span>),</span> 3676<span id="cb5-54"><a href="#cb5-54" aria-hidden="true" tabindex="-1"></a> <span class="at">per_capita_income =</span> agg_income <span class="sc">/</span> tot_pop,</span> 3677<span id="cb5-55"><a href="#cb5-55" aria-hidden="true" tabindex="-1"></a> <span class="at">tot_pop_density =</span> tot_pop <span class="sc">/</span> area_sqmi,</span> 3678<span id="cb5-56"><a href="#cb5-56" aria-hidden="true" tabindex="-1"></a> <span class="at">pct_below_poverty =</span> pop_in_poverty <span class="sc">/</span> tot_pop_pov_count,</span> 3679<span id="cb5-57"><a href="#cb5-57" aria-hidden="true" tabindex="-1"></a> <span class="at">pop_over_15_divorced =</span> males_divorced <span class="sc">+</span> females_divorced,</span> 3680<span id="cb5-58"><a href="#cb5-58" aria-hidden="true" tabindex="-1"></a> <span class="at">pct_divorced =</span> pop_over_15_divorced <span class="sc">/</span> tot_pop_over_15,</span> 3681<span id="cb5-59"><a href="#cb5-59" aria-hidden="true" tabindex="-1"></a> <span class="at">pct_didnt_work_past_yr =</span> pop_over_16_didnt_work <span class="sc">/</span> pop_over_16,</span> 3682<span id="cb5-60"><a href="#cb5-60" aria-hidden="true" tabindex="-1"></a> <span class="at">pct_family_hhs =</span> tot_families <span class="sc">/</span> tot_hhs,</span> 3683<span id="cb5-61"><a href="#cb5-61" aria-hidden="true" tabindex="-1"></a> <span class="at">hu_vacancy_rate =</span> vacant_housing_units <span class="sc">/</span> tot_housing_units,</span> 3684<span id="cb5-62"><a href="#cb5-62" aria-hidden="true" tabindex="-1"></a> <span class="at">avg_fam_income =</span> agg_fam_income <span class="sc">/</span> tot_families,</span> 3685<span id="cb5-63"><a href="#cb5-63" aria-hidden="true" tabindex="-1"></a> <span class="at">avg_rent =</span> agg_gross_rent <span class="sc">/</span> renter_occ_housing_units,</span> 3686<span id="cb5-64"><a href="#cb5-64" aria-hidden="true" tabindex="-1"></a> <span class="fu">across</span>(<span class="sc">-</span>geometry, <span class="sc">~</span> <span class="fu">ifelse</span>((<span class="fu">is.na</span>(.)), <span class="dv">0</span>, .)),</span> 3687<span id="cb5-65"><a href="#cb5-65" aria-hidden="true" tabindex="-1"></a> <span class="at">is_distressed =</span> <span class="fu">ifelse</span>(</span> 3688<span id="cb5-66"><a href="#cb5-66" aria-hidden="true" tabindex="-1"></a> (((avg_fam_income <span class="sc">/</span> median_income_us) <span class="sc"><=</span> <span class="fl">0.67</span>) <span class="sc">&</span> ((pct_below_poverty <span class="sc">/</span> pct_below_poverty_us) <span class="sc">>=</span> <span class="fl">1.50</span>)),</span> 3689<span id="cb5-67"><a href="#cb5-67" aria-hidden="true" tabindex="-1"></a>
3689 <span class="dv">1</span>, <span class="dv">0</span></span> 3690<span id="cb5-68"><a href="#cb5-68" aria-hidden="true" tabindex="-1"></a> )</span> 3691<span id="cb5-69"><a href="#cb5-69" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">%>%</span></span> 3692<span id="cb5-70"><a href="#cb5-70" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(<span class="sc">-</span><span class="fu">ends_with</span>(<span class="st">'_us'</span>)) <span class="sc">%>%</span></span> 3693<span id="cb5-71"><a href="#cb5-71" aria-hidden="true" tabindex="-1"></a> <span class="fu">relocate</span>(pop_over_15_divorced, <span class="at">.after =</span> females_divorced) <span class="sc">%>%</span></span> 3694<span id="cb5-72"><a href="#cb5-72" aria-hidden="true" tabindex="-1"></a> <span class="fu">relocate</span>(pop_over_16_didnt_work, <span class="at">.after =</span> females_didnt_work) <span class="sc">%>%</span></span> 3695<span id="cb5-73"><a href="#cb5-73" aria-hidden="true" tabindex="-1"></a> <span class="fu">relocate</span>(<span class="fu">c</span>(county, year), <span class="at">.after =</span> GEOID) <span class="sc">%>%</span></span> 3696<span id="cb5-74"><a href="#cb5-74" aria-hidden="true" tabindex="-1"></a> <span class="fu">relocate</span>(geometry, <span class="at">.after =</span> <span class="fu">last_col</span>())</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div> 3697</details> 3698</div> 3699<div style="page-break-after: always;"></div> 3700</section> 3701<section id="land-cover-data" class="level3"> 3702<h3 class="anchored" data-anchor-id="land-cover-data">Land Cover Data</h3> 3703<p>This section pertains to downloading and processing national land cover rasters over the entire 14-county study area. The interpretation of the land cover imagesâ pixel values and corresponding land cover classes is from the <a href="https://www.mrlc.gov/data/type/land-cover">Multi-Resolution Land Characteristics Consortium</a>.</p> 3704<section id="land-cover-processing" class="level4"> 3705<h4 class="anchored" data-anchor-id="land-cover-processing">Land Cover Processing</h4> 3706<div class="cell"> 3707<details class="code-fold"> 3708<summary>Code</summary> 3709<div class="sourceCode cell-code" id="cb6"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="co"># dissolve counties into single study area polygon</span></span> 3710<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a>st_full_study_area <span class="ot"><-</span> st_bgs_final <span class="sc">%>%</span> <span class="fu">filter</span>(year <span class="sc">==</span> <span class="dv">2023</span>) <span class="sc">%>%</span> <span class="fu">st_union</span>()</span> 3711<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a></span> 3712<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a><span class="co"># set years of land cover data</span></span> 3713<span id="cb6-5"><a href="#cb6-5" aria-hidden="true" tabindex="-1"></a>lc_yrs <span class="ot"><-</span> <span class="dv">2008</span><span class="sc">:</span><span class="dv">2023</span></span> 3714<span id="cb6-6"><a href="#cb6-6" aria-hidden="true" tabindex="-1"></a></span> 3715<span id="cb6-7"><a href="#cb6-7" aria-hidden="true" tabindex="-1"></a><span class="co"># create list of tiff urls for each year</span></span> 3716<span id="cb6-8"><a href="#cb6-8" aria-hidden="true" tabindex="-1"></a>nlcd_urls <span class="ot"><-</span> <span class="fu">paste0</span>(<span class="st">'https://www.mrlc.gov/downloads/sciweb1/shared/mrlc/data-bundles/Annual_NLCD_LndCov_'</span>,</span> 3717<span id="cb6-9"><a href="#cb6-9" aria-hidden="true" tabindex="-1"></a> lc_yrs,</span> 3718<span id="cb6-10"><a href="#cb6-10" aria-hidden="true" tabindex="-1"></a> <span class="st">'_CU_C1V0.tif'</span>)</span> 3719<span id="cb6-11"><a href="#cb6-11" aria-hidden="true" tabindex="-1"></a></span> 3720<span id="cb6-12"><a href="#cb6-12" aria-hidden="true" tabindex="-1"></a><span class="fu">names</span>(nlcd_urls) <span class="ot"><-</span> lc_yrs</span> 3721<span id="cb6-13"><a href="#cb6-13" aria-hidden="true" tabindex="-1"></a></span> 3722<span id="cb6-14"><a href="#cb6-14" aria-hidden="true" tabindex="-1"></a><span class="co"># # download all land cover rasters</span></span> 3723<span id="cb6-15"><a href="#cb6-15" aria-hidden="true" tabindex="-1"></a><span class="co"># # RUN THE FIRST TIME, THEN RE-COMMENT OUT AFTER</span></span> 3724<span id="cb6-16"><a href="#cb6-16" aria-hidden="true" tabindex="-1"></a><span class="co"># # THIS STEP CAN TAKE AROUND AN HOUR</span></span> 3725<span id="cb6-17"><a href="#cb6-17" aria-hidden="true" tabindex="-1"></a><span class="co"># for (year in names(nlcd_urls)) {</span></span> 3726<span id="cb6-18"><a href="#cb6-18" aria-hidden="true" tabindex="-1"></a><span class="co"># cat('Downloading NLCD raster for year', paste(year, '...', sep = ''))</span></span> 3727<span id="cb6-19"><a href="#cb6-19" aria-hidden="true" tabindex="-1"></a><span class="co"># </span></span> 3728<span id="cb6-20"><a href="#cb6-20" aria-hidden="true" tabindex="-1"></a><span class="co"># # set path of downloaded raster</span></span> 3729<span id="cb6-21"><a href="#cb6-21" aria-hidden="true" tabindex="-1"></a><span class="co"># download_path = paste(mrlc_data_dir, paste('nlcd_', year, '.tif', sep=''), sep = '')</span></span> 3730<span id="cb6-22"><a href="#cb6-22" aria-hidden="true" tabindex="-1"></a><span class="co"># </span></span> 3731<span id="cb6-23"><a href="#cb6-23" aria-hidden="true" tabindex="-1"></a><span class="co"># # download raster</span></span> 3732<span id="cb6-24"><a href="#cb6-24" aria-hidden="true" tabindex="-1"></a><span class="co"># download(nlcd_urls[[year]], download_path, mode = 'wb')</span></span> 3733<span id="cb6-25"><a href="#cb6-25" aria-hidden="true" tabindex="-1"></a><span class="co"># }</span></span> 3734<span id="cb6-26"><a href="#cb6-26" aria-hidden="true" tabindex="-1"></a></span> 3735<span id="cb6-27"><a href="#cb6-27" aria-hidden="true" tabindex="-1"></a><span class="co"># process rasters and wrap them so they can be used in main session</span></span> 3736<span id="cb6-28"><a href="#cb6-28" aria-hidden="true" tabindex="-1"></a>nlcd_wrapped <span class="ot"><-</span> <span class="fu">lapply</span>(lc_yrs, <span class="cf">function</span>(yr) {</span> 3737<span id="cb6-29"><a href="#cb6-29" aria-hidden="true" tabindex="-1"></a> <span class="co"># crop and mask each raster, then project it to NAD83</span></span> 3738<span id="cb6-30"><a href="#cb6-30" aria-hidden="true" tabindex="-1"></a> </span> 3739<span id="cb6-31"><a href="#cb6-31" aria-hidden="true" tabindex="-1"></a> <span class="co">#print(paste('Processing NLCD for', as.character(yr)))</span></span> 3740<span id="cb6-32"><a href="#cb6-32" aria-hidden="true" tabindex="-1"></a> </span> 3741<span id="cb6-33"><a href="#cb6-33" aria-hidden="true" tabindex="-1"></a> <span class="co"># get path to file</span></span> 3742<span id="cb6-34"><a href="#cb6-34" aria-hidden="true" tabindex="-1"></a> <span class="co">
3742# YOU MAY HAVE TO ADD .tif AS THE EXTENSION OF THE RASTER FILE</span></span> 3743<span id="cb6-35"><a href="#cb6-35" aria-hidden="true" tabindex="-1"></a> lc_rast <span class="ot"><-</span> <span class="fu">paste</span>(mrlc_data_dir, <span class="fu">paste</span>(<span class="st">'nlcd_'</span>, <span class="fu">as.character</span>(yr), <span class="at">sep =</span> <span class="st">''</span>), <span class="at">sep =</span> <span class="st">''</span>)</span> 3744<span id="cb6-36"><a href="#cb6-36" aria-hidden="true" tabindex="-1"></a> </span> 3745<span id="cb6-37"><a href="#cb6-37" aria-hidden="true" tabindex="-1"></a> <span class="co"># catch errors in processing the raster</span></span> 3746<span id="cb6-38"><a href="#cb6-38" aria-hidden="true" tabindex="-1"></a> <span class="fu">tryCatch</span>({</span> 3747<span id="cb6-39"><a href="#cb6-39" aria-hidden="true" tabindex="-1"></a> <span class="co"># load raster, then crop and mask to southern tier study area</span></span> 3748<span id="cb6-40"><a href="#cb6-40" aria-hidden="true" tabindex="-1"></a> r <span class="ot"><-</span> <span class="fu">rast</span>(lc_rast)</span> 3749<span id="cb6-41"><a href="#cb6-41" aria-hidden="true" tabindex="-1"></a> r_crop <span class="ot"><-</span> </span> 3750<span id="cb6-42"><a href="#cb6-42" aria-hidden="true" tabindex="-1"></a> terra<span class="sc">::</span><span class="fu">crop</span>(</span> 3751<span id="cb6-43"><a href="#cb6-43" aria-hidden="true" tabindex="-1"></a> r, </span> 3752<span id="cb6-44"><a href="#cb6-44" aria-hidden="true" tabindex="-1"></a> <span class="fu">vect</span>(<span class="fu">st_transform</span>(st_full_study_area, <span class="at">crs =</span> <span class="fu">st_crs</span>(r))),</span> 3753<span id="cb6-45"><a href="#cb6-45" aria-hidden="true" tabindex="-1"></a> <span class="at">progress =</span> <span class="dv">0</span></span> 3754<span id="cb6-46"><a href="#cb6-46" aria-hidden="true" tabindex="-1"></a> )</span> 3755<span id="cb6-47"><a href="#cb6-47" aria-hidden="true" tabindex="-1"></a> r_mask <span class="ot"><-</span> terra<span class="sc">::</span><span class="fu">mask</span>(</span> 3756<span id="cb6-48"><a href="#cb6-48" aria-hidden="true" tabindex="-1"></a> r_crop, </span> 3757<span id="cb6-49"><a href="#cb6-49" aria-hidden="true" tabindex="-1"></a>
3757 <span class="fu">vect</span>(<span class="fu">st_transform</span>(st_full_study_area, <span class="at">crs =</span> <span class="fu">st_crs</span>(r))),</span> 3758<span id="cb6-50"><a href="#cb6-50" aria-hidden="true" tabindex="-1"></a> <span class="at">progress =</span> <span class="dv">0</span></span> 3759<span id="cb6-51"><a href="#cb6-51" aria-hidden="true" tabindex="-1"></a> )</span> 3760<span id="cb6-52"><a href="#cb6-52" aria-hidden="true" tabindex="-1"></a> </span> 3761<span id="cb6-53"><a href="#cb6-53" aria-hidden="true" tabindex="-1"></a> <span class="co"># add year as name of masked raster</span></span> 3762<span id="cb6-54"><a href="#cb6-54" aria-hidden="true" tabindex="-1"></a> <span class="fu">names</span>(r_mask) <span class="ot"><-</span> <span class="fu">paste</span>(<span class="st">'NLCD_'</span>, <span class="fu">as.character</span>(yr), <span class="at">sep =</span> <span class="st">''</span>)</span> 3763<span id="cb6-55"><a href="#cb6-55" aria-hidden="true" tabindex="-1"></a> </span> 3764<span id="cb6-56"><a href="#cb6-56" aria-hidden="true" tabindex="-1"></a> <span class="co"># return wrapped, masked raster</span></span> 3765<span id="cb6-57"><a href="#cb6-57" aria-hidden="true" tabindex="-1"></a> <span class="fu">return</span>(</span> 3766<span id="cb6-58"><a href="#cb6-58" aria-hidden="true" tabindex="-1"></a>
3766 <span class="fu">wrap</span>(</span> 3767<span id="cb6-59"><a href="#cb6-59" aria-hidden="true" tabindex="-1"></a> <span class="fu">project</span>(r_mask, <span class="fu">crs</span>(<span class="fu">as_spatvector</span>(st_bgs_final)), <span class="at">progress =</span> <span class="dv">0</span>)</span> 3768<span id="cb6-60"><a href="#cb6-60" aria-hidden="true" tabindex="-1"></a> )</span> 3769<span id="cb6-61"><a href="#cb6-61" aria-hidden="true" tabindex="-1"></a> )</span> 3770<span id="cb6-62"><a href="#cb6-62" aria-hidden="true" tabindex="-1"></a> }, <span class="at">error =</span> <span class="cf">function</span>(e) {</span> 3771<span id="cb6-63"><a href="#cb6-63" aria-hidden="true" tabindex="-1"></a> <span class="fu">message</span>(<span class="st">'Failed for year '</span>, <span class="fu">as.character</span>(yr), <span class="st">': '</span>, e<span class="sc">$</span>message)</span> 3772<span id="cb6-64"><a href="#cb6-64" aria-hidden="true" tabindex="-1"></a> <span class="fu">return</span>(<span class="cn">NULL</span>)</span> 3773<span id="cb6-65"><a href="#cb6-65" aria-hidden="true" tabindex="-1"></a> })</span> 3774<span id="cb6-66"><a href="#cb6-66" aria-hidden="true" tabindex="-1"></a>})</span> 3775<span id="cb6-67"><a href="#cb6-67" aria-hidden="true" tabindex="-1"></a></span> 3776<span id="cb6-68"><a href="#cb6-68" aria-hidden="true" tabindex="-1"></a><span class="co"># unwrap rasters so they can be used in the main session</span></span> 3777<span id="cb6-69"><a href="#cb6-69" aria-hidden="true" tabindex="-1"></a>nlcd <span class="ot"><-</span> <span class="fu">lapply</span>(nlcd_wrapped, <span class="cf">function</span>(r) {</span> 3778<span id="cb6-70"><a href="#cb6-70" aria-hidden="true" tabindex="-1"></a> <span class="cf">if</span> (<span class="sc">!</span><span class="fu">is.null</span>(r)) <span class="fu">unwrap</span>(r) <span class="cf">else</span> <span class="cn">NULL</span></span> 3779<span id="cb6-71"><a href="#cb6-71" aria-hidden="true" tabindex="-1"></a>})</span> 3780<span id="cb6-72"><a href="#cb6-72" aria-hidden="true" tabindex="-1"></a></span> 3781<span id="cb6-73"><a href="#cb6-73" aria-hidden="true" tabindex="-1"></a><span class="fu">names</span>(nlcd) <span class="ot"><-</span> <span class="fu">lapply</span>(lc_yrs, as.character)</span> 3782<span id="cb6-74"><a href="#cb6-74" aria-hidden="true" tabindex="-1"></a></span> 3783<span id="cb6-75"><a href="#cb6-75" aria-hidden="true" tabindex="-1"></a><span class="co"># stack rasters</span></span> 3784<span id="cb6-76"><a href="#cb6-76" aria-hidden="true" tabindex="-1"></a>nlcd_stack <span class="ot"><-</span> <span class="fu">round</span>(<span class="fu">rast</span>(<span class="fu">compact</span>(nlcd)))</span> 3785<span id="cb6-77"><a href="#cb6-77" aria-hidden="true" tabindex="-1"></a></span> 3786<span id="cb6-78"><a href="#cb6-78" aria-hidden="true" tabindex="-1"></a><span class="co"># get unique pixel values</span></span> 3787<span id="cb6-79"><a href="#cb6-79" aria-hidden="true" tabindex="-1"></a>all_classes <span class="ot"><-</span> <span class="fu">sort</span>(<span class="fu">unique</span>(<span class="fu">values</span>(nlcd_stack<span class="sc">$</span><span class="st">`</span><span class="at">2023</span><span class="st">`</span>)))</span> 3788<span id="cb6-80"><a href="#cb6-80" aria-hidden="true" tabindex="-1"></a></span> 3789<span id="cb6-81"><a href="#cb6-81" aria-hidden="true" tabindex="-1"></a><span class="co"># create reclassification matrix from data frame</span></span> 3790<span id="cb6-82"><a href="#cb6-82" aria-hidden="true" tabindex="-1"></a>lc_type_reclass <span class="ot"><-</span> <span class="fu">data.frame</span>(</span> 3791<span id="cb6-83"><a href="#cb6-83" aria-hidden="true" tabindex="-1"></a> <span class="at">code =</span> all_classes</span> 3792<span id="cb6-84"><a href="#cb6-84" aria-hidden="true" tabindex="-1"></a>) <span class="sc">%>%</span></span> 3793<span id="cb6-85"><a href="#cb6-85" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(</span> 3794<span id="cb6-86"><a href="#cb6-86" aria-hidden="true" tabindex="-1"></a> <span class="at">reclass =</span> <span class="fu">case_when</span>(</span> 3795<span id="cb6-87"><a href="#cb6-87" aria-hidden="true" tabindex="-1"></a> code <span class="sc">%in%</span> <span class="dv">15</span><span class="sc">:</span><span class="dv">30</span>
3795 <span class="sc">~</span> <span class="dv">1</span>, <span class="co"># Developed (Open Space, Low Intensity, Median Intensity, High Intensity) => 1</span></span> 3796<span id="cb6-88"><a href="#cb6-88" aria-hidden="true" tabindex="-1"></a> code <span class="sc">%in%</span> <span class="dv">41</span><span class="sc">:</span><span class="dv">43</span> <span class="sc">~</span> <span class="dv">2</span>, <span class="co"># Forests (Deciduous, Evergreen, and Mixed) => 2</span></span> 3797<span id="cb6-89"><a href="#cb6-89" aria-hidden="true" tabindex="-1"></a> code <span class="sc">%in%</span> <span class="dv">44</span><span class="sc">:</span><span class="dv">59</span> <span class="sc">~</span> <span class="dv">3</span>, <span class="co"># Shrubs and Scrubs => 3</span></span> 3798<span id="cb6-90"><a href="#cb6-90" aria-hidden="true" tabindex="-1"></a> code <span class="sc">%in%</span> <span class="dv">60</span><span class="sc">:</span><span class="dv">74</span> <span class="sc">~</span> <span class="dv">4</span>, <span class="co"># Grassland => 4</span></span> 3799<span id="cb6-91"><a href="#cb6-91" aria-hidden="true" tabindex="-1"></a> code <span class="sc">%in%</span> <span class="dv">75</span><span class="sc">:</span><span class="dv">87</span> <span class="sc">~</span> <span class="dv">5</span>, <span class="co"># Agriculture (Pasture and Cultivated Crops) => 5</span></span> 3800<span id="cb6-92"><a href="#cb6-92" aria-hidden="true" tabindex="-1"></a> code <span class="sc">%in%</span> <span class="dv">88</span><span class="sc">:</span><span class="dv">95</span> <span class="sc">~</span> <span class="dv">6</span>, <span class="co"># Wetlands (Woody and Emergent Herbaceous) => 6</span></span> 3801<span id="cb6-93"><a href="#cb6-93" aria-hidden="true" tabindex="-1"></a> code <span class="sc">==</span> <span class="dv">12</span> <span class="sc">~</span> <span class="dv">7</span>, <span class="co"># Perennial Ice and Snow => 7</span></span> 3802<span id="cb6-94"><a href="#cb6-94" aria-hidden="true" tabindex="-1"></a> code <span class="sc">%in%</span> <span class="dv">11</span><span class="sc">:</span><span class="dv">20</span> <span class="sc">~</span> <span class="dv">8</span>, <span class="co"># Open Water => 8</span></span> 3803<span id="cb6-95"><a href="#cb6-95" aria-hidden="true" tabindex="-1"></a> code <span class="sc">%in%</span> <span class="dv">31</span><span class="sc">:</span><span class="dv">40</span> <span class="sc">~</span> <span class="dv">9</span>, <span class="co"># Barren Land or Mining => 9</span></span> 3804<span id="cb6-96"><a href="#cb6-96" aria-hidden="true" tabindex="-1"></a>
3804 <span class="fu">is.na</span>(code) <span class="sc">~</span> <span class="dv">0</span> <span class="co"># Unknown</span></span> 3805<span id="cb6-97"><a href="#cb6-97" aria-hidden="true" tabindex="-1"></a> )</span> 3806<span id="cb6-98"><a href="#cb6-98" aria-hidden="true" tabindex="-1"></a> )</span> 3807<span id="cb6-99"><a href="#cb6-99" aria-hidden="true" tabindex="-1"></a></span> 3808<span id="cb6-100"><a href="#cb6-100" aria-hidden="true" tabindex="-1"></a>lc_type_reclass <span class="ot"><-</span> <span class="fu">as.matrix</span>(lc_type_reclass)</span> 3809<span id="cb6-101"><a href="#cb6-101" aria-hidden="true" tabindex="-1"></a></span> 3810<span id="cb6-102"><a href="#cb6-102" aria-hidden="true" tabindex="-1"></a><span class="co"># create labels for reclassified categories</span></span> 3811<span id="cb6-103"><a href="#cb6-103" aria-hidden="true" tabindex="-1"></a>reclassed_labels <span class="ot"><-</span> <span class="fu">c</span>(</span> 3812<span id="cb6-104"><a href="#cb6-104" aria-hidden="true" tabindex="-1"></a> <span class="st">'0'</span> <span class="ot">=</span> <span class="st">'No Change or Unknown'</span>,</span> 3813<span id="cb6-105"><a href="#cb6-105" aria-hidden="true" tabindex="-1"></a> <span class="st">'1'</span> <span class="ot">=</span> <span class="st">'Developed'</span>,</span> 3814<span id="cb6-106"><a href="#cb6-106" aria-hidden="true" tabindex="-1"></a> <span class="st">'2'</span> <span class="ot">=</span> <span class="st">'Forest'</span>,</span> 3815<span id="cb6-107"><a href="#cb6-107" aria-hidden="true" tabindex="-1"></a> <span class="st">'3'</span> <span class="ot">=</span> <span class="st">'Shrubland'</span>,</span> 3816<span id="cb6-108"><a href="#cb6-108" aria-hidden="true" tabindex="-1"></a> <span class="st">'4'</span> <span class="ot">=</span> <span class="st">'Grassland'</span>,</span> 3817<span id="cb6-109"><a href="#cb6-109" aria-hidden="true" tabindex="-1"></a> <span class="st">'5'</span> <span class="ot">=</span> <span class="st">'Agriculture'</span>,</span> 3818<span id="cb6-110"><a href="#cb6-110" aria-hidden="true" tabindex="-1"></a> <span class="st">'6'</span> <span class="ot">=</span> <span class="st">'Wetlands'</span>,</span> 3819<span id="cb6-111"><a href="#cb6-111" aria-hidden="true" tabindex="-1"></a> <span class="st">'7'</span> <span class="ot">=</span> <span class="st">'Ice & Snow'</span>,</span> 3820<span id="cb6-112"><a href="#cb6-112" aria-hidden="true" tabindex="-1"></a> <span class="st">'8'</span> <span class="ot">=</span> <span class="st">'Water'</span>,</span> 3821<span id="cb6-113"><a href="#cb6-113" aria-hidden="true" tabindex="-1"></a> <span class="st">'9'</span> <span class="ot">=</span> <span class="st">'Barren Land'</span></span> 3822<span id="cb6-114"><a href="#cb6-114" aria-hidden="true" tabindex="-1"></a>)</span> 3823<span id="cb6-115"><a href="#cb6-115" aria-hidden="true" tabindex="-1"></a></span> 3824<span id="cb6-116"><a href="#cb6-116" aria-hidden="true" tabindex="-1"></a><span class="co"># apply reclassification across the whole stack</span></span> 3825<span id="cb6-117"><a href="#cb6-117" aria-hidden="true" tabindex="-1"></a>nlcd_stack.reclass <span class="ot"><-</span> <span class="fu">classify</span>(nlcd_stack, <span class="at">rcl =</span> lc_type_reclass, </span> 3826<span id="cb6-118"><a href="#cb6-118" aria-hidden="true" tabindex="-1"></a> <span class="at">others =</span> <span class="cn">NA</span>, <span class="at">progress =</span> <span class="dv">0</span>)</span> 3827<span id="cb6-119"><a href="#cb6-119" aria-hidden="true" tabindex="-1"></a></span> 3828<span id="cb6-120"><a href="#cb6-120" aria-hidden="true" tabindex="-1"></a><span class="do">################################################</span></span> 3829<span id="cb6-121"><a href="#cb6-121" aria-hidden="true" tabindex="-1"></a><span class="co"># RUN THE FIRST TIME, THEN RE-COMMENT OUT AFTER</span></span> 3830<span id="cb6-122"><a href="#cb6-122" aria-hidden="true" tabindex="-1"></a><span class="co"># THIS STEP CAN TAKE AROUND AN HOUR</span></span> 3831<span id="cb6-123"><a href="#cb6-123" aria-hidden="true" tabindex="-1"></a><span class="do">###############################################</span></span> 3832<span id="cb6-124"><a href="#cb6-124" aria-hidden="true" tabindex="-1"></a></span> 3833<span id="cb6-125"><a href="#cb6-125" aria-hidden="true" tabindex="-1"></a><span class="co"># download reclassified rasters to speed up process</span></span> 3834<span id="cb6-126"><a href="#cb6-126" aria-hidden="true" tabindex="-1"></a><span class="co"># for (yr in names(nlcd_stack.reclass)) {</span></span> 3835<span id="cb6-127"><a href="#cb6-127" aria-hidden="true" tabindex="-1"></a><span class="co"># cat('Downloading reclassified raster for year', paste(yr, '...', sep = ''))</span></span> 3836<span id="cb6-128"><a href="#cb6-128" aria-hidden="true" tabindex="-1"></a><span class="co"># </span></span> 3837<span id="cb6-129"><a href="#cb6-129" aria-hidden="true" tabindex="-1"></a><span class="co"># # set path of downloaded raster</span></span> 3838<span id="cb6-130"><a href="#cb6-130" aria-hidden="true" tabindex="-1"></a><span class="co"># download_path = paste(mrlc_data_dir, paste('nlcd_', yr, '_reclass', '.tif', sep=''), sep = '')</span></span> 3839<span id="cb6-131"><a href="#cb6-131" aria-hidden="true" tabindex="-1"></a><span class="co"># </span></span> 3840<span id="cb6-132"><a href="#cb6-132" aria-hidden="true" tabindex="-1"></a><span class="co"># # download raster</span></span> 3841<span id="cb6-133"><a href="#cb6-133" aria-hidden="true" tabindex="-1"></a><span class="co">
3841# writeRaster(nlcd_stack.reclass[[yr]], download_path, overwrite = TRUE)</span></span> 3842<span id="cb6-134"><a href="#cb6-134" aria-hidden="true" tabindex="-1"></a><span class="co"># }</span></span> 3843<span id="cb6-135"><a href="#cb6-135" aria-hidden="true" tabindex="-1"></a></span> 3844<span id="cb6-136"><a href="#cb6-136" aria-hidden="true" tabindex="-1"></a><span class="co"># convert sq meters to sq miles</span></span> 3845<span id="cb6-137"><a href="#cb6-137" aria-hidden="true" tabindex="-1"></a>pixel_area_sqmi <span class="ot"><-</span> (<span class="fl">29.49398</span> <span class="sc">*</span> <span class="fl">29.49398</span>) <span class="sc">*</span> <span class="fl">3.861e-7</span></span> 3846<span id="cb6-138"><a href="#cb6-138" aria-hidden="true" tabindex="-1"></a></span> 3847<span id="cb6-139"><a href="#cb6-139" aria-hidden="true" tabindex="-1"></a><span class="co"># prepare 3 background R sessions to process in parallel</span></span> 3848<span id="cb6-140"><a href="#cb6-140" aria-hidden="true" tabindex="-1"></a><span class="fu">plan</span>(multisession, <span class="at">workers =</span> <span class="dv">2</span>)</span> 3849<span id="cb6-141"><a href="#cb6-141" aria-hidden="true" tabindex="-1"></a></span> 3850<span id="cb6-142"><a href="#cb6-142" aria-hidden="true" tabindex="-1"></a><span class="co"># calculate total area of each land cover category </span></span> 3851<span id="cb6-143"><a href="#cb6-143" aria-hidden="true" tabindex="-1"></a><span class="co"># in square miles over the entire study area for each year (2008-2023)</span></span> 3852<span id="cb6-144"><a href="#cb6-144" aria-hidden="true" tabindex="-1"></a><span class="co"># have to also include 2008 due to </span></span> 3853<span id="cb6-145"><a href="#cb6-145" aria-hidden="true" tabindex="-1"></a>zonal_stats <span class="ot"><-</span> <span class="fu">future_lapply</span>(lc_yrs, <span class="cf">function</span>(yr) {</span> 3854<span id="cb6-146"><a href="#cb6-146" aria-hidden="true" tabindex="-1"></a> <span class="co">#print(paste('Calculating zonal statistics for', as.character(yr)))</span></span> 3855<span id="cb6-147"><a href="#cb6-147" aria-hidden="true" tabindex="-1"></a> </span> 3856<span id="cb6-148"><a href="#cb6-148" aria-hidden="true" tabindex="-1"></a> <span class="co"># get tract boundaries, then project</span></span> 3857<span id="cb6-149"><a href="#cb6-149" aria-hidden="true" tabindex="-1"></a> bgs_yr <span class="ot"><-</span> <span class="cf">if</span> (yr <span class="sc">==</span> <span class="dv">2008</span>) {</span> 3858<span id="cb6-150"><a href="#cb6-150" aria-hidden="true" tabindex="-1"></a> st_bgs_final <span class="sc">%>%</span> <span class="fu">filter</span>(year <span class="sc">==</span> <span class="dv">2009</span>)</span> 3859<span id="cb6-151"><a href="#cb6-151" aria-hidden="true" tabindex="-1"></a> } <span class="cf">else</span> {</span> 3860<span id="cb6-152"><a href="#cb6-152" aria-hidden="true" tabindex="-1"></a> st_bgs_final <span class="sc">%>%</span> <span class="fu">filter</span>(year <span class="sc">==</span> yr)</span> 3861<span id="cb6-153"><a href="#cb6-153" aria-hidden="true" tabindex="-1"></a> }</span> 3862<span id="cb6-154"><a href="#cb6-154" aria-hidden="true" tabindex="-1"></a> </span> 3863<span id="cb6-155"><a href="#cb6-155" aria-hidden="true" tabindex="-1"></a> bgs_yr <span class="ot"><-</span> <span class="fu">st_transform</span>(bgs_yr, <span class="dv">5070</span>)</span> 3864<span id="cb6-156"><a href="#cb6-156" aria-hidden="true" tabindex="-1"></a> </span> 3865<span id="cb6-157"><a href="#cb6-157" aria-hidden="true" tabindex="-1"></a> <span class="co"># load reclassified nlcd raster for year and project</span></span> 3866<span id="cb6-158"><a href="#cb6-158" aria-hidden="true" tabindex="-1"></a> lc <span class="ot"><-</span> <span class="fu">project</span>(</span> 3867<span id="cb6-159"><a href="#cb6-159" aria-hidden="true" tabindex="-1"></a> <span class="fu">rast</span>(<span class="fu">paste</span>(mrlc_data_dir, <span class="st">'/nlcd_'</span>, <span class="fu">as.character</span>(yr), <span class="st">'_reclass.tif'</span>, <span class="at">sep =</span> <span class="st">''</span>)),</span> 3868<span id="cb6-160"><a href="#cb6-160" aria-hidden="true" tabindex="-1"></a> <span class="st">'EPSG:5070'</span>,</span> 3869<span id="cb6-161"><a href="#cb6-161" aria-hidden="true" tabindex="-1"></a> <span class="at">progress =</span> <span class="dv">0</span></span> 3870<span id="cb6-162"><a href="#cb6-162" aria-hidden="true" tabindex="-1"></a> )</span> 3871<span id="cb6-163"><a href="#cb6-163" aria-hidden="true" tabindex="-1"></a> </span> 3872<span id="cb6-164"><a href="#cb6-164" aria-hidden="true" tabindex="-1"></a> <span class="co"># extract counts of each class per tract</span></span> 3873<span id="cb6-165"><a href="#cb6-165" aria-hidden="true" tabindex="-1"></a> z <span class="ot"><-</span> terra<span class="sc">::</span><span class="fu">extract</span>(</span> 3874<span id="cb6-166"><a href="#cb6-166" aria-hidden="true" tabindex="-1"></a> lc, <span class="fu">vect</span>(bgs_yr),</span> 3875<span id="cb6-167"><a href="#cb6-167" aria-hidden="true" tabindex="-1"></a> <span class="at">fun =</span> <span class="cf">function</span>(x, ...) <span class="fu">table</span>(<span class="fu">factor</span>
3875(x, <span class="at">levels =</span> <span class="dv">1</span><span class="sc">:</span><span class="dv">9</span>)),</span> 3876<span id="cb6-168"><a href="#cb6-168" aria-hidden="true" tabindex="-1"></a> <span class="at">progress =</span> <span class="dv">0</span></span> 3877<span id="cb6-169"><a href="#cb6-169" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">%>%</span> </span> 3878<span id="cb6-170"><a href="#cb6-170" aria-hidden="true" tabindex="-1"></a> <span class="fu">st_drop_geometry</span>() <span class="sc">%>%</span></span> 3879<span id="cb6-171"><a href="#cb6-171" aria-hidden="true" tabindex="-1"></a> <span class="fu">as.data.frame</span>()</span> 3880<span id="cb6-172"><a href="#cb6-172" aria-hidden="true" tabindex="-1"></a> </span> 3881<span id="cb6-173"><a href="#cb6-173" aria-hidden="true" tabindex="-1"></a> <span class="co"># replace index IDs with actual GEOIDs</span></span> 3882<span id="cb6-174"><a href="#cb6-174" aria-hidden="true" tabindex="-1"></a> z<span class="sc">$</span>GEOID <span class="ot"><-</span> <span class="fu">as.character</span>(bgs_yr<span class="sc">$</span>GEOID)</span> 3883<span id="cb6-175"><a href="#cb6-175" aria-hidden="true" tabindex="-1"></a> </span> 3884<span id="cb6-176"><a href="#cb6-176" aria-hidden="true" tabindex="-1"></a> <span class="co"># remove the index column</span></span> 3885<span id="cb6-177"><a href="#cb6-177" aria-hidden="true" tabindex="-1"></a> z <span class="ot"><-</span> z <span class="sc">%>%</span> <span class="fu">select</span>(<span class="sc">-</span>ID) <span class="sc">%>%</span> <span class="fu">relocate</span>(<span class="dv">1</span><span class="sc">:</span><span class="dv">9</span>, <span class="at">.after =</span> GEOID)</span> 3886<span id="cb6-178"><a href="#cb6-178" aria-hidden="true" tabindex="-1"></a> </span> 3887<span id="cb6-179"><a href="#cb6-179" aria-hidden="true" tabindex="-1"></a> <span class="co"># rename columns</span></span> 3888<span id="cb6-180"><a href="#cb6-180" aria-hidden="true" tabindex="-1"></a> <span class="fu">colnames</span>(z) <span class="ot"><-</span> <span class="fu">c</span>(<span class="st">'GEOID'</span>, <span class="fu">as.character</span>(<span class="dv">1</span><span class="sc">:</span><span class="dv">9</span>))</span> 3889<span id="cb6-181"><a href="#cb6-181" aria-hidden="true" tabindex="-1"></a> </span> 3890<span id="cb6-182"><a href="#cb6-182" aria-hidden="true" tabindex="-1"></a> <span class="co"># elongate data, then return it</span></span> 3891<span id="cb6-183"><a href="#cb6-183" aria-hidden="true" tabindex="-1"></a> z_long <span class="ot"><-</span> </span> 3892<span id="cb6-184"><a href="#cb6-184" aria-hidden="true" tabindex="-1"></a> z <span class="sc">%>%</span></span> 3893<span id="cb6-185"><a href="#cb6-185" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="fu">across</span>(<span class="fu">all_of</span>(<span class="fu">as.character</span>(<span class="dv">1</span><span class="sc">:</span><span class="dv">9</span>)), as.numeric)) <span class="sc">%>%</span></span> 3894<span id="cb6-186"><a href="#cb6-186" aria-hidden="true" tabindex="-1"></a> <span class="fu">pivot_longer</span>(</span> 3895<span id="cb6-187"><a href="#cb6-187" aria-hidden="true" tabindex="-1"></a>
3895 <span class="at">cols =</span> <span class="fu">all_of</span>(<span class="fu">as.character</span>(<span class="dv">1</span><span class="sc">:</span><span class="dv">9</span>)),</span> 3896<span id="cb6-188"><a href="#cb6-188" aria-hidden="true" tabindex="-1"></a> <span class="at">names_to =</span> <span class="st">'land_cover_class'</span>,</span> 3897<span id="cb6-189"><a href="#cb6-189" aria-hidden="true" tabindex="-1"></a> <span class="at">values_to =</span> <span class="st">'pixel_count'</span></span> 3898<span id="cb6-190"><a href="#cb6-190" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">%>%</span></span> 3899<span id="cb6-191"><a href="#cb6-191" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(</span> 3900<span id="cb6-192"><a href="#cb6-192" aria-hidden="true" tabindex="-1"></a> <span class="at">year =</span> yr,</span> 3901<span id="cb6-193"><a href="#cb6-193" aria-hidden="true" tabindex="-1"></a> <span class="at">land_cover_class =</span> reclassed_labels[land_cover_class],</span> 3902<span id="cb6-194"><a href="#cb6-194" aria-hidden="true" tabindex="-1"></a>
3902 <span class="at">area_sqmi =</span> pixel_count <span class="sc">*</span> pixel_area_sqmi</span> 3903<span id="cb6-195"><a href="#cb6-195" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">%>%</span></span> 3904<span id="cb6-196"><a href="#cb6-196" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(GEOID, year, land_cover_class, area_sqmi)</span> 3905<span id="cb6-197"><a href="#cb6-197" aria-hidden="true" tabindex="-1"></a> </span> 3906<span id="cb6-198"><a href="#cb6-198" aria-hidden="true" tabindex="-1"></a> <span class="fu">return</span>(z_long)</span> 3907<span id="cb6-199"><a href="#cb6-199" aria-hidden="true" tabindex="-1"></a>}, <span class="at">future.seed =</span> <span class="cn">TRUE</span>)</span> 3908<span id="cb6-200"><a href="#cb6-200" aria-hidden="true" tabindex="-1"></a></span> 3909<span id="cb6-201"><a href="#cb6-201" aria-hidden="true" tabindex="-1"></a><span class="co"># switch back to sequential processing</span></span> 3910<span id="cb6-202"><a href="#cb6-202" aria-hidden="true" tabindex="-1"></a><span class="fu">plan</span>(sequential)</span> 3911<span id="cb6-203"><a href="#cb6-203" aria-hidden="true" tabindex="-1"></a></span> 3912<span id="cb6-204"><a href="#cb6-204" aria-hidden="true" tabindex="-1"></a><span class="co"># combine zonal stats</span></span> 3913<span id="cb6-205"><a href="#cb6-205" aria-hidden="true" tabindex="-1"></a>zonal_stats_full <span class="ot"><-</span> <span class="fu">bind_rows</span>(zonal_stats)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div> 3914</details> 3915</div> 3916<div style="page-break-after: always;"></div> 3917</section> 3918<section id="land-cover-calculations-at-block-group-level" class="level4"> 3919<h4 class="anchored" data-anchor-id="land-cover-calculations-at-block-group-level">Land Cover Calculations at Block Group Level</h4> 3920<p>Calculate the total coverage of each land cover class within each block group in each of the 15 years.</p> 3921<div class="cell"> 3922<details class="code-fold"> 3923<summary>Code</summary> 3924<div class="sourceCode cell-code" id="cb7"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="co"># calculate total areas of each class for each GEOID in each year,</span></span> 3925<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a><span class="co"># as well as the percent change from the previous year</span></span> 3926<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a><span class="co"># fill all null values</span></span> 3927<span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a><span class="co"># pivot to wide format</span></span> 3928<span id="cb7-5"><a href="#cb7-5" aria-hidden="true" tabindex="-1"></a>zonal_changes <span class="ot"><-</span></span> 3929<span id="cb7-6"><a href="#cb7-6" aria-hidden="true" tabindex="-1"></a> zonal_stats_full <span class="sc">%>%</span></span> 3930<span id="cb7-7"><a href="#cb7-7" aria-hidden="true" tabindex="-1"></a> <span class="fu">arrange</span>(GEOID, land_cover_class, year) <span class="sc">%>%</span></span> 3931<span id="cb7-8"><a href="#cb7-8" aria-hidden="true" tabindex="-1"></a> <span class="fu">group_by</span>(GEOID, land_cover_class) <span class="sc">%>%</span></span> 3932<span id="cb7-9"><a href="#cb7-9" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(</span> 3933<span id="cb7-10"><a href="#cb7-10" aria-hidden="true" tabindex="-1"></a> <span class="at">prev_area_sqmi =</span> <span class="fu">lag</span>(area_sqmi),</span> 3934<span id="cb7-11"><a href="#cb7-11" aria-hidden="true" tabindex="-1"></a> <span class="at">pct_change =</span> <span class="fu">ifelse</span>(</span> 3935<span id="cb7-12"><a href="#cb7-12" aria-hidden="true" tabindex="-1"></a>
3935 <span class="sc">!</span><span class="fu">is.na</span>(prev_area_sqmi) <span class="sc">&</span> prev_area_sqmi <span class="sc">></span> <span class="dv">0</span>,</span> 3936<span id="cb7-13"><a href="#cb7-13" aria-hidden="true" tabindex="-1"></a> ((area_sqmi <span class="sc">-</span> prev_area_sqmi) <span class="sc">/</span> prev_area_sqmi),</span> 3937<span id="cb7-14"><a href="#cb7-14" aria-hidden="true" tabindex="-1"></a> <span class="dv">0</span></span> 3938<span id="cb7-15"><a href="#cb7-15" aria-hidden="true" tabindex="-1"></a> )</span> 3939<span id="cb7-16"><a href="#cb7-16" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">%>%</span></span> 3940<span id="cb7-17"><a href="#cb7-17" aria-hidden="true" tabindex="-1"></a> <span class="fu">ungroup</span>() <span class="sc">%>%</span></span> 3941<span id="cb7-18"><a href="#cb7-18" aria-hidden="true" tabindex="-1"></a> <span class="fu">pivot_wider</span>(<span class="at">id_cols =</span> <span class="fu">c</span>(GEOID, year),</span> 3942<span id="cb7-19"><a href="#cb7-19" aria-hidden="true" tabindex="-1"></a> <span class="at">names_from=</span>land_cover_class,</span> 3943<span id="cb7-20"><a href="#cb7-20" aria-hidden="true" tabindex="-1"></a> <span class="at">values_from =</span> <span class="fu">c</span>(area_sqmi, pct_change),</span> 3944<span id="cb7-21"><a href="#cb7-21" aria-hidden="true" tabindex="-1"></a> <span class="at">names_glue =</span> <span class="st">'{land_cover_class}_{.value}'</span>) <span class="sc">%>%</span></span> 3945<span id="cb7-22"><a href="#cb7-22" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="fu">across</span>(<span class="fu">everything</span>(), <span class="sc">~</span> <span class="fu">ifelse</span>(<span class="fu">is.na</span>(.), <span class="dv">0</span>, .)),</span> 3946<span id="cb7-23"><a href="#cb7-23" aria-hidden="true" tabindex="-1"></a> <span class="at">GEOID =</span> <span class="fu">as.numeric</span>(GEOID))</span> 3947<span id="cb7-24"><a href="#cb7-24" aria-hidden="true" tabindex="-1"></a></span> 3948<span id="cb7-25"><a href="#cb7-25" aria-hidden="true" tabindex="-1"></a><span class="co"># join zonal changes to final st_bgs_final dataframe</span></span> 3949<span id="cb7-26"><a href="#cb7-26" aria-hidden="true" tabindex="-1"></a>st_bgs_all <span class="ot"><-</span></span> 3950<span id="cb7-27"><a href="#cb7-27" aria-hidden="true" tabindex="-1"></a> st_bgs_final <span class="sc">%>%</span></span> 3951<span id="cb7-28"><a href="#cb7-28" aria-hidden="true" tabindex="-1"></a> <span class="fu">st_transform</span>(<span class="at">crs=</span> <span class="st">'EPSG:26918'</span>) <span class="sc">%>%</span> </span> 3952<span id="cb7-29"><a href="#cb7-29" aria-hidden="true" tabindex="-1"></a> <span class="fu">left_join</span>(zonal_changes <span class="sc">%>%</span> <span class="fu">filter</span>(year <span class="sc">!=</span> <span class="dv">2008</span>), </span> 3953<span id="cb7-30"><a href="#cb7-30" aria-hidden="true" tabindex="-1"></a> <span class="at">by =</span> <span class="fu">c</span>(<span class="st">'GEOID'</span>, <span class="st">'year'</span>)) <span class="sc">%>%</span></span> 3954<span id="cb7-31"><a href="#cb7-31" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(</span> 3955<span id="cb7-32"><a href="#cb7-32" aria-hidden="true" tabindex="-1"></a> <span class="at">pct_agriculture =</span> Agriculture_area_sqmi <span class="sc">/</span> area_sqmi,</span> 3956<span id="cb7-33"><a href="#cb7-33" aria-hidden="true" tabindex="-1"></a> <span class="at">pct_barren =</span> <span class="st">`</span><span class="at">Barren Land_area_sqmi</span><span class="st">`</span> <span class="sc">/</span> area_sqmi,</span> 3957<span id="cb7-34"><a href="#cb7-34" aria-hidden="true" tabindex="-1"></a> <span class="at">pct_developed =</span> Developed_area_sqmi <span class="sc">/</span> area_sqmi,</span> 3958<span id="cb7-35"><a href="#cb7-35" aria-hidden="true" tabindex="-1"></a> <span class="at">pct_forest =</span> Forest_area_sqmi <span class="sc">/</span> area_sqmi,</span> 3959<span id="cb7-36"><a href="#cb7-36" aria-hidden="true" tabindex="-1"></a> <span class="at">pct_grassland =</span> Grassland_area_sqmi,</span> 3960<span id="cb7-37"><a href="#cb7-37" aria-hidden="true" tabindex="-1"></a> <span class="at">pct_ice_snow =</span> <span class="st">`</span><span class="at">Ice & Snow_area_sqmi</span><span class="st">`</span> <span class="sc">/</span> area_sqmi,</span> 3961<span id="cb7-38"><a href="#cb7-38" aria-hidden="true" tabindex="-1"></a> <span class="at">pct_shrubland =</span> Shrubland_area_sqmi <span class="sc">/</span> area_sqmi,</span> 3962<span id="cb7-39"><a href="#cb7-39" aria-hidden="true" tabindex="-1"></a> <span class="at">pct_water =</span> Water_area_sqmi <span class="sc">/</span> area_sqmi,</span> 3963<span id="cb7-40"><a href="#cb7-40" aria-hidden="true" tabindex="-1"></a> <span class="at">pct_wetlands =</span> Wetlands_area_sqmi <span class="sc">/</span> area_sqmi</span> 3964<span id="cb7-41"><a href="#cb7-41" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">%>%</span></span> 3965<span id="cb7-42"><a href="#cb7-42" aria-hidden="true" tabindex="-1"></a> <span class="fu">relocate</span>(geometry, <span class="at">.after =</span> <span class="fu">
3965last_col</span>())</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div> 3966</details> 3967</div> 3968</section> 3969</section> 3970</section> 3971</section> 3972<section id="results" class="level1"> 3973<h1>Results</h1> 3974<section id="logit-mixed-effects-model-using-glmmtmb" class="level2"> 3975<h2 class="anchored" data-anchor-id="logit-mixed-effects-model-using-glmmtmb">Logit Mixed-Effects Model Using glmmTMB</h2> 3976<p>There are 15,060 observations in the dataset, which represent the 1,004 uniform block groups across a 15-year period. The model predicts the distressed status of a block group given the scaled predictors (population density, unemployment rate, spatially lagged unemployment rate, average rent, divorced rate, and percent developed), while incorporating a categorical time period variable (year_group) and taking into account the fact that some block groups may be inherently more or less at risk than average through the random intercepts for each individual block group (1 | GEOID). The model was trained using a random sample of 80% of the unique 1,004 block group GEOIDs in the study, which amounts to 803 block groups and 12,045 training observations.</p> 3977<div class="cell"> 3978<details class="code-fold"> 3979<summary>Code</summary> 3980<div class="sourceCode cell-code" id="cb8"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="fu">set.seed</span>(<span class="dv">1234</span>)</span> 3981<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a></span> 3982<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a><span class="co"># scale quantitative independent variables, then place each observation in a year group,</span></span> 3983<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a><span class="co"># create if_distressed value</span></span> 3984<span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a>st_bgs.scaled <span class="ot"><-</span></span> 3985<span id="cb8-6"><a href="#cb8-6" aria-hidden="true" tabindex="-1"></a> st_bgs_all <span class="sc">%>%</span></span> 3986<span id="cb8-7"><a href="#cb8-7" aria-hidden="true" tabindex="-1"></a> <span class="fu">rename</span>(</span> 3987<span id="cb8-8"><a href="#cb8-8" aria-hidden="true" tabindex="-1"></a> <span class="at">Barren_Land_area_sqmi =</span> <span class="st">`</span><span class="at">Barren Land_area_sqmi</span><span class="st">`</span>,</span> 3988<span id="cb8-9"><a href="#cb8-9" aria-hidden="true" tabindex="-1"></a> <span class="at">Barren_Land_pct_change =</span> <span class="st">`</span><span class="at">Barren Land_pct_change</span><span class="st">`</span></span> 3989<span id="cb8-10"><a href="#cb8-10" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">%>%</span></span> 3990<span id="cb8-11"><a href="#cb8-11" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(</span> 3991<span id="cb8-12"><a href="#cb8-12" aria-hidden="true" tabindex="-1"></a> <span class="at">tot_pop_density =</span> <span class="fu">scale</span>(tot_pop_density)[,<span class="dv">1</span>],</span> 3992<span id="cb8-13"><a href="#cb8-13" aria-hidden="true" tabindex="-1"></a> <span class="at">per_capita_income =</span> <span class="fu">scale</span>(per_capita_income)[,<span class="dv">1</span>],</span> 3993<span id="cb8-14"><a href="#cb8-14" aria-hidden="true" tabindex="-1"></a> <span class="at">pct_below_poverty =</span> <span class="fu">scale</span>(pct_below_poverty)[,<span class="dv">1</span>],</span> 3994<span id="cb8-15"><a href="#cb8-15" aria-hidden="true" tabindex="-1"></a> <span class="at">pct_didnt_work_past_yr =</span> <span class="fu">scale</span>(pct_didnt_work_past_yr)[,<span class="dv">1</span>],</span> 3995<span id="cb8-16"><a href="#cb8-16" aria-hidden="true" tabindex="-1"></a> <span class="at">pct_family_hhs =</span> <span class="fu">scale</span>(pct_family_hhs)[,<span class="dv">1</span>],</span> 3996<span id="cb8-17"><a href="#cb8-17" aria-hidden="true" tabindex="-1"></a> <span class="at">pct_divorced =</span> <span class="fu">scale</span>(pct_divorced)[,<span class="dv">1</span>],</span> 3997<span id="cb8-18"><a href="#cb8-18" aria-hidden="true" tabindex="-1"></a> <span class="at">avg_fam_income =</span> <span class="fu">scale</span>(avg_fam_income)[,<span class="dv">1</span>],</span> 3998<span id="cb8-19"><a href="#cb8-19" aria-hidden="true" tabindex="-1"></a> <span class="at">pct_developed =</span> <span class="fu">scale</span>(pct_developed)[,<span class="dv">1</span>],</span> 3999<span id="cb8-20"><a href="#cb8-20" aria-hidden="true" tabindex="-1"></a> <span class="at">year_group =</span> <span class="fu">case_when</span>(</span> 4000<span id="cb8-21"><a href="#cb8-21" aria-hidden="true" tabindex="-1"></a> year <span class="sc"><=</span> <span class="dv">2013</span> <span class="sc">~</span> <span class="st">'2009-2013'</span>,</span> 4001<span id="cb8-22"><a href="#cb8-22" aria-hidden="true" tabindex="-1"></a> year <span class="sc"><=</span> <span class="dv">2018</span> <span class="sc">~</span> <span class="st">'2014-2018'</span>,</span> 4002<span id="cb8-23"><a href="#cb8-23" aria-hidden="true" tabindex="-1"></a>
4002 <span class="cn">TRUE</span> <span class="sc">~</span> <span class="st">'2019-2023'</span></span> 4003<span id="cb8-24"><a href="#cb8-24" aria-hidden="true" tabindex="-1"></a> ),</span> 4004<span id="cb8-25"><a href="#cb8-25" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">%>%</span></span> 4005<span id="cb8-26"><a href="#cb8-26" aria-hidden="true" tabindex="-1"></a> <span class="fu">relocate</span>(geometry, <span class="at">.after =</span> <span class="fu">last_col</span>()) <span class="sc">%>%</span></span> 4006<span id="cb8-27"><a href="#cb8-27" aria-hidden="true" tabindex="-1"></a> <span class="fu">st_transform</span>(<span class="at">crs =</span> <span class="dv">5070</span>)</span> 4007<span id="cb8-28"><a href="#cb8-28" aria-hidden="true" tabindex="-1"></a></span> 4008<span id="cb8-29"><a href="#cb8-29" aria-hidden="true" tabindex="-1"></a><span class="do">###</span></span> 4009<span id="cb8-30"><a href="#cb8-30" aria-hidden="true" tabindex="-1"></a><span class="co"># define neighbors and weights to add spatial lag variables</span></span> 4010<span id="cb8-31"><a href="#cb8-31" aria-hidden="true" tabindex="-1"></a><span class="do">###</span></span> 4011<span id="cb8-32"><a href="#cb8-32" aria-hidden="true" tabindex="-1"></a></span> 4012<span id="cb8-33"><a href="#cb8-33" aria-hidden="true" tabindex="-1"></a><span class="co"># get centroids of unique block groups</span></span> 4013<span id="cb8-34"><a href="#cb8-34" aria-hidden="true" tabindex="-1"></a>unique_bgs <span class="ot"><-</span> st_bgs.scaled <span class="sc">%>%</span> <span class="fu">distinct</span>(GEOID, <span class="at">.keep_all =</span> <span class="cn">TRUE</span>)</span> 4014<span id="cb8-35"><a href="#cb8-35" aria-hidden="true" tabindex="-1"></a>centroids <span class="ot"><-</span> <span class="fu">st_centroid</span>(unique_bgs)</span> 4015<span id="cb8-36"><a href="#cb8-36" aria-hidden="true" tabindex="-1"></a>coords <span class="ot"><-</span> <span class="fu">st_coordinates</span>(centroids)[, <span class="dv">1</span><span class="sc">:</span><span class="dv">2</span>]</span> 4016<span id="cb8-37"><a href="#cb8-37" aria-hidden="true" tabindex="-1"></a></span> 4017<span id="cb8-38"><a href="#cb8-38" aria-hidden="true" tabindex="-1"></a><span class="co"># define k-nearest neighbors</span></span> 4018<span id="cb8-39"><a href="#cb8-39" aria-hidden="true" tabindex="-1"></a>k <span class="ot"><-</span> <span class="dv">6</span></span> 4019<span id="cb8-40"><a href="#cb8-40" aria-hidden="true" tabindex="-1"></a>knn_neighbors <span class="ot"><-</span> <span class="fu">knearneigh</span>(coords, <span class="at">k =</span> k)</span> 4020<span id="cb8-41"><a href="#cb8-41" aria-hidden="true" tabindex="-1"></a>nb <span class="ot"><-</span> <span class="fu">knn2nb</span>(knn_neighbors)</span> 4021<span id="cb8-42"><a href="#cb8-42" aria-hidden="true" tabindex="-1"></a></span> 4022<span id="cb8-43"><a href="#cb8-43" aria-hidden="true" tabindex="-1"></a><span class="co"># create spatial weights matrix</span></span> 4023<span id="cb8-44"><a href="#cb8-44" aria-hidden="true" tabindex="-1"></a>lw <span class="ot"><-</span> <span class="fu">nb2listw</span>(nb, <span class="at">style =</span> <span class="st">'W'</span>)</span> 4024<span id="cb8-45"><a href="#cb8-45" aria-hidden="true" tabindex="-1"></a></span> 4025<span id="cb8-46"><a href="#cb8-46" aria-hidden="true" tabindex="-1"></a><span class="co"># pct_didnt_work_past_yr as spatial</span></span> 4026<span id="cb8-47"><a href="#cb8-47" aria-hidden="true" tabindex="-1"></a>lag_didnt_work <span class="ot"><-</span> <span class="fu">lag.listw</span>(</span> 4027<span id="cb8-48"><a href="#cb8-48" aria-hidden="true" tabindex="-1"></a> lw, st_bgs.scaled<span class="sc">$</span>pct_didnt_work_past_yr[<span class="sc">!</span><span class="fu">duplicated</span>(st_bgs.scaled<span class="sc">$</span>GEOID)]</span> 4028<span id="cb8-49"><a href="#cb8-49" aria-hidden="true" tabindex="-1"></a>)</span> 4029<span id="cb8-50"><a href="#cb8-50" aria-hidden="true" tabindex="-1"></a></span> 4030<span id="cb8-51"><a href="#cb8-51" aria-hidden="true" tabindex="-1"></a><span class="co"># append lags to each unique GEOID</span></span> 4031<span id="cb8-52"><a href="#cb8-52" aria-hidden="true" tabindex="-1"></a>st_bgs.lagged <span class="ot"><-</span> </span> 4032<span id="cb8-53"><a href="#cb8-53" aria-hidden="true" tabindex="-1"></a> st_bgs.scaled <span class="sc">%>%</span></span> 4033<span id="cb8-54"><a href="#cb8-54" aria-hidden="true" tabindex="-1"></a> <span class="fu">distinct</span>(GEOID, <span class="at">.keep_all =</span> <span class="cn">TRUE</span>) <span class="sc">%>%</span></span> 4034<span id="cb8-55"><a href="#cb8-55" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">lag_pct_didnt_work_past_yr =</span> lag_didnt_work)</span> 4035<span id="cb8-56"><a href="#cb8-56" aria-hidden="true" tabindex="-1"></a></span> 4036<span id="cb8-57"><a href="#cb8-57" aria-hidden="true" tabindex="-1"></a><span class="co"># join back to full dataset</span></span> 4037<span id="cb8-58"><a href="#cb8-58" aria-hidden="true" tabindex="-1"></a>st_bgs.scaled_lagged <span class="ot"><-</span></span> 4038<span id="cb8-59"><a href="#cb8-59" aria-hidden="true" tabindex="-1"></a> st_bgs.scaled <span class="sc">%>%</span></span> 4039<span id="cb8-60"><a href="#cb8-60" aria-hidden="true" tabindex="-1"></a> <span class="fu">left_join</span>(</span> 4040<span id="cb8-61"><a href="#cb8-61" aria-hidden="true" tabindex="-1"></a> st_bgs.lagged <span class="sc">%>%</span> </span> 4041<span id="cb8-62"><a href="#cb8-62" aria-hidden="true" tabindex="-1"></a>
4041 <span class="fu">select</span>(GEOID, lag_pct_didnt_work_past_yr) <span class="sc">%>%</span> </span> 4042<span id="cb8-63"><a href="#cb8-63" aria-hidden="true" tabindex="-1"></a> st_drop_geometry,</span> 4043<span id="cb8-64"><a href="#cb8-64" aria-hidden="true" tabindex="-1"></a> <span class="at">by =</span> <span class="st">'GEOID'</span></span> 4044<span id="cb8-65"><a href="#cb8-65" aria-hidden="true" tabindex="-1"></a> )</span> 4045<span id="cb8-66"><a href="#cb8-66" aria-hidden="true" tabindex="-1"></a></span> 4046<span id="cb8-67"><a href="#cb8-67" aria-hidden="true" tabindex="-1"></a><span class="co"># get unique GEOIDs</span></span> 4047<span id="cb8-68"><a href="#cb8-68" aria-hidden="true" tabindex="-1"></a>unique_geoids <span class="ot"><-</span> <span class="fu">unique</span>(st_bgs.scaled_lagged<span class="sc">$</span>GEOID)</span> 4048<span id="cb8-69"><a href="#cb8-69" aria-hidden="true" tabindex="-1"></a></span> 4049<span id="cb8-70"><a href="#cb8-70" aria-hidden="true" tabindex="-1"></a><span class="co"># randomly split GEOIDs into train (80%) and test (20%) sets</span></span> 4050<span id="cb8-71"><a href="#cb8-71" aria-hidden="true" tabindex="-1"></a>train_geoids <span class="ot"><-</span> <span class="fu">sample</span>(unique_geoids, <span class="at">size =</span> <span class="fl">0.8</span> <span class="sc">*</span> <span class="fu">length</span>(<span class="fu">unique</span>(unique_geoids)))</span> 4051<span id="cb8-72"><a href="#cb8-72" aria-hidden="true" tabindex="-1"></a>test_geoids <span class="ot"><-</span> <span class="fu">setdiff</span>(unique_geoids, train_geoids)</span> 4052<span id="cb8-73"><a href="#cb8-73" aria-hidden="true" tabindex="-1"></a></span> 4053<span id="cb8-74"><a href="#cb8-74" aria-hidden="true" tabindex="-1"></a><span class="co"># split data into train and test datasets</span></span> 4054<span id="cb8-75"><a href="#cb8-75" aria-hidden="true" tabindex="-1"></a>train_data <span class="ot"><-</span> st_bgs.scaled_lagged <span class="sc">%>%</span> <span class="fu">filter</span>(GEOID <span class="sc">%in%</span> train_geoids)</span> 4055<span id="cb8-76"><a href="#cb8-76" aria-hidden="true" tabindex="-1"></a>test_data <span class="ot"><-</span> st_bgs.scaled_lagged <span class="sc">%>%</span> <span class="fu">filter</span>(GEOID <span class="sc">%in%</span> test_geoids)</span> 4056<span id="cb8-77"><a href="#cb8-77" aria-hidden="true" tabindex="-1"></a></span> 4057<span id="cb8-78"><a href="#cb8-78" aria-hidden="true" tabindex="-1"></a><span class="co"># # check</span></span> 4058<span id="cb8-79"><a href="#cb8-79" aria-hidden="true" tabindex="-1"></a><span class="co"># n_distinct(train_data$GEOID)</span></span> 4059<span id="cb8-80"><a href="#cb8-80" aria-hidden="true" tabindex="-1"></a><span class="co"># n_distinct(test_data$GEOID)</span></span> 4060<span id="cb8-81"><a href="#cb8-81" aria-hidden="true" tabindex="-1"></a></span> 4061<span id="cb8-82"><a href="#cb8-82" aria-hidden="true" tabindex="-1"></a><span class="do">######################################</span></span> 4062<span id="cb8-83"><a href="#cb8-83" aria-hidden="true" tabindex="-1"></a><span class="co"># glmm with template model builder</span></span> 4063<span id="cb8-84"><a href="#cb8-84" aria-hidden="true" tabindex="-1"></a><span class="co"># a spatial lag variable (pct_didnt_work_past_yr), </span></span> 4064<span id="cb8-85"><a href="#cb8-85" aria-hidden="true" tabindex="-1"></a><span class="co"># a fixed year_group categorical variable, </span></span> 4065<span id="cb8-86"><a href="#cb8-86" aria-hidden="true" tabindex="-1"></a><span class="co"># and GEOID as random effects</span></span> 4066<span id="cb8-87"><a href="#cb8-87" aria-hidden="true" tabindex="-1"></a><span class="do">######################################</span></span> 4067<span id="cb8-88"><a href="#cb8-88" aria-hidden="true" tabindex="-1"></a>model_glmm <span class="ot"><-</span> <span class="fu">glmmTMB</span>(</span> 4068<span id="cb8-89"><a href="#cb8-89" aria-hidden="true" tabindex="-1"></a> is_distressed <span class="sc">~</span> tot_pop_density <span class="sc">+</span>
4068 pct_didnt_work_past_yr <span class="sc">+</span> lag_pct_didnt_work_past_yr <span class="sc">+</span></span> 4069<span id="cb8-90"><a href="#cb8-90" aria-hidden="true" tabindex="-1"></a> avg_rent <span class="sc">+</span> pct_divorced <span class="sc">+</span> pct_developed <span class="sc">+</span> <span class="fu">factor</span>(year_group) <span class="sc">+</span> (<span class="dv">1</span> <span class="sc">|</span> GEOID),</span> 4070<span id="cb8-91"><a href="#cb8-91" aria-hidden="true" tabindex="-1"></a> <span class="at">data =</span> train_data,</span> 4071<span id="cb8-92"><a href="#cb8-92" aria-hidden="true" tabindex="-1"></a> <span class="at">family =</span> <span class="fu">binomial</span>(<span class="at">link =</span> <span class="st">'logit'</span>)</span> 4072<span id="cb8-93"><a href="#cb8-93" aria-hidden="true" tabindex="-1"></a>)</span> 4073<span id="cb8-94"><a href="#cb8-94" aria-hidden="true" tabindex="-1"></a></span> 4074<span id="cb8-95"><a href="#cb8-95" aria-hidden="true" tabindex="-1"></a><span class="co">#summary(model_glmm)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div> 4075</details> 4076</div> 4077<p>The model can be expressed in mathematical terms as:</p> 4078<div class="quarto-figure quarto-figure-center"> 4079<figure class="figure"> 4080<p><img role="img" aria-label="Distress presence predictive logit mixed-effects model." 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wtbXFU089hbKyMkRGRgoXe2QlJCSgpqYGSqUSwcHB3M0ywzD4xz/+gQ8//BAPHjxo0o2DJWOnU9y1axfi4+MxevRoi9yPzUFBKyEtzNHREV9//bXR+VlN8ff350b2fvPNN5g4cSJiY2OFiz1ydu7ciUmTJqG0tBQLFixAVVUVLly4gE8++QRjxozB2rVr4erqajAvjJDWMmTIEJSUlEAmk3G9HGPGjIFIJMK3334LhmHw6quvGs0bb
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8+TIGDRrEaylkicVinD17FgzDYNu2bbCzs8OwYcO4bu76ZGZmNipgNZdGo6m3RbA5sEHTM888I6wCtNuJTT2JjY2FtbU1j
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" class="img-fluid figure-img" alt="Distress presence predictive logit mixed-effects model."></p> 4081<figcaption>Distress presence predictive logit mixed-effects model</figcaption> 4082</figure> 4083</div> 4084<p>Where:</p> 4085<ul> 4086<li>β<sub>0</sub> -> fixed intercept</li> 4087<li>β<sub>1</sub>,â¦,β<sub>6</sub> -> fixed-effect coefficients</li> 4088<li>γ<sub>k</sub>γ<sub>k</sub> -> fixed effects for year group (excluding the reference group)</li> 4089<li>u<sub>i</sub> -> random intercept for block group i (GEOID)</li> 4090</ul> 4091<p>Before interpretation and result analysis can be conducted, various diagnostic tests must be completed to ensure model stability and fit.</p> 4092<section id="diagnostic-testing" class="level3"> 4093<h3 class="anchored" data-anchor-id="diagnostic-testing">Diagnostic Testing</h3> 4094<div class="cell"> 4095<details class="code-fold"> 4096<summary>Code</summary> 4097<div class="sourceCode cell-code" id="cb9"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="co"># residual diagnostics by simulating residuals to check for</span></span> 4098<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a><span class="co"># uniformity, outliers, non-linearity, and heterscedasticity</span></span> 4099<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a></span> 4100<span id="cb9-4"><a href="#cb9-4" aria-hidden="true" tabindex="-1"></a><span class="co"># simulate residuals</span></span> 4101<span id="cb9-5"><a href="#cb9-5" aria-hidden="true" tabindex="-1"></a>sim_res <span class="ot"><-</span> <span class="fu">simulateResiduals</span>(model_glmm)</span> 4102<span id="cb9-6"><a href="#cb9-6" aria-hidden="true" tabindex="-1"></a></span> 4103<span id="cb9-7"><a href="#cb9-7" aria-hidden="true" tabindex="-1"></a><span class="co"># plot simulated residuals</span></span> 4104<span id="cb9-8"><a href="#cb9-8" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(sim_res)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div> 4105</details> 4106<div class="cell-output-display"> 4107<div> 4108<figure class="figure"> 4109<p><img role="img" 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
4109QKsNnnNnQ6nCiy9Uf4CItN//8LmBlhjdO01wEpJKXKdAZbTZeXXreLLWflVrvzq95VC8cXy64U/KpQB9he3ygD71xsCrJ/E+Il/ARjArgiwzQSW3ih/AYHZV4AJsMYHL/8hFGBbviMWXTeRxvhJ8Qdg9/vL5gBsT4BtJrD0RvkLCMy+AkyANT54+Q+hANvyHbHouok0xplvQFy9MICrJMA2E1h6o/wFBGZfASbAGh+8/IdQgG35jlh03UQqw18AA9gXAbaZwNIb5S8gMPsKMAHW+ODlP4QCbMt3xKLrJlIZ9f66+q0BWEKAbSaw9Eb5CwjMvgJMgDU+ePkPoQDb8h2x6LqJrr4y9BfAzgiwzQSW3ih/AYHZV4AJsMYHL/8hFGBbviMWXTfRtWeG/gLYGwG2mcDSG+UvIDD7CjAB1vjg5T+EAmzLd8Si6ya69s7wAxAB9kaAbSaw9Eb5CwjMvgJMgDU+ePkPoQDb8h2x6LqJrjw06v113fsCsJAA20xg6Y3yFxCYfQWYAGt88PIfQgG25Tti0XUTXXdo+AGIAPsjwDYTWHqj/AUEZl8BJsAaH7z8h1CAbfmOWHTdRFedGvoLYIcE2GYCS2+Uv4DA7CvABFjjg5f/EAqwLd8Ri66b6JpbQ38B7JEA20xg6Y3yFxCYfQWYAGt88PIfQgG25Tti0XUTXXNs6C+APRJgmwksvVH+AgKzrwATYI0PXv5DKMC2fEcsum6iK64N/QWwS5UA++NCJcDKUeNLhXI0+fStWoAtnwiXCNzSXIEhN6D26ZW7rkRXKRJg/ZICbMnzkJMlA4f6+Plud9cDAdYYZuXXreLLWflVrvzqV35V/OqtWoD9WeHbt/628O4NAdbvenOj/gMQV68M4LoJsCUCtzRXYBAfmNUrdy3ABp6HwHEIMAE24x2x6LqJrjY39BfATgmwJQK3NFdgEB+Y1St3LcAGnofAcQgwATbjHbHouomutTf8AA6AvRJgSwRuaa7AID4wq1fuWoANPA+B4xBgAmzGO2LRdRNda3DoL4C9EmBLBG5prsAgPjCrV+5agA08D4HjEGACbMY7YtF1E11pcVT76zq3A2BPBNgSgVs64zQTAAAgAElEQVSaKzCID8zqlbsWYAPPQ+A4BJgAm/GOWHTdRNdZHPoLYL8E2BKBW5orMIgPzOqVuxZgA89D4DgEmACb8Y5YdN1EV5kc+gtgxwTYEoFbmiswiA/M6pW7FmADz0PgOASYAJvxjlh03UTX2Bz6C2DPBNgSgVuaKzCID8zqlbsWYAPPQ+A4BJgAm/GOWHTdRFcYHa9/AKL+AtgjAbZE4JbmCgziA7N65a4F2MDzEDgOASbAZrwjFl030fVVR72/rm4rAHZJgC0RuKW5AoP4wKxeuWsBNvA8BI5DgAmwGe+IRddNdH3V4RsQAfZNgC0RuKW5AoP4wKxeuWsBNvA8BI5DgAmwGe+IRddNdHXZob8Adk6ALRG4pbkCg/jArF65awE28DwEjkOACbAZ74hF1010bd2hvwD2ToAtEbiluQKD+MCsXrlrATbwPASOQ4AJsBnviEXXTXRl4aG/AHYvP8A+U8iPrqTRb08CY2i+wCA+MKtX7nRJgNWia1cBVtju9BoP9fHznXCrlTUeOcD+sFAJsL8pCLAEV1Ye1f66rm0A2DMBtlxk4k43d1av3KkAC9ju9BoP9fHznXCrlTUKsC3fEYuum+i6ysMPQATYPwG2XGTiTjd3Vq/cqQAL2O70Gg/18fOdcKuVNQqwLd8Ri66b6KrSQ38BHIAAWy4ycaebO6tX7lSABWx3eo2H+vj5TrjVyhoF2JbviEXXTXRN7eEvgAEcgQBbLjJxp5s7q1fuVIAFbHd6jYf6+PlOuNXKGgXYlu+IRddNdE3xob8AjkCALReZuNPNndUrdyrAArY7vcZDffx8J9xqZY0CbMt3xKLrJrqi+tBfAIcgwJaLTNzp5s7qlTsVYAHbnV7joT5+vhNutbJGAbblO2LRdRNdT374AYgAxyDAlotM3OnmzuqVOxVgAdudXuOhPn6+E261skYBtuU7YtF1E11NfugvgIMQYMtFJu50c2f1yp0KsIDtTq/xUB8/3wm3WlmjANvyHbHouomupj+qPwDxWjYA4CgE2HKRiTvd3Fm9cqcCLGC702s81MfPd8KtVtYowLZ8Ryy6bqJr6Y9qf61eGAD3CLDlIhN3urmzeuVOBVjAdqfXeKiPn++EW62sUYBt+Y5YdN1E11EgJ9+ACHAYAmy5yMSdbu6sXrlTARaw3ek1Hurj5zvhVitrFGBbviMWXTfRdRSI/gI4DgG2XGTiTjd3Vq/cqQAL2O70Gg/18fOdcKuVNQqwLd8Ri66b6CoSRH8BHEhjgBUzxW8WvlgYCLC5416Oyqwe0HihyCDeZrtZPbBhAixgV4daOd+5t1pZ49zo2lWA/eMNAdbvGhpEfwEciQBrJMACs3pgwwRYwK4OtXK+c2+1skYBtuU7YtF1E11BhOgvgEMRYI0EWGBWD2yYAAvY1aFWznfurVbWKMC2fEcsum6iy6+Qen9d/K0DHJIAayTAArN6YMMEWMCuDrVyvnNvtbJGAbblO2LRdRNdfIXc/ABE+QVwEAKskQALzOqBDRNgAbs61Mr5zr3VyhoF2JbviEXXTXTpHVLvrwu/cYDDEmCNBFhgVg9smAAL2NWhVs537q1W1ijAtnxHLLpuogvvkJv+8gdgAIchwBoJsMCsHtgwARawq0OtnO/cW62sUYBt+Y5YdN1Elx0i+gvgeARYIwEWmNUDGybAAnZ1qJXznXurlTUKsC3fEYuum+iiS0R/ARyQAGskwAKzemDDBFjArg61cr5zb7WyxqMG2OkkwFa45BQ5018XfNcARyfAGgmwwKwe2DABFrCrQ62c79xbraxRgG35jlh03UQXnCL6C+CQBFgjARaY1QMbJsACdnWolfOde6uVNR41wHwL4hqX2yInPwAR4JAEWCMBFpjVAxsmwAJ2daiV8517q5U1CrAt3xGLrpvoYmPkXH9d6j0DXAQB1kiABWb1wIYJsIBdHWrlfOfeamWNAmzLd8Si6ya61Bh53V/+AAzgYARYIwEWmNUDGybAAnZ1qJXznXurlTUKsC3fEYuum+hCa0R/ARzW6fRXr33zVhlg5fAQCLBiSvn0ZtGVFD2Nn94vZ12BUbkmf0Bv3Ly5AdYfXfmnlGSzsw584na3F1hvTmgFouvTxT9aKgPsy4WBAPvWLQGW7DJzRH8BHJcAG/j0fjnr6hzIg4Nt44DeuHkCrNFmZx34xO1uL7DewwXY6STANnaRPaK/AA5MgA18er+cdXUO5MHBtnFAb9w8AdZos7MOfOJ2txdY7wED7CTAtnWRQaK/AA5MgA18er+cdXUO5MHBtnFAb9w8AdZos7MOfOJ2txdY7+EC7A8F2NYusUhe/wGY/gI4IAE28On9ctbVOZAHB9vGAb1x8wRYo83OOvCJ291eYL3HCzB/B2xrF5gk+gvg0ATYwKf3y1lX50AeHGwbB/TGzRNgjTY768Anbnd7gfUKsNnviEXXTXSBTeIbEAEOTYANfHq/nHV1DuTBwbZxQG/cPAHWaLOzDnzidrcXWO/hAswP4djc5TWJ/gI4NgE28On9ctbVOZAHB9vGAb1x8wRYo83OOvCJ291eYL0CbPY7YtF1E11elOgvgGMTYAOf3i9nXZ0DeXCwbRzQGzdPgDXa7KwDn7jd7QXWe7gA8y2Im7u0Kjnz51+rlwZAiAAb+PR+OevqHMiDg23jgN64eQKs0WZnHfjE7W4vsF4BNvsdsei6iS6sS/QXwOEJsIFP75ezrs6BPDjYNg7ojZsnwBptdtaBT9zu9gLrFWCz3xGLrpvossJEfwEcnwAb+PR+OevqHMiDg23jgN64eQKs0WZnHfjE7W4vsN6jBVjxV8AE2EYuqUxuWkt/ARyZABv49H456+ocyIODbeOA3rh5AqzRZmcd+MTtbi+wXgE2+x2x6LqJLilN9BfAJcgJsHIcGQiwwJyWWUHhKTv/ov1rieif1RtFFlO51UAi5UfXkQOstNkBB8y9pf7oGqmuxwOs/EdOZYD9VuFrtyLfgvjtWwIs2eW0ye2ff70ZYKvXBkCcAFty0f61RGwwdocPTIBNsNkBB8y9JQEmwBJcTJzoL4ALIcCWXLR/LREbjN3hAxNgE2x2wAFzb+niAqz8BkQBtpVLqRP9BXApBNiSi/avJWKDsTt8YAJsgs0OOGDuLQkwAZbgUvJEfwFcCgG25KL9a4nYYOwOH5gAm2CzAw6Ye0sXF2C+BXGFC+kT/QVwMQTYkov2ryVig7E7fGACbILNDjhg7i0JMAGW4DIC5fYbEP0ARICjE2BLLtq/logNxu7wgQmwCTY74IC5tyTABFiCiwgU/QVwQQTYkov2ryVig7E7fGACbILNDjhg7i0JMAGW4BIKxQ/gALgkAmzJRfvXErHB2B0+MAE2wWYHHDD3lgSYAEtwCYmivwAuiQBbctH+tURsMHaHD0yATbDZAQfMvSUBJsASXECjnOmv498cwPURYEsu2r+WiA3G7vCBCbAJNjvggLm3JMAEWILjN4r+ArgsAmzJRfvXErHB2B0+MAE2wWYHHDD3lgSYAEtw+EjRXwAXRoAtuWj/WiI2GLvDBybAJtjsgAPm3tLFBZh/EfMKB6+U1631UH8d+9YArpUAW3LR/rVEbDB2hw9MgE2w2QEHzL0lASbAEhy7Us721+rFAdClMcB++1ZjgDVGV2bWHF/+YDt3yg7cUmMuia6KnPPd7tFovI+5AdYWXZ8pvsp9qVB+JQx8C+IfFQTYNg7dKUV/+QGIAJdCgB1B/mA7d8oO3JIAy5Fzvts9Go33IcAEWIJDh4r+ArhAAuwI8gfbuVN24JYEWI6c893u0Wi8DwEmwBIcuVT0F8AlEmBHkD/Yzp2yA7ckwHLknO92j0bjfQgwAZbgwKmivwAukgA7gvzBdu6UHbglAZYj53y3ezQa70OACbAEx20V/QVwmQTYEeQPtnOn7MAtCbAcOee73aPReB8CTIAlOG6snPkBiKuXBsAAAXYE+YPt3Ck7cEsCLEfO+W73aDTehwATYAkOWyv6C+BCCbAjyB9s507ZgVsSYDlyzne7R6PxPgSYAEtw1Fw58w2Iq5cGwBABdgT5g+3cKTtwSwIsR875bvdoNN6HABNgCQ7aK/oL4GIJsCPIH2znTtmBWxJgOXLOd7tHo/E+BJgAS3DMYPEDOAAulwA7gvzBdu6UHbglAZYj53y3ezQa70OACbAExwwW/QVwuQTYEeQPtnOn7MAtCbAcOee73aPReB8CTIAlOGSx6C+ACybAjiB/sJ07ZQduSYDlyDnf7R6NxvsQYAIswQGTxfcfAly00+mvX2sLsC8XagFWjENzA2xg4j6wpNm338DaG9Op39H2dMAGufWmuWtvDLD+6PqVx6PrnS/eqgVY8QXyt3/n1u8VGgPs3RsCrN/xmuV0G2Bv9NfhbgaANwiwA0uaffsNrF2A5dsgt940d+0CTIAlOFyznOuv1WsDIIEAO7Ck2bffwNoFWL4NcutNc9cuwARYgqNVS9FffgA9wEUSYAeWNPv2G1i7AMu3QW69ae7aBZgAS3C0bNFfAJdOgB1Y0uzbb2DtAizfBrn1prlrF2ACLMHBuqXeX6tXBkASAXZgSbNvv4G1C7B8G+TWm+auXYAJsATHKh
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" class="img-fluid figure-img" style="width:1728cm"></p> 4110</figure> 4111</div> 4112</div> 4113<details class="code-fold"> 4114<summary>Code</summary> 4115<div class="sourceCode cell-code" id="cb10"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a><span class="co"># check for zero inflation</span></span> 4116<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a><span class="fu">testZeroInflation</span>(sim_res)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div> 4117</details> 4118<div class="cell-output-display"> 4119<div> 4120<figure class="figure"> 4121<p><img role="img" 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4121aO5LFjGwuMdZp+bQfnv7Oaq/7z1KsQlgb8v95Kkd9gSw/y0felZ11CxV+OIANrnHgZfvuvrr/iqt5xkMYMV92x9KOuYLubMu7lxtlHXtMOHu5MSzvlXWOqu/Ldpv3r1boXdrl4mwNBTALmrHmM+7hzvPLreBsVrrJurt2fQCGBArAQxIXbaH2T50UQtg/1HuRf5X9R1FZ9dDAWxd2/V82ghgu53edlT7p+a+5G6Hs/hTq+bAfnv7OfZc2LvufHUvf9DDAzrsBrD2/nanVOlLA9j0Hgdevvp22VOl/TxDAeyfy+KtBHbEF7K5rPISHNsFVTkvK/+weFGLN3B/kfbbovPm3bcVerd2OwqNBLAqrK1b78lNsvx98ZjHRb1PtZjacuEzYECkBDAgefku5cnv/lQLYbsAtr3m2mW+v5odA8guCVCcpNgfwLLH5Hkuu0xbvlta7nJuL7eYndNV1CivB5d/s1FjD7baM93uWndq7t1v7zzHurho3OXLvRd4PF+V3392QIc9AezkxYfsi6m2q+2Umv5inNXqdz8DdkCPAy9ftuiT7z9sT0vsrdJ5nqEAtntsa6nHeyEbz1dGmItGjNu+DPkmKCJRe5X1vhpvi9qbd3gr9G/t886xv73qoav9EbbzWmrr/4VEze50S4DICGAA1Ve7Vh90qe2P7g42VH8q9lj7A9ju9/Kb2xsZ4qLaqyz2HdflDR/bl7+vDmlsT9Lq1Ny73955juosr/2XezivHnNAh90AVlRfb3vtlJpoNIAd0OPAy7e5b9FU8adOlc7zDAaw6rGtpR7vhexuotpGKU5K3Nz1u+2ftiGts8pK+22xO0A3shX6t3b7UNaAgQCWvYuyvJh/yqz+k95Nf+4AGBArAQzg+upd+VGZfP+vEcC2e4uNqxv07zL3fZdtI0PUDhNcFGfZlTesu/GmPJDRW3Pvfnv7OfLK7c8QtZxXO7uHddgKYE+HG5loNIAd0OPAy7fe7b2f91fpLGAogJV3PW8v9Xgv5E7Pj4qDYhf5J/G2HzUr0lJjlTvtt8W2j/GtsGdrH/ASDwSwWtRcN86Z7Nn0jS9nBoiLAAaQe//6292pT7sAVuzkdf40FsA+/fU/T1eNDFH/oEx+YKS2U965onZxSKO5h7qruW+/vfMc24/jnPzujwNXKzivxZTJHXYC2FgjE40FsEN6HHj5mlcz76nSXcBQACvv2v1Y0tFeyEq9j9p5mdnd1/mlMR5Wua+9yp3222JbdHwr7NnaB7zEQ6cgNhZZO6bW3fT5wTInIAKREsAAKtWpT18UwK7efVue09gKYHUP6tfx7lzQrTikUR2AaNUc2G9vPsfuczUne69DX7+I+eQOxwNYu5HS0BUaJgWwyT3uf/nqF0DPG+5U6S5g+DL0tT9WDy0uK3mkF7K15esbpbjmxuYnT7ffEbZ91s4qa0233hZVABvZCnu2dl+82/MSrycFsOa1STubPru4o/wFREsAA6hpfjTnRgGsdt24Roao357vzNb2iTsBbHsIo7q5XXPffnvnOa63F2DY2rOzW4WDQzocCWA9jZS+LIAd0uNgAKv26PNHdap0FzAxgOUhZbe0472QrS1f3yjFVec3z3K2PeR20fnUWuf1a74ttncd3wp7tnYr3t30Koh77tbZ9Flb951/CERLAAMS1/ouou1fvyCAbXe/7z36w5/+8awdwJoha+gIWLkfvbvyX6PmwH57q07m3avBBFYPYJM7HA9gPY3kvjiATe7xS46AdRdwsyNgR30hO31UW2dT+Gn2EbA8RJ0VF6EfOgJ23Xxb7DkCNnVrH3AEbOh7wJp32xvANu+R1ZOBc2oBAieAAYlbNz+2s/7SALauvrK2dRGOfddhqJWoKfajt8/RqTlw5lp/SPj051etAyk79VMQJ3c4fgrizS6RMBbADulx2mfA8kTQ9+mn7seObvAZsGO/kHsCWH6g7bx45MPPz6pr7TdW2VG9LfZ/BmzS1j7gM2C13FYPiG3r/Z8BW+/9VQJAHAQwIHHlxeK3in3C8QBWuz7cuv5VSrWdyovmKYjN6wo0b1h3wtHmhw/LfdVuzXKPtN1E33OUq9u7T7/7fNIBHY4EsE6pqcYC2CE9Drx86+5VEJtVugsYCmDlD3oO6RzphazpvUDiOv+q6e3HzR68LR7cXmXfWurnZI5vhT1b+4CrIHZ/c1FZ76p8blx7v7Hp1z7+BcROAAMSl+1S3q+uyL1u7BcPB7Bi5/byWX8Ay25vZIis9pvqaZ7WdmE/P+senVqvvvrvXRhp1dztt7ea6DzHbsd7PIAd0uFIAOuWmmgsgB3S48DLt/97wD7v25JDAax4FZphvlrRUV7I7iaqb53sqf+1ugL974s2Br8HrPG22B2+HdkK/Vt74MNcHVWFq5ethe0231XzS75aB/2+GvleBYDACWBA6vILDTz+NYtQ+deBTbsKYvlBlKv8W5xrpyCeb/Pc9iIH5SXIHxTfBbU6ebH506eftz/Jbrj/6/X1u7JEp63iOTs1yz3SThOd58g+jvMku9Dd++f7vrh2F8AO6HAsgHVKTdQTwLbPe4OtOJKfs+98yzdp2W+jSud5BgPY6vGb7avQ3cRHeiEbBcvcsvue5CzNbEvlz1e00V5lpf22qH2AcXgr9G/t8wNe4rJC9szNDZqtIX+y962LHDYP2Dn+BcROAAOS97ZxxYB813VCANtdofvfntUDWOMCBPm+4vZScmfNH+W70NUN/9K5CMd2j7rYj+7ULPdIO010n+N89/fyIa092OYpbRM7HAtg3VLTtANYtfFusBWnvXzFTzpV2s9T2+adQHSv8b0DTcd6IevLKpdZbZRthKsSank8qrPKqvnW26KWd4e3Qu/W7l5DZkitZOtrsusXXayvuRbA1vUtKIsBcRLAAD4+3+3R/ZDfMiGAXV8Ue4tPPzcC2PXPZanvi8/L5Hvg+Q7lRePS3xtvtzdkX5/b2YNd7/ajOzWrPdJ2E93n2D575v5PRf/7A9j0DkcDWLfUJOtWAKs23g224uDLd1m+6OX5p50qrecZCmAPi1DRe230I72Q9YJlA7uNksea8jvBdjmwvcqq+dbboupjbCv0bu0DPgKW+UtRshi22hatJrH8UbXM8tzEqu3t3QQwIEYCGMD19ftX32S7c4/+uLs+22gAu/7082ZH8tGP160Adv3u202te99/qK48d5Vdau5J/qfX3+Q/q74T+fLV6erkyYe+QwiNDxS1au72SFtN9D3H+1fZDu+jHz+U/Q8EsMkdjgewbqkp2gFst/EO34rDL99Vvk03m25vlebzDAawPDrce9G7pKO9kLWCZd7ZbZTsYNdZuQl3m6G1ylrzjbfFro+RrdC7tc/3nJy5T1ZhdfK4/ChXramrYvs017zrrnZITwADoiWAASRmbbf1qA64BvuRnB9yxt/t+/zMZeEBDiCAASTm/LDDFYyYP4B9PL3ZNf5vyfrAA2AAiRPAANJy9TKowyfxmz+AbSJPQK+hA2AAhxHAANKyDuroyQLcQQDbZJ5wXsSg0iBABAQwgKRc/Xv/pSK4qTsIYJvnDOasv4+nPlMIcBABDAC+wF0EsOv1HTxnr6uXAR2MA4iCAAYAADATAQwAAGAmAhgAAMBMBDAAAICZCGAAAAAzEcAAAABmIoABAADMRAADAACYiQAGAAAwEwEMAABgJgIYAADATAQwAACAmQhgAAAAMxHAAAAAZiKAAQAAzEQAAwAAmIkABgAAMBMBDAAAYCYCGAAAwEwEMAAAgJkIYAAAADMRwAAAAGYigAEAAMxEAAMAAJiJAAYAADATAQwAAGAmAhgAAMBMBDAAAICZCGAAAAAzEcAAAABmIoABAADMRAADAACYiQAGAAAwEwEMAABgJgIYAADATAQwAACAmQhgAAAAMxHAAAAAZiKAAQAAzEQAAwAAmIkABgAAMBMBDAAAYCYCGAAAwEz+H5ikkzZVTMRYAAAAAElFTkSuQmCC" class="img-fluid figure-img" style="width:1728cm"></p> 4122</figure> 4123</div> 4124</div> 4125<div class="cell-output cell-output-stdout"> 4126<pre><code> 4127 DHARMa zero-inflation test via comparison to expected zeros with 4128 simulation under H0 = fitted model 4129 4130data: simulationOutput 4131ratioObsSim = 0.99544, p-value = 0.512 4132alternative hypothesis: two.sided</code></pre> 4133</div> 4134<details class="code-fold"> 4135<summary>Code</summary> 4136<div class="sourceCode cell-code" id="cb12"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a><span class="co"># check for normality of random effects,</span></span> 4137<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a><span class="co">
4137# then plot a histogram of them</span></span> 4138<span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a>ranef_vals <span class="ot"><-</span> <span class="fu">ranef</span>(model_glmm)<span class="sc">$</span>cond<span class="sc">$</span>GEOID</span> 4139<span id="cb12-4"><a href="#cb12-4" aria-hidden="true" tabindex="-1"></a></span> 4140<span id="cb12-5"><a href="#cb12-5" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="fu">data.frame</span>(<span class="at">ranef =</span> ranef_vals[,<span class="dv">1</span>]), <span class="fu">aes</span>(<span class="at">x =</span> ranef)) <span class="sc">+</span></span> 4141<span id="cb12-6"><a href="#cb12-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_histogram</span>(<span class="at">bins =</span> <span class="dv">30</span>) <span class="sc">+</span></span> 4142<span id="cb12-7"><a href="#cb12-7" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">'Distribution of Random Intercepts (GEOID)'</span>,</span> 4143<span id="cb12-8"><a href="#cb12-8" aria-hidden="true" tabindex="-1"></a> <span class="at">x =</span> <span class="st">'Random Effects'</span>, <span class="at">y =</span> <span class="st">'Count'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div> 4144</details> 4145<div class="cell-output-display"> 4146<div> 4147<figure class="figure"> 4148<p><img role="img" src="data:image/png;base64,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
4148nf7vrRt37xX//MLwywRlJtkbuYAUbkdU9xvLfIXegGA+xP37//b/6/zwbYn/7tv/72z/zCAGsk1Ra5ixlgRF73FMd7i9yFbjHAfvl/+fgvPtta333/Lz/9109//cEvDLBGUm2Ru5gBRuR1T3G8t8hd6AYD7Kc21o+/89k/bfjX3/yVP/vgFz/5r/3jz1z8XwHgSa8ywNtu2VAAACAASURBVE5/kQDwBhlgAIUMMADodLsB9kt//MEvPvyvn/77fjyTVFvkLvYqA+z0F8l1XvcUx3uL3IXuNsCe/DtgBlgZqbbIXcwAI/K6pzjeW+QuZIARSLVF7mIGGJHXPcXx3iJ3odsOML8L4hsj1Ra5ixlgRF73FMd7i9yFbjzAPv8jvz77c8B++4P/0ACrI9UWuYsZYERe9xTHe4vchW48wD77/TZ+9K1P/unDD35hgDWSaovcxQwwIq97iuO9Re5CNx5gP/7O+1/+o49/8J33n/y/vj74hQHWSKotchczwIi87imO9xa5C914gH38g2+9/8RPft/5D35hgBWSaovcxQwwIq97iuO9Re5Ctx5gH//g9/5mcv3Wnz3xCwOsj1Rb5C5mgBF53VMc7y1yF7rRAHuh0181zyTVFrmLGWBEXvcUx3uL3IUMMAKptshdzAAj8rqnON5b5C5kgBFItUXuYgYYkdc9xfHeInchA4xAqi1yFzPAiLzuKY73FrkLGWAEUm2Ru5gBRuR1T3G8t8hdyAAjkGqL3MUMMCKve4rjvUXuQgYYgVRb5C5mgBF53VMc7y1yFzLACKTaIncxA4zI657ieG+Ru5ABRiDVFrmLGWBEXvcUx3uL3IUMMAKptshdzAAj8rqnON5b5C5kgBFItUXuYgYYkdc9xfHeInchA4xAqi1yFzPAiLzuKY73FrkLGWAEUm2Ru5gBRuR1T3G8t8hdyAAjkGqL3MUMMCKve4rjvUXuQgYYgVRb5C5mgBF53VMc7y1yFzLACKTaIncxA4zI657ieG+Ru5ABRiDVFrmLGWBEXvcUx3uL3IUMMAKptshdzAAj8rqnON5b5C5kgBFItUXuYgYYkdc9xfHeInchA4xAqi1yFzPAiLzuKY73FrkLGWAEUm2Ru5gBRuR1T3G8t8hdyAAjkGqL3MUMMCKve4rjvUXuQgYYgVRb5C5mgBF53VMc7y1yFzLACKTaIncxA4zI657ieG+Ru5ABRiDVFrmLGWBEXvcUx3uL3IUMMAKptshdzAAj8rqnON5b5C5kgBFItUXuYgYYkdc9xfHeInchA4xAqi1yFzPAiLzuKY73FrkLGWAEUm2Ru5gBRuR1T3G8t8hdyAAjkGqL3MUMMCKve4rjvUXuQgYYgVRb5C5mgBF53VMc7y1yFzLACKTaIncxA4zI657ieG+Ru5ABRiDVFrmLGWBEXvcUx3uL3IUMMAKptshdzAAj8rqnON5b5C5kgBFItUXuYgYYkdc9xfHeInchA4xAqi1yFzPAiLzuKY73FrkLGWAEUm2Ru5gBRuR1T3G8t8hdyAAjkGqL3MUMMCKve4rjvUXuQgYYgVRb5C5mgBF53VMc7y1yFzLACKTaIncxA4zI657ieG+Ru5ABRiDVFrmLGWBEXvcUx3uL3IUMMAKptshdzAAj8rqnON5b5C5kgBFItUXuYgYYkdc9xfHeInchA4xAqi1yFzPAiLzuKY73FrkLGWAEUm2Ru5gBRuR1T3G8t8hdyAAjkGqL3MUMMCKve4rjvUXuQgYYgVRb5C5mgBF53VMc7y1yFzLACKTaIncxA4zI657ieG+Ru5ABRiDVFrmLGWBEXvcUx3uL3IUMMAKptshdzAAj8rqnON5b5C5kgBFItUXuYgYYkdc9xfHeInchA4xAqi1yFzPAiLzuKY73FrkLGWAEUm2Ru5gBRuR1T3G8t8hdyAAjkGqL3MUMMCKve4rjvUXuQgYYgVRb5C5mgBF53VMc7y1yFzLACKTaIncxA4zI657ieG+Ru5ABRiDVFrmLGWBEXvcUx3uL3IUMMAKptshdzAAj8rqnON5b5C5kgBFItUXuYgYYkdc9xfHeInchA4xAqi1yFzPAiLzuKY73FrkLGWAEUm2Ru5gBRuR1T3G8t8hdyAAjkGqL3MUMMCKve4rjvUXuQgYYgVRb5C5mgBF53VMc7y1yFzLACKTaIncxA4zI657ieG+Ru5ABRiDVFrmLGWBEXvcUx3uL3IUMMAKptshdzAAj8rqnON5b5C5kgBFItUXuYgYYkdc9xfHeInchA4xAqi1yFzPAiLzuKY73FrkLGWAEUm2Ru5gBRuR1T3G8t8hdyAAjkGqL3MUMMCKve4rjvUXuQgYYgVRb5C5mgBF53VMc7y1yFzLACKTaIncxA4zI657ieG+Ru5ABRiDVFrmLGWBEXvcUx3uL3IUMMAKptshdzAAj8rqnON5b5C5kgBFItUXuYgYYkdc9xfHeInchA4xAqi1yFzPAiLzuKY73FrkLGWAEUm2Ru5gBRuR1T3G8t8hdyAAjkGqL3HfxKtPodZz+KLnO657ieG+Ru5ABRiDVFrnv4vRquqHTHyXXed1THO8tchcywAik2iL3XZxeTTd0+qPkOq97iuO9Re5CBhiBVFvkvovTq+mGTn+UXOd1T3G8t8hdyAAjkGqL3HdxejXd0OmPkuu87imO9xa5CxlgBFJtkfsuTq+mGzr9UXKd1z3F8d4idyEDjECqLXLfxenVdEOnP0qu87qnON5b5C5kgBFItUXuuzi9mm7o9EfJdV73FMd7i9yFDDACqbbIfRenV9MNnf4ouc7rnuJ4b5G7kAFGINUWue/i9Gq6odMfJdd53VMc7y1yFzLACKTaIvddnF5NN3T6o+Q6r3uK471F7kIGGIFUW+S+i9Or6YZOf5Rc53VPcby3yF3IACOQaovcd3F6Nd3Q6Y+S67zuKY73FrkLGWAEUm2R+y5Or6YbOv1Rcp3XPcXx3iJ3IQOMQKotct/F6dV0Q6c/Sq7zuqc43lvkLmSAEUi1Re67OL2abuj0R8l1XvcUx3uL3IUMMAKptsh9F6dX0w2d/ii5zuue4nhvkbuQAUYg1Ra57+L0arqh0x8l13ndUxzvLXIXMsAIpNoi912cXk03dPqj5Dqve4rjvUXuQgYYgVRb5L6L06vphk5/lFzndU9xvLfIXcgAI5Bqi9x3cXo13dDpj5LrvO4pjvcWuQsZYARSbZH7Lk6vphs6/VFyndc9xfHeInchA4xAqi1y38Xp1XRDpz9KrvO6pzjeW+QuZIARSLVF7rs4vZpu6PRHyXVe9xTHe4vchQwwAqm2yH0Xp1fTDZ3+KLnO657ieG+Ru5ABRiDVFrnv4vRquqHTHyXXed1THO8tchcywAik2iL3XZxeTTd0+qPkOq97iuO9Re5CBhiBVFvkvovTq+mGTn+UXOd1T3G8t8hdyAAjkGqL3HdxejXd0OmPkuu87imO9xa5CxlgBFJtkfsuTq+mGzr9UXKd1z3F8d4idyEDjECqLXLfxenVdEOnP0qu87qnON5b5C5kgBFItUXuuzi9mm7o9EfJdV73FMd7i9yFDDACqbbIfRenV9MNnf4ouc7rnuJ4b5G7kAFGINUWue/i9Gq6odMfJdd53VMc7y1yFzLACKTaIvddnF5NN3T6o+Q6r3uK471F7kIGGIFUW+S+i9Or6YZOf5Rc53VPcby3yF3IACOQaovcd3F6Nd3Q6Y+S67zuKY73FrkLGWAEUm2R+y5Or6YbOv1Rcp3XPcXx3iJ3IQOMQKotct/F6dV0Q6c/Sq7zuqc43lvkLmSAEUi1Re67OL2abuj0R8l1XvcUx3uL3IUMMAKptsh9F6dX0w2d/ii5zuue4nhvkbuQAUYg1Ra57+L0arqh0x8l13ndUxzvLXIXMsAIpNoi912cXk03dPqj5Dqve4rjvUXuQgYYgVRb5L6L06vphk5/lFzndU9xvLfIXcgAI5Bqi9x3cXo13dDpj5LrvO4pjvcWuQsZYARSbZH7Lk6vphs6/VFyndc9xfHeInchA4xAqi1y38Xp1XRDpz9KrvO6pzjeW+QuZIARSLVF7rs4vZpu6PRHyXVe9xTHe4vchQwwAqm2yH0Xp1fTDZ3+KLnO657ieG+Ru5ABRiDVFrnv4vRquqHTHyXXed1THO8tchcywAik2iL3XZxeTTd0+qPkOq97iuO9Re5CBhiBVFvkvovTq+mGTn+UXOd1T3G8t8hdyAAjkGqL3HdxejXd0OmPkuu87imO9xa5CxlgBFJtkfsuTq+mGzr9UXKd1z3F8d4idyEDjECqLXLfxenVdEOnP0qu87qnON5b5C5kgBFItUXuuzi9mm7o9EfJdV73FMd7i9yFDDACqbbIfRenV9MNnf4ouc7rnuJ4b5G7kAFGINUWue/i9Gq6odMfJdd53VMc7y1yFzLACKTaIvddnF5NN3T6o+Q6r3uK471F7kIGGIFUW+S+i9Or6YZOf5Rc53VPcby3yF3IACOQaovcd3F6Nd3Q6Y+S67zuKY73FrkLGWAEUm2R+y5Or6YbOv1Rcp3XPcXx3iJ3IQOMQKotct/F6dV0Q6c/Sq7zuqc43lvkLmSAEUi1Re67OL2abuj0R8l1XvcUx3uL3IUMMAKptsh9F6dX0w2d/ii5zuue4nhvkbuQAUYg1Ra57+L0arqh0x8l13ndUxzvLXIXMsAIpNoi912cXk03dPqj5Dqve4rjvUXuQgYYgVRb5L6L06vphk5/lFzndU9xvLfIXcgAI5Bqi9x3cXo13dDpj5LrvO4pjvcWuQsZYARSbZH7Lk6vphs6/VFyndc9xfHeInchA4xAqi1y38Xp1XRDpz9KrvO6pzjeW+QuZIARSLVF7rs4vZpu6PRHyXVe9xTHe4vchQwwAqm2yH0Xp1fTDZ3+KLnO657ieG+Ru5ABRiDVFrnv4vRquqHTHyXXed1THO8tchcywAik2iL3XZxeTTd0+qPkOq97iuO9Re5CBhiBVFvkvovTq+mGTn+UXOd1T3G8t8hdyAAjkGqL3HdxejXd0OmPkuu87imO9xa5CxlgBFJtkfsuTq+mGzr9UXKd1z3F8d4idyEDjECqLXLfxenVdEOnP0qu87qnON5b5C5kgBFItUXuuzi9mm7o9EfJdV73FMd7i9yFDDACqbbIfRenV9MNnf4ouc7rnuJ4b5G7kAFGINUWue/i9Gq6odMfJdd53VMc7y1yFzLACKTaIvddnF5NN3T6o+Q6r3uK471F7kIGGIFUW+S+i9Or6YZOf5Rc53VPcby3yF3IACOQaovcd3F6Nd3Q6Y+S67zuKY73FrkLGWAEUm2R+y5Or6YbOv1Rcp3XPcXx3iJ3IQOMQKotct/F6dV0Q6c/Sq7zuqc43lvkLmSAEUi1Re67OL2abuj0R8l1XvcUx3uL3IUMMAKptsh9F6dX0w2d/ii5zuue4nhvkbuQAUYg1Ra57+L0arqh0x8l13ndUxzvLXIXMsAIpNoi912cXk03dPqj5Dqve4rjvUXuQgYYgVRb5L6L06vphk5/lFzndU9xvLfIXcgAI5Bqi9x3cXo13dDpj5LrvO4pjvcWuQsZYARSbZH7Lk6vphs6/VFyndc9xfHeInchA4xAqi1y38Xp1XRDpz9KrvO6pzjeW+QuZIARSLVF7rs4vZpu6PRHyXVe9xTHe4vchQwwAqm2yH0Xp1fTDZ3+KLnO657ieG+Ru5ABRiDVFrnv4vRquqHTHyXXed1THO8tchcywAik2iL3XZxeTTd0+qPkOq97iuO9Re5CBhiBVFvkvovTq+mGTn+UXOd1T3G8t8hdyAAjkGqL3HdxejXd0OmPkuu87imO9xa5CxlgBFJtkfsuTq+mGzr9UXKd1z3F8d4idyEDjECqLXLfxenVdEOnP0qu87qnON5b5C5kgBFItUXuuzi9mm7o9EfJdV73FMd7i9yFDDACqbbIfRenV9MNnf4ouc7rnuJ4b5G7kAFGINUWue/i9Gq6odMfJdd53VMc7y1yFzLACKTaIvddnF5NN3T6o+Q6r3uK471F7kIGGIFUW+S+i9Or6YZOf5Rc53VPcby3yF3IACOQaovcd3F6Nd3Q6Y+S67zuKY73FrkLGWAEUm2R+y5Or6YbOv1Rcp3XPcXx3iJ3IQOMQKotct/F6dV0Q6c/Sq7zuqc43lvkLmSAEUi1Re67OL2abuj0R8l1XvcUx3uL3IUMMAKptsh9F6dX0w2d/ii5zuue4nhvkbuQAUYg1Ra57+L0arqh0x8l13ndUxzvLXIXMsAIpNoi912cXk03dPqj5Dqve4rjvUXuQgYYgVRb5L6L06vphk5/lFzndU9xvLfIXcgAI5Bqi9x3cXo13dDpj5LrvO4pjvcWuQsZYARSbZH7Lk6vphs6/VFyndc9xfHeInchA4xAqi1y38Xp1XRDpz9KrvO6pzjeW+QuZIARSLVF7rs4vZpu6PRHyXVe9xTHe4vchQwwAqm2yH0Xp1fTDZ3+KLnO657ieG+Ru9CZAQaw4vRquqHTHyUAvEH+Dhg/Taotct/F6dV0Q6c/Sq7zuqc43lvkLmSAEUi1Re67OL2abuj0R8l1XvcUx3uL3IUMMAKptsh9F6dX0w2d/ii5zuue4nhvkbuQAUYg1Ra57+L0arqh0x8l13ndUxzvLXIXMsAIpNoi912cXk03dPqj5Dqve4rjvUXuQgYYgVRb5L6L06vphk5/lFzndU9xvLfIXcgAI5Bqi9x3cXo13dDpj5LrvO4pjvcWuQsZYARSbZH7Lk6vphs6/VFyndc9xfHeInchA4xAqi1y38Xp1XRDpz9KrvO6pzjeW+QuZIARSLVF7rs4vZpu6PRHyXVe9xTHe4vchQwwAqm2yH0Xp1fTDZ3+KLnO657ieG+Ru5ABRiDVFrnv4vRquqHTHyXXed1THO8tchcywAik2iL3XZxeTTd0+qPkOq97iuO9Re5CBhiBVFvkvovTq+mGTn+UXOd1T3G8t8hdyAAjkGqL3HdxejXd0OmPkuu87imO9xa5CxlgBFJtkfsuTq+mGzr9UXKd1z3F8d4idyEDjECqLXLfxenVdEOnP0qu87qnON5b5C5kgBFItUXuuzi9mm7o9EfJdV73FMd7i9yFDDACqbbIfRenV9MNnf4ouc7rnuJ4b5G7kAFGINWWvdynB02b0724bu91T3O8t8hdyAAjkGrLXu7Tg6bN6V5ct/e6pzneW+QuZIARSLVlL/fpQdPmdC+u23vd0xzvLXIXMsAIpNqyl/v0oGlzuhfX7b3uaY73FrkLGWAEUm3Zy3160LQ53Yvr9l73NMd7i9yFDDACqbbs5T49aNqc7sV1e697muO9Re5CBhiBVFv2cp8eNG1O9+K6vdc9zfHeInchA4xAqi17uU8Pmjane3Hd3uue5nhvkbuQAUYg1Za93KcHTZvTvbhu73VPc7y3yF3IACOQaste7tODps3pXly397qnOd5b5C5kgBFItWUv9+lB0+Z0L67be93THO8tchcywAik2rKX+/SgaXO6F9ftve5pjvcWuQsZYARSbdnLfXrQtDndi+v2Xvc0x3uL3IUMMAKptuzlPj1o2pzuxXV7r3ua471F7kIGGIFUW/Zynx40bU734rq91z3N8d4idyEDjECqLXu5Tw+aNqd7cd3e657meG+Ru5ABRiDVlr3cpwdNm9O9uG7vdU9zvLfIXcgAI5Bqy17u04OmzeleXLf3uqc53lvkLmSAEUi1ZS/36UHT5nQvrtt73dMc7y1yFzLACKTaspf79KBpc7oX1+297mmO9xa5CxlgBFJt2ct9etC0Od2L6/Ze9zTHe4vchQwwAqm27OU+PWjanO7FdXuve5rjvUXuQgYYgVRb9nKfHjRtTvfiur3XPc3x3iJ3IQOMQKote7lPD5o2p3tx3d7rnuZ4b5G7kAFGINWWvdynB02b0724bu91T3O8t8hdyAAjkGrLXu7Tg6bN6V5ct/e6pzneW+QuZIARSLVlL/fpQdPmdC+u23vd0xzvLXIXMsAIpNqyl/v0oGlzuhfX7b3uaY73FrkLGWAEUm3Zy3160LQ53Yvr9l73NMd7i9yFDDACqbbs5T49aNqc7sV1e697muO9Re5CBhiBVFv2cp8eNG1O9+K6vdc9zfHeInchA4xAqi17uU8Pmjane3Hd3uue5nhvkbuQAUYg1Za93KcHTZvTvbhu73VPc7y3yF3IACOQaste7tODps3pXly397qnOd5b5C5kgBFIteWhcp/eGjzh9DcF1z3U6+beHO8tchcywAik2vJQuU9vDZ5w+puC6x7qdXNvjvcWuQsZYARSbXmo3Ke3Bk84/U3BdQ/1urk3x3uL3IUMMAKptjxU7tNbgyec/qbguod63dyb471F7kIGGIFUWx4q9+mtw
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" class="img-fluid figure-img" style="width:1728cm"></p> 4149</figure> 4150</div> 4151</div> 4152<details class="code-fold"> 4153<summary>Code</summary> 4154<div class="sourceCode cell-code" id="cb13"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="co"># approximate multicollinearity</span></span> 4155<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a>vif_check <span class="ot"><-</span> <span class="fu">lm</span>(</span> 4156<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a> is_distressed <span class="sc">~</span> tot_pop_density <span class="sc">+</span> pct_didnt_work_past_yr <span class="sc">+</span> lag_pct_didnt_work_past_yr <span class="sc">+</span></span> 4157<span id="cb13-4"><a href="#cb13-4" aria-hidden="true" tabindex="-1"></a> avg_rent <span class="sc">+</span> pct_divorced <span class="sc">+</span> pct_developed <span class="sc">+</span> <span class="fu">factor</span>(year_group),</span> 4158<span id="cb13-5"><a href="#cb13-5" aria-hidden="true" tabindex="-1"></a> <span class="at">data =</span> train_data</span> 4159<span id="cb13-6"><a href="#cb13-6" aria-hidden="true" tabindex="-1"></a>)</span> 4160<span id="cb13-7"><a href="#cb13-7" aria-hidden="true" tabindex="-1"></a></span> 4161<span id="cb13-8"><a href="#cb13-8" aria-hidden="true" tabindex="-1"></a><span class="fu">vif</span>(vif_check)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div> 4162</details> 4163<div class="cell-output cell-output-stdout"> 4164<pre><code> GVIF Df GVIF^(1/(2*Df)) 4165tot_pop_density 2.063527 1 1.436498 4166pct_didnt_work_past_yr 1.254943 1 1.120242 4167lag_pct_didnt_work_past_yr 1.174861 1 1.083910 4168avg_rent 1.189808 1 1.090783 4169pct_divorced 1.245533 1 1.116035 4170pct_developed 2.149328 1 1.466059 4171factor(year_group) 1.069657 2 1.016977</code></pre> 4172</div> 4173<details class="code-fold"> 4174<summary>Code</summary> 4175<div class="sourceCode cell-code" id="cb15"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a><span class="do">###</span></span> 4176<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a><span class="co"># Moran's I (spatial autocorrelation)</span></span> 4177<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a><span class="do">###</span></span> 4178<span id="cb15-4"><a href="#cb15-4" aria-hidden="true" tabindex="-1"></a></span> 4179<span id="cb15-5"><a href="#cb15-5" aria-hidden="true" tabindex="-1"></a><span class="co"># </span></span> 4180<span id="cb15-6"><a href="#cb15-6" aria-hidden="true" tabindex="-1"></a>train_bgs <span class="ot"><-</span> train_data <span class="sc">%>%</span> <span class="fu">distinct</span>(GEOID, <span class="at">.keep_all =</span> <span class="cn">TRUE</span>)</span> 4181<span id="cb15-7"><a href="#cb15-7" aria-hidden="true" tabindex="-1"></a>unique_train_geoids <span class="ot"><-</span> <span class="fu">unique</span>(train_bgs<span class="sc">$</span>GEOID)</span> 4182<span id="cb15-8"><a href="#cb15-8" aria-hidden="true" tabindex="-1"></a></span> 4183<span id="cb15-9"><a href="#cb15-9" aria-hidden="true" tabindex="-1"></a><span class="co"># extract residuals and attach GEOID, calculate avg residuals per block group,</span></span> 4184<span id="cb15-10"><a href="#cb15-10" aria-hidden="true" tabindex="-1"></a><span class="co"># then ensure order matches spatial weights matrix</span></span> 4185<span id="cb15-11"><a href="#cb15-11" aria-hidden="true" tabindex="-1"></a>avg_res <span class="ot"><-</span> <span class="fu">data.frame</span>(</span> 4186<span id="cb15-12"><a href="#cb15-12" aria-hidden="true" tabindex="-1"></a> <span class="at">GEOID =</span> train_data<span class="sc">$</span>GEOID,</span> 4187<span id="cb15-13"><a href="#cb15-13" aria-hidden="true" tabindex="-1"></a> <span class="at">residuals =</span> <span class="fu">residuals</span>(model_glmm)</span> 4188<span id="cb15-14"><a href="#cb15-14" aria-hidden="true" tabindex="-1"></a>) <span class="sc">%>%</span></span> 4189<span id="cb15-15"><a href="#cb15-15" aria-hidden="true" tabindex="-1"></a> <span class="fu">group_by</span>(GEOID) <span class="sc">%>%</span></span> 4190<span id="cb15-16"><a href="#cb15-16" aria-hidden="true" tabindex="-1"></a> <span class="fu">summarize</span>(<span class="at">mean_residual =</span> <span class="fu">mean</span>(residuals, <span class="at">na.rm =</span> <span class="cn">TRUE</span>)) <span class="sc">%>%</span></span> 4191<span id="cb15-17"><a href="#cb15-17" aria-hidden="true" tabindex="-1"></a> <span class="fu">arrange</span>(<span class="fu">match</span>(GEOID, unique_train_geoids))</span> 4192<span id="cb15-18"><a href="#cb15-18" aria-hidden="true" tabindex="-1"></a></span> 4193<span id="cb15-19"><a href="#cb15-19" aria-hidden="true" tabindex="-1"></a><span class="co"># get centroids of unique block groups</span></span> 4194<span id="cb15-20"><a href="#cb15-20" aria-hidden="true" tabindex="-1"></a>centroids_train <span class="ot"><-</span> <span class="fu">st_centroid</span>(train_bgs)</span> 4195<span id="cb15-21"><a href="#cb15-21" aria-hidden="true" tabindex="-1"></a>coords_train <span class="ot"><-</span> <span class="fu">st_coordinates</span>(centroids_train)[, <span class="dv">1</span><span class="sc">:</span><span class="dv">2</span>]</span> 4196<span id="cb15-22"><a href="#cb15-22" aria-hidden="true" tabindex="-1"></a></span> 4197<span id="cb15-23"><a href="#cb15-23" aria-hidden="true" tabindex="-1"></a><span class="co"># define k-nearest neighbors</span></span> 4198<span id="cb15-24"><a href="#cb15-24" aria-hidden="true" tabindex="-1"></a>knn_neighbors_train <span class="ot"><-</span> <span class="fu">knearneigh</span>(coords_train, <span class="at">k =</span> k)</span> 4199<span id="cb15-25"><a href="#cb15-25" aria-hidden="true" tabindex="-1"></a>nb_train <span class="ot"><-</span> <span class="fu">knn2nb</span>(knn_neighbors_train)</span> 4200<span id="cb15-26"><a href="#cb15-26" aria-hidden="true" tabindex="-1"></a></span> 4201<span id="cb15-27"><a href="#cb15-27" aria-hidden="true" tabindex="-1"></a><span class="co"># create spatial weights matrix</span></span> 4202<span id="cb15-28"><a href="#cb15-28" aria-hidden="true" tabindex="-1"></a>lw_train <span class="ot"><-</span> <span class="fu">nb2listw</span>(nb_train, <span class="at">style =</span> <span class="st">'W'</span>)</span> 4203<span id="cb15-29"><a href="#cb15-29" aria-hidden="true" tabindex="-1"></a></span> 4204<span id="cb15-30"><a href="#cb15-30" aria-hidden="true" tabindex="-1"></a><span class="co"># Moran's I test</span></span> 4205<span id="cb15-31"><a href="#cb15-31" aria-hidden="true" tabindex="-1"></a>moran_test <span class="ot"><-</span> <span class="fu">moran.test</span>(avg_res<span class="sc">$</span>mean_residual, lw_train)</span> 4206<span id="cb15-32"><a href="#cb15-32" aria-hidden="true" tabindex="-1"></a></span> 4207<span id="cb15-33"><a href="#cb15-33" aria-hidden="true" tabindex="-1"></a>moran <span class="ot"><-</span> <span class="fu">data.frame</span>(</span> 4208<span id="cb15-34"><a href="#cb15-34" aria-hidden="true" tabindex="-1"></a> <span class="at">Moran_I =</span> moran_test<span class="sc">$</span>estimate[[<span class="dv">1</span>]],</span> 4209<span id="cb15-35"><a href="#cb15-35" aria-hidden="true" tabindex="-1"></a> <span class="at">Moran_I_Std_Dev =</span> moran_test<span class="sc">$</span>statistic,</span> 4210<span id="cb15-36"><a href="#cb15-36" aria-hidden="true" tabindex="-1"></a> <span class="at">p_value =</span> moran_test<span class="sc">$</span>p.value</span> 4211<span id="cb15-37"><a href="#cb15-37" aria-hidden="true" tabindex="-1"></a>)</span> 4212<span id="cb15-38"><a href="#cb15-38" aria-hidden="true" tabindex="-1"></a></span> 4213<span id="cb15-39"><a href="#cb15-39" aria-hidden="true" tabindex="-1"></a>moran <span class="sc">%>%</span></span> 4214<span id="cb15-40"><a href="#cb15-40" aria-hidden="true" tabindex="-1"></a> <span class="fu">gt</span>() <span class="sc">%>%</span></span> 4215<span id="cb15-41"><a href="#cb15-41" aria-hidden="true" tabindex="-1"></a> <span class="fu">tab_header</span>(</span> 4216<span id="cb15-42"><a href="#cb15-42" aria-hidden="true" tabindex="-1"></a> <span class="at">title =</span> <span class="st">'Moran</span><span class="sc">\'</span><span class="st">s I Test Results'</span></span> 4217<span id="cb15-43"><a href="#cb15-43" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">%>%</span></span> 4218<span id="cb15-44"><a href="#cb15-44" aria-hidden="true" tabindex="-1"></a> <span class="co">#fmt_markdown(columns = c(CI)) %>%</span></span> 4219<span id="cb15-45"><a href="#cb15-45" aria-hidden="true" tabindex="-1"></a> <span class="fu">cols_label</span>(</span> 4220<span id="cb15-46"><a href="#cb15-46" aria-hidden="true" tabindex="-1"></a> <span class="at">Moran_I =</span> <span class="st">'Moran</span><span class="sc">\'</span><span class="st">s I'</span>,</span> 4221<span id="cb15-47"><a href="#cb15-47" aria-hidden="true" tabindex="-1"></a> <span class="at">Moran_I_Std_Dev =</span> <span class="st">'Std. Dev.'</span>,</span> 4222<span id="cb15-48"><a href="#cb15-48" aria-hidden="true" tabindex="-1"></a> <span class="at">p_value =</span> <span class="st">'p-value'</span></span> 4223<span id="cb15-49"><a href="#cb15-49" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">%>%</span></span> 4224<span id="cb15-50"><a href="#cb15-50" aria-hidden="true" tabindex="-1"></a> <span class="fu">tab_options</span>(</span> 4225<span id="cb15-51"><a href="#cb15-51" aria-hidden="true" tabindex="-1"></a> <span class="at">table.font.size =</span> <span class="st">'large'</span></span> 4226<span id="cb15-52"><a href="#cb15-52" aria-hidden="true" tabindex="-1"></a> )</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div> 4227</details> 4228<div class="cell-output-display"> 4229<div id="dqvsrskurp" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;"> 4230<style>#dqvsrskurp table { 4231font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji'; 4232-webkit-font-smoothing: antialiased; 4233-moz-osx-font-smoothing: grayscale; 4234} 4235#dqvsrskurp thead, #dqvsrskurp tbody, #dqvsrskurp tfoot, #dqvsrskurp tr, #dqvsrskurp td, #dqvsrskurp th { 4236border-style: none; 4237} 4238#dqvsrskurp p { 4239margin: 0; 4240padding: 0; 4241} 4242#dqvsrskurp .gt_table { 4243display: table; 4244border-collapse: collapse; 4245line-height: normal; 4246margin-left: auto; 4247margin-right: auto; 4248color: #333333; 4249font-size: large;
4250font-weight: normal; 4251font-style: normal; 4252background-color: #FFFFFF; 4253width: auto; 4254border-top-style: solid; 4255border-top-width: 2px; 4256border-top-color: #A8A8A8; 4257border-right-style: none; 4258border-right-width: 2px; 4259border-right-color: #D3D3D3; 4260border-bottom-style: solid; 4261border-bottom-width: 2px; 4262border-bottom-color: #A8A8A8; 4263border-left-style: none; 4264border-left-width: 2px; 4265border-left-color: #D3D3D3; 4266} 4267#dqvsrskurp .gt_caption { 4268padding-top: 4px; 4269padding-bottom: 4px; 4270} 4271#dqvsrskurp .gt_title { 4272color: #333333; 4273font-size: 125%; 4274font-weight: initial; 4275padding-top: 4px; 4276padding-bottom: 4px; 4277padding-left: 5px; 4278padding-right: 5px; 4279border-bottom-color: #FFFFFF; 4280border-bottom-width: 0; 4281} 4282#dqvsrskurp .gt_subtitle { 4283color: #333333; 4284font-size: 85%; 4285font-weight: initial; 4286padding-top: 3px; 4287padding-bottom: 5px; 4288padding-left: 5px; 4289padding-right: 5px; 4290border-top-color: #FFFFFF; 4291border-top-width: 0; 4292} 4293#dqvsrskurp .gt_heading { 4294background-color: #FFFFFF; 4295text-align: center; 4296border-bottom-color: #FFFFFF; 4297border-left-style: none; 4298border-left-width: 1px; 4299border-left-color: #D3D3D3; 4300border-right-style: none; 4301border-right-width: 1px; 4302border-right-color: #D3D3D3; 4303} 4304#dqvsrskurp .gt_bottom_border { 4305border-bottom-style: solid; 4306border-bottom-width: 2px; 4307border-bottom-color: #D3D3D3; 4308} 4309#dqvsrskurp .gt_col_headings { 4310border-top-style: solid; 4311border-top-width: 2px; 4312border-top-color: #D3D3D3; 4313border-bottom-style: solid; 4314border-bottom-width: 2px; 4315border-bottom-color: #D3D3D3; 4316border-left-style: none; 4317border-left-width: 1px; 4318border-left-color: #D3D3D3; 4319border-right-style: none; 4320border-right-width: 1px; 4321border-right-color: #D3D3D3; 4322} 4323#dqvsrskurp .gt_col_heading { 4324color: #333333; 4325background-color: #FFFFFF; 4326font-size: 100%; 4327font-weight: normal; 4328text-transform: inherit; 4329border-left-style: none; 4330border-left-width: 1px; 4331border-left-color: #D3D3D3; 4332border-right-style: none; 4333border-right-width: 1px; 4334border-right-color: #D3D3D3; 4335vertical-align: bottom; 4336padding-top: 5px; 4337padding-bottom: 6px; 4338padding-left: 5px; 4339padding-right: 5px; 4340overflow-x: hidden; 4341} 4342#dqvsrskurp .gt_column_spanner_outer { 4343color: #333333; 4344background-color: #FFFFFF; 4345font-size: 100%; 4346font-weight: normal; 4347text-transform: inherit; 4348padding-top: 0; 4349padding-bottom: 0; 4350padding-left: 4px; 4351padding-right: 4px; 4352} 4353#dqvsrskurp .gt_column_spanner_outer:first-child { 4354padding-left: 0; 4355} 4356#dqvsrskurp .gt_column_spanner_outer:last-child { 4357padding-right: 0; 4358} 4359#dqvsrskurp .gt_column_spanner { 4360border-bottom-style: solid; 4361border-bottom-width: 2px; 4362border-bottom-color: #D3D3D3; 4363vertical-align: bottom; 4364padding-top: 5px; 4365padding-bottom: 5px; 4366overflow-x: hidden; 4367display: inline-block; 4368width: 100%; 4369} 4370#dqvsrskurp .gt_spanner_row { 4371border-bottom-style: hidden; 4372} 4373#dqvsrskurp .gt_group_heading { 4374padding-top: 8px; 4375padding-bottom: 8px; 4376padding-left: 5px; 4377padding-right: 5px; 4378color: #333333; 4379background-color: #FFFFFF; 4380font-size: 100%; 4381font-weight: initial; 4382text-transform: inherit; 4383border-top-style: solid; 4384border-top-width: 2px; 4385border-top-color: #D3D3D3; 4386border-bottom-style: solid; 4387border-bottom-width: 2px; 4388border-bottom-color: #D3D3D3; 4389border-left-style: none; 4390border-left-width: 1px; 4391border-left-color: #D3D3D3; 4392border-right-style: none; 4393border-right-width: 1px; 4394border-right-color: #D3D3D3; 4395vertical-align: middle; 4396text-align: left; 4397} 4398#dqvsrskurp .gt_empty_group_heading { 4399padding: 0.5px; 4400color: #333333; 4401background-color: #FFFFFF; 4402font-size: 100%; 4403font-weight: initial; 4404border-top-style: solid; 4405border-top-width: 2px; 4406border-top-color: #D3D3D3; 4407border-bottom-style: solid; 4408border-bottom-width: 2px; 4409border-bottom-color: #D3D3D3; 4410vertical-align: middle; 4411} 4412#dqvsrskurp .gt_from_md > :first-child { 4413margin-top: 0; 4414} 4415#dqvsrskurp .gt_from_md > :last-child { 4416margin-bottom: 0; 4417} 4418#dqvsrskurp .gt_row { 4419padding-top: 8px; 4420padding-bottom: 8px; 4421padding-left: 5px; 4422padding-right: 5px; 4423margin: 10px; 4424border-top-style: solid; 4425border-top-width: 1px; 4426border-top-color: #D3D3D3; 4427border-left-style: none; 4428border-left-width: 1px; 4429border-left-color: #D3D3D3; 4430border-right-style: none; 4431border-right-width: 1px; 4432border-right-color: #D3D3D3; 4433vertical-align: middle; 4434overflow-x: hidden; 4435} 4436#dqvsrskurp .gt_stub { 4437color: #333333; 4438background-color: #FFFFFF; 4439font-size: 100%;
4440font-weight: initial; 4441text-transform: inherit; 4442border-right-style: solid; 4443border-right-width: 2px; 4444border-right-color: #D3D3D3; 4445padding-left: 5px; 4446padding-right: 5px; 4447} 4448#dqvsrskurp .gt_stub_row_group { 4449color: #333333; 4450background-color: #FFFFFF; 4451font-size: 100%; 4452font-weight: initial; 4453text-transform: inherit; 4454border-right-style: solid; 4455border-right-width: 2px; 4456border-right-color: #D3D3D3; 4457padding-left: 5px; 4458padding-right: 5px; 4459vertical-align: top; 4460} 4461#dqvsrskurp .gt_row_group_first td { 4462border-top-width: 2px; 4463} 4464#dqvsrskurp .gt_row_group_first th { 4465border-top-width: 2px; 4466} 4467#dqvsrskurp .gt_summary_row { 4468color: #333333; 4469background-color: #FFFFFF; 4470text-transform: inherit; 4471padding-top: 8px; 4472padding-bottom: 8px; 4473padding-left: 5px; 4474padding-right: 5px; 4475} 4476#dqvsrskurp .gt_first_summary_row { 4477border-top-style: solid; 4478border-top-color: #D3D3D3; 4479} 4480#dqvsrskurp .gt_first_summary_row.thick { 4481border-top-width: 2px; 4482} 4483#dqvsrskurp .gt_last_summary_row { 4484padding-top: 8px; 4485padding-bottom: 8px; 4486padding-left: 5px; 4487padding-right: 5px; 4488border-bottom-style: solid; 4489border-bottom-width: 2px; 4490border-bottom-color: #D3D3D3; 4491} 4492#dqvsrskurp .gt_grand_summary_row { 4493color: #333333; 4494background-color: #FFFFFF; 4495text-transform: inherit; 4496padding-top: 8px; 4497padding-bottom: 8px; 4498padding-left: 5px; 4499padding-right: 5px; 4500} 4501#dqvsrskurp .gt_first_grand_summary_row { 4502padding-top: 8px; 4503padding-bottom: 8px; 4504padding-left: 5px; 4505padding-right: 5px; 4506border-top-style: double; 4507border-top-width: 6px; 4508border-top-color: #D3D3D3; 4509} 4510#dqvsrskurp .gt_last_grand_summary_row_top { 4511padding-top: 8px; 4512padding-bottom: 8px; 4513padding-left: 5px; 4514padding-right: 5px; 4515border-bottom-style: double; 4516border-bottom-width: 6px; 4517border-bottom-color: #D3D3D3; 4518} 4519#dqvsrskurp .gt_striped { 4520background-color: rgba(128, 128, 128, 0.05); 4521} 4522#dqvsrskurp .gt_table_body { 4523border-top-style: solid; 4524border-top-width: 2px; 4525border-top-color: #D3D3D3; 4526border-bottom-style: solid; 4527border-bottom-width: 2px; 4528border-bottom-color: #D3D3D3; 4529} 4530#dqvsrskurp .gt_footnotes { 4531color: #333333; 4532background-color: #FFFFFF; 4533border-bottom-style: none; 4534border-bottom-width: 2px; 4535border-bottom-color: #D3D3D3; 4536border-left-style: none; 4537border-left-width: 2px; 4538border-left-color: #D3D3D3; 4539border-right-style: none; 4540border-right-width: 2px; 4541border-right-color: #D3D3D3; 4542} 4543#dqvsrskurp .gt_footnote { 4544margin: 0px; 4545font-size: 90%; 4546padding-top: 4px; 4547padding-bottom: 4px; 4548padding-left: 5px; 4549padding-right: 5px; 4550} 4551#dqvsrskurp .gt_sourcenotes { 4552color: #333333; 4553background-color: #FFFFFF; 4554border-bottom-style: none; 4555border-bottom-width: 2px; 4556border-bottom-color: #D3D3D3; 4557border-left-style: none; 4558border-left-width: 2px; 4559border-left-color: #D3D3D3; 4560border-right-style: none; 4561border-right-width: 2px; 4562border-right-color: #D3D3D3; 4563} 4564#dqvsrskurp .gt_sourcenote { 4565font-size: 90%; 4566padding-top: 4px; 4567padding-bottom: 4px; 4568padding-left: 5px; 4569padding-right: 5px; 4570} 4571#dqvsrskurp .gt_left { 4572text-align: left; 4573} 4574#dqvsrskurp .gt_center { 4575text-align: center; 4576} 4577#dqvsrskurp .gt_right { 4578text-align: right; 4579font-variant-numeric: tabular-nums; 4580} 4581#dqvsrskurp .gt_font_normal { 4582font-weight: normal; 4583} 4584#dqvsrskurp .gt_font_bold {
4585font-weight: bold; 4586} 4587#dqvsrskurp .gt_font_italic { 4588font-style: italic; 4589} 4590#dqvsrskurp .gt_super { 4591font-size: 65%; 4592} 4593#dqvsrskurp .gt_footnote_marks { 4594font-size: 75%; 4595vertical-align: 0.4em; 4596position: initial; 4597} 4598#dqvsrskurp .gt_asterisk { 4599font-size: 100%; 4600vertical-align: 0; 4601} 4602#dqvsrskurp .gt_indent_1 { 4603text-indent: 5px; 4604} 4605#dqvsrskurp .gt_indent_2 { 4606text-indent: 10px; 4607} 4608#dqvsrskurp .gt_indent_3 { 4609text-indent: 15px; 4610} 4611#dqvsrskurp .gt_indent_4 { 4612text-indent: 20px; 4613} 4614#dqvsrskurp .gt_indent_5 { 4615text-indent: 25px; 4616} 4617#dqvsrskurp .katex-display { 4618display: inline-flex !important; 4619margin-bottom: 0.75em !important; 4620} 4621#dqvsrskurp div.Reactable > div.rt-table > div.rt-thead > div.rt-tr.rt-tr-group-header > div.rt-th-group:after { 4622height: 0px !important; 4623} 4624</style> 4625 4626<table class="gt_table caption-top table table-sm table-striped small" data-quarto-postprocess="true" data-quarto-disable-processing="false" data-quarto-bootstrap="false"> 4627<thead> 4628<tr class="header gt_heading"> 4629<th colspan="3" class="gt_heading gt_title gt_font_normal gt_bottom_border">Moran's I Test Results</th> 4630</tr> 4631<tr class="odd gt_col_headings"> 4632<th id="Moran_I" class="gt_col_heading gt_columns_bottom_border gt_right" data-quarto-table-cell-role="th" scope="col">Moran's I</th> 4633<th id="Moran_I_Std_Dev" class="gt_col_heading gt_columns_bottom_border gt_right" data-quarto-table-cell-role="th" scope="col">Std. Dev.</th> 4634<th id="p_value" class="gt_col_heading gt_columns_bottom_border gt_right" data-quarto-table-cell-role="th" scope="col">p-value</th> 4635</tr> 4636</thead> 4637<tbody class="gt_table_body"> 4638<tr class="odd"> 4639<td class="gt_row gt_right" headers="Moran_I">0.08849177</td> 4640<td class="gt_row gt_right" headers="Moran_I_Std_Dev">4.827659</td> 4641<td class="gt_row gt_right" headers="p_value">6.907385e-07</td> 4642</tr> 4643</tbody> 4644</table> 4645 4646</div> 4647</div> 4648</div> 4649<p>The KS test result for uniformity showed no significant deviation from uniformity (p = 0.547), which suggests expected well-behaved residuals. The dispersion test showed no significant over- or under-dispersion (p = 0.08), signalling appropriate levels of variance. The outlier test showed no significant outliers (p = 0.645). The zero inflation test showed no evidence of zero inflation (p = 0.512). The QQ plot indicates appropriate residual distribution, and the Residuals vs. Predicted plot shows a slight curve at higher predictions, but is generally fairly flat otherwise. All VIF values used to measure multicollinearity are less than 5, meaning there is no concerning multicollinearity between the predictors.</p> 4650<p>While significant spatial autocorrelation is present in the residuals (Moranâs I = 0.088, p < 0.001), its magnitude is modest and expected given the spatial clustering of socioeconomic distress. In precursor models, Moranâs I reached upwards of 0.15. The inclusion of a lagged variable and block group-level random effects substantially reduced spatial dependence compared to these initial models.</p> 4651<p>Overall, the model fits well, the random effects have a reasonable distribution, and multicollinearity is not a concern. While spatial autocorrelation is present, it is moderate and characteristic of socioeconomic spatial data. There were also attempts at limiting spatial autocorrelation in the form of introducing a spatially lagged covariate, which alleviated some of the autocorrelation. Remaining autocorrelation likely reflects unmeasured spatial processes beyond the scope of this analysis.</p> 4652</section> 4653<section id="fixed-effects" class="level3"> 4654<h3 class="anchored" data-anchor-id="fixed-effects">Fixed Effects</h3> 4655<div class="cell"> 4656<details class="code-fold"> 4657<summary>Code</summary> 4658<div class="sourceCode cell-code" id="cb16"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a><span class="co"># extract fixed effects</span></span> 4659<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a>fixef_vals <span class="ot"><-</span> <span class="fu">fixef</span>(model_glmm)<span class="sc">$</span>cond</span> 4660<span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a>odds_ratios <span class="ot"><-</span> <span class="fu">exp</span>(fixef_vals)</span> 4661<span id="cb16-4"><a href="#cb16-4" aria-hidden="true" tabindex="-1"></a></span> 4662<span id="cb16-5"><a href="#cb16-5" aria-hidden="true" tabindex="-1"></a><span class="co"># get standard errors</span></span> 4663<span id="cb16-6"><a href="#cb16-6" aria-hidden="true" tabindex="-1"></a>se_vals <span class="ot"><-</span> <span class="fu">summary</span>(model_glmm)<span class="sc">$</span>coefficients<span class="sc">$</span>cond[, <span class="st">'Std. Error'</span>]</span> 4664<span id="cb16-7"><a href="#cb16-7" aria-hidden="true" tabindex="-1"></a></span> 4665<span id="cb16-8"><a href="#cb16-8" aria-hidden="true" tabindex="-1"></a><span class="co"># compute 95% confidence interval</span></span> 4666<span id="cb16-9"><a href="#cb16-9" aria-hidden="true" tabindex="-1"></a>lower_ci <span class="ot"><-</span> <span class="fu">exp</span>(fixef_vals <span class="sc">-</span> <span class="fl">1.96</span> <span class="sc">*</span> se_vals)</span> 4667<span id="cb16-10"><a href="#cb16-10" aria-hidden="true" tabindex="-1"></a>upper_ci <span class="ot"><-</span> <span class="fu">exp</span>(fixef_vals <span class="sc">+</span> <span class="fl">1.96</span> <span class="sc">*</span> se_vals)</span> 4668<span id="cb16-11"><a href="#cb16-11" aria-hidden="true" tabindex="-1"></a></span> 4669<span id="cb16-12"><a href="#cb16-12" aria-hidden="true" tabindex="-1"></a><span class="co"># get fixed effects data</span></span> 4670<span id="cb16-13"><a href="#cb16-13" aria-hidden="true" tabindex="-1"></a>fixed_effects <span class="ot"><-</span></span> 4671<span id="cb16-14"><a href="#cb16-14" aria-hidden="true" tabindex="-1"></a> <span class="fu">data.frame</span>(</span> 4672<span id="cb16-15"><a href="#cb16-15" aria-hidden="true" tabindex="-1"></a> <span class="at">Estimate =</span> fixef_vals,</span> 4673<span id="cb16-16"><a href="#cb16-16" aria-hidden="true" tabindex="-1"></a> <span class="at">Odds_Ratio =</span> odds_ratios,</span> 4674<span id="cb16-17"><a href="#cb16-17" aria-hidden="true" tabindex="-1"></a> <span class="at">CI_Lower =</span> lower_ci,</span> 4675<span id="cb16-18"><a href="#cb16-18" aria-hidden="true" tabindex="-1"></a> <span class="at">CI_Upper =</span> upper_ci,</span> 4676<span id="cb16-19"><a href="#cb16-19" aria-hidden="true" tabindex="-1"></a> <span class="at">p_value =</span> <span class="fu">summary</span>(model_glmm)<span class="sc">$</span>coefficients<span class="sc">$</span>cond[, <span class="st">'Pr(>|z|)'</span>]</span> 4677<span id="cb16-20"><a href="#cb16-20" aria-hidden="true" tabindex="-1"></a> )</span> 4678<span id="cb16-21"><a href="#cb16-21" aria-hidden="true" tabindex="-1"></a></span> 4679<span id="cb16-22"><a href="#cb16-22" aria-hidden="true" tabindex="-1"></a><span class="co"># odds ratio table</span></span> 4680<span id="cb16-23"><a href="#cb16-23" aria-hidden="true" tabindex="-1"></a>
4680odds_table <span class="ot"><-</span></span> 4681<span id="cb16-24"><a href="#cb16-24" aria-hidden="true" tabindex="-1"></a> fixed_effects <span class="sc">%>%</span></span> 4682<span id="cb16-25"><a href="#cb16-25" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(</span> 4683<span id="cb16-26"><a href="#cb16-26" aria-hidden="true" tabindex="-1"></a> <span class="at">Predictor =</span> <span class="fu">rownames</span>(.),</span> 4684<span id="cb16-27"><a href="#cb16-27" aria-hidden="true" tabindex="-1"></a> <span class="at">Estimate =</span> <span class="fu">round</span>(Estimate, <span class="dv">3</span>),</span> 4685<span id="cb16-28"><a href="#cb16-28" aria-hidden="true" tabindex="-1"></a> <span class="at">Odds_Ratio =</span> <span class="fu">round</span>(Odds_Ratio, <span class="dv">2</span>),</span> 4686<span id="cb16-29"><a href="#cb16-29" aria-hidden="true" tabindex="-1"></a> <span class="at">CI =</span> <span class="fu">paste0</span>(<span class="st">'['</span>, <span class="fu">round</span>(CI_Lower, <span class="dv">2</span>), <span class="st">', '</span>, <span class="fu">round</span>(CI_Upper, <span class="dv">2</span>), <span class="st">']'</span>),</span> 4687<span id="cb16-30"><a href="#cb16-30" aria-hidden="true" tabindex="-1"></a> <span class="at">p_value =</span> <span class="fu">formatC</span>(p_value, <span class="at">format =</span> <span class="st">'e'</span>, <span class="at">digits =</span> <span class="dv">2</span>)</span> 4688<span id="cb16-31"><a href="#cb16-31" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">%>%</span></span> 4689<span id="cb16-32"><a href="#cb16-32" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(Predictor, Estimate, Odds_Ratio, CI, p_value) <span class="sc">%>%</span></span> 4690<span id="cb16-33"><a href="#cb16-33" aria-hidden="true" tabindex="-1"></a> <span class="fu">gt</span>() <span class="sc">%>%</span></span> 4691<span id="cb16-34"><a href="#cb16-34" aria-hidden="true" tabindex="-1"></a> <span class="fu">tab_header</span>(</span> 4692<span id="cb16-35"><a href="#cb16-35" aria-hidden="true" tabindex="-1"></a> <span class="at">title =</span> <span class="st">'Odds Ratios from Logit Mixed-Effects Model'</span>,</span> 4693<span id="cb16-36"><a href="#cb16-36" aria-hidden="true" tabindex="-1"></a> <span class="at">subtitle =</span> <span class="st">'95% Confidence Intervals and p-values'</span></span> 4694<span id="cb16-37"><a href="#cb16-37" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">%>%</span></span> 4695<span id="cb16-38"><a href="#cb16-38" aria-hidden="true" tabindex="-1"></a> <span class="fu">fmt_markdown</span>(<span class="at">columns =</span> <span class="fu">c</span>(CI)) <span class="sc">%>%</span></span> 4696<span id="cb16-39"><a href="#cb16-39" aria-hidden="true" tabindex="-1"></a> <span class="fu">cols_label</span>(</span> 4697<span id="cb16-40"><a href="#cb16-40" aria-hidden="true" tabindex="-1"></a> <span class="at">Predictor =</span> <span class="st">'Predictor'</span>,</span> 4698<span id="cb16-41"><a href="#cb16-41" aria-hidden="true" tabindex="-1"></a> <span class="at">Odds_Ratio =</span> <span class="st">'Odds Ratio'</span>,</span> 4699<span id="cb16-42"><a href="#cb16-42" aria-hidden="true" tabindex="-1"></a> <span class="at">CI =</span> <span class="st">'95% CI'</span>,</span> 4700<span id="cb16-43"><a href="#cb16-43" aria-hidden="true" tabindex="-1"></a> <span class="at">p_value =</span> <span class="st">'p_value'</span></span> 4701<span id="cb16-44"><a href="#cb16-44" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">%>%</span></span> 4702<span id="cb16-45"><a href="#cb16-45" aria-hidden="true" tabindex="-1"></a> <span class="fu">tab_options</span>(</span> 4703<span id="cb16-46"><a href="#cb16-46" aria-hidden="true" tabindex="-1"></a> <span class="at">table.font.size =</span> <span class="st">'small'</span>,</span> 4704<span id="cb16-47"><a href="#cb16-47" aria-hidden="true" tabindex="-1"></a> <span class="at">heading.align =</span> <span class="st">'left'</span></span> 4705<span id="cb16-48"><a href="#cb16-48" aria-hidden="true" tabindex="-1"></a> )</span> 4706<span id="cb16-49"><a href="#cb16-49" aria-hidden="true" tabindex="-1"></a></span> 4707<span id="cb16-50"><a href="#cb16-50" aria-hidden="true" tabindex="-1"></a>odds_table</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div> 4708</details> 4709<div class="cell-output-display"> 4710<div id="fxbvwksuxk" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;"> 4711<style>#fxbvwksuxk table { 4712font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji'; 4713-webkit-font-smoothing: antialiased; 4714-moz-osx-font-smoothing: grayscale; 4715} 4716#fxbvwksuxk thead, #fxbvwksuxk tbody, #fxbvwksuxk tfoot, #fxbvwksuxk tr, #fxbvwksuxk td, #fxbvwksuxk th { 4717border-style: none; 4718} 4719#fxbvwksuxk p { 4720margin: 0; 4721padding: 0; 4722} 4723#fxbvwksuxk .gt_table { 4724display: table; 4725border-collapse: collapse; 4726line-height: normal; 4727margin-left: auto; 4728margin-right: auto; 4729color: #333333; 4730font-size: small;
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5066font-weight: bold; 5067} 5068#fxbvwksuxk .gt_font_italic { 5069font-style: italic; 5070} 5071#fxbvwksuxk .gt_super { 5072font-size: 65%; 5073} 5074#fxbvwksuxk .gt_footnote_marks { 5075font-size: 75%; 5076vertical-align: 0.4em; 5077position: initial; 5078} 5079#fxbvwksuxk .gt_asterisk { 5080font-size: 100%; 5081vertical-align: 0; 5082} 5083#fxbvwksuxk .gt_indent_1 { 5084text-indent: 5px; 5085} 5086#fxbvwksuxk .gt_indent_2 { 5087text-indent: 10px; 5088} 5089#fxbvwksuxk .gt_indent_3 { 5090text-indent: 15px; 5091} 5092#fxbvwksuxk .gt_indent_4 { 5093text-indent: 20px; 5094} 5095#fxbvwksuxk .gt_indent_5 { 5096text-indent: 25px; 5097} 5098#fxbvwksuxk .katex-display { 5099display: inline-flex !important; 5100margin-bottom: 0.75em !important; 5101} 5102#fxbvwksuxk div.Reactable > div.rt-table > div.rt-thead > div.rt-tr.rt-tr-group-header > div.rt-th-group:after { 5103height: 0px !important; 5104} 5105</style> 5106 5107<table class="gt_table caption-top table table-sm table-striped small" data-quarto-postprocess="true" data-quarto-disable-processing="false" data-quarto-bootstrap="false"> 5108<thead> 5109<tr class="header gt_heading"> 5110<th colspan="5" class="gt_heading gt_title gt_font_normal">Odds Ratios from Logit Mixed-Effects Model</th> 5111</tr> 5112<tr class="odd gt_heading"> 5113<th colspan="5" class="gt_heading gt_subtitle gt_font_normal gt_bottom_border">95% Confidence Intervals and p-values</th> 5114</tr> 5115<tr class="header gt_col_headings"> 5116<th id="Predictor" class="gt_col_heading gt_columns_bottom_border gt_left" data-quarto-table-cell-role="th" scope="col">Predictor</th> 5117<th id="Estimate" class="gt_col_heading gt_columns_bottom_border gt_right" data-quarto-table-cell-role="th" scope="col">Estimate</th> 5118<th id="Odds_Ratio" class="gt_col_heading gt_columns_bottom_border gt_right" data-quarto-table-cell-role="th" scope="col">Odds Ratio</th> 5119<th id="CI" class="gt_col_heading gt_columns_bottom_border gt_left" data-quarto-table-cell-role="th" scope="col">95% CI</th> 5120<th id="p_value" class="gt_col_heading gt_columns_bottom_border gt_left" data-quarto-table-cell-role="th" scope="col">p_value</th> 5121</tr> 5122</thead> 5123<tbody class="gt_table_body"> 5124<tr class="odd"> 5125<td class="gt_row gt_left" headers="Predictor">(Intercept)</td> 5126<td class="gt_row gt_right" headers="Estimate">-5.444</td> 5127<td class="gt_row gt_right" headers="Odds_Ratio">0.00</td> 5128<td class="gt_row gt_left" headers="CI">[0, 0.01]</td> 5129<td class="gt_row gt_left" headers="p_value">4.37e-91</td> 5130</tr> 5131<tr class="even"> 5132<td class="gt_row gt_left" headers="Predictor">tot_pop_density</td> 5133<td class="gt_row gt_right" headers="Estimate">0.440</td> 5134<td class="gt_row gt_right" headers="Odds_Ratio">1.55</td> 5135<td class="gt_row gt_left" headers="CI">[1.28, 1.89]</td> 5136<td class="gt_row gt_left" headers="p_value">1.19e-05</td> 5137</tr> 5138<tr class="odd"> 5139<td class="gt_row gt_left" headers="Predictor">pct_didnt_work_past_yr</td> 5140<td class="gt_row gt_right" headers="Estimate">1.108</td> 5141<td class="gt_row gt_right" headers="Odds_Ratio">3.03</td> 5142<td class="gt_row gt_left" headers="CI">[2.62, 3.49]</td> 5143<td class="gt_row gt_left" headers="p_value">1.71e-51</td> 5144</tr> 5145<tr class="even"> 5146<td class="gt_row gt_left" headers="Predictor">lag_pct_didnt_work_past_yr</td> 5147<td class="gt_row gt_right" headers="Estimate">0.954</td> 5148<td class="gt_row gt_right" headers="Odds_Ratio">2.60</td> 5149<td class="gt_row gt_left" headers="CI">[1.46, 4.61]</td> 5150<td class="gt_row gt_left" headers="p_value">1.12e-03</td> 5151</tr> 5152<tr class="odd"> 5153<td class="gt_row gt_left" headers="Predictor">avg_rent</td> 5154<td class="gt_row gt_right" headers="Estimate">-0.001</td> 5155<td class="gt_row gt_right" headers="Odds_Ratio">1.00</td> 5156<td class="gt_row gt_left" headers="CI">[1, 1]</td> 5157<td class="gt_row gt_left" headers="p_value">1.60e-03</td> 5158</tr> 5159<tr class="even"> 5160<td class="gt_row gt_left" headers="Predictor">pct_divorced</td> 5161<td class="gt_row gt_right" headers="Estimate">0.386</td> 5162<td class="gt_row gt_right" headers="Odds_Ratio">1.47</td> 5163<td class="gt_row gt_left" headers="CI">[1.31, 1.65]</td> 5164<td class="gt_row gt_left" headers="p_value">1.05e-10</td> 5165</tr> 5166<tr class="odd"> 5167<td class="gt_row gt_left" headers="Predictor">pct_developed</td> 5168<td class="gt_row gt_right" headers="Estimate">1.726</td> 5169<td class="gt_row gt_right" headers="Odds_Ratio">
51695.62</td> 5170<td class="gt_row gt_left" headers="CI">[4.02, 7.86]</td> 5171<td class="gt_row gt_left" headers="p_value">6.31e-24</td> 5172</tr> 5173<tr class="even"> 5174<td class="gt_row gt_left" headers="Predictor">factor(year_group)2014-2018</td> 5175<td class="gt_row gt_right" headers="Estimate">0.169</td> 5176<td class="gt_row gt_right" headers="Odds_Ratio">1.18</td> 5177<td class="gt_row gt_left" headers="CI">[0.92, 1.53]</td> 5178<td class="gt_row gt_left" headers="p_value">1.95e-01</td> 5179</tr> 5180<tr class="odd"> 5181<td class="gt_row gt_left" headers="Predictor">factor(year_group)2019-2023</td> 5182<td class="gt_row gt_right" headers="Estimate">0.953</td> 5183<td class="gt_row gt_right" headers="Odds_Ratio">2.59</td> 5184<td class="gt_row gt_left" headers="CI">[1.99, 3.38]</td> 5185<td class="gt_row gt_left" headers="p_value">1.26e-12</td> 5186</tr> 5187</tbody> 5188</table> 5189 5190</div> 5191</div> 5192<details class="code-fold"> 5193<summary>Code</summary> 5194<div class="sourceCode cell-code" id="cb17"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a><span class="co"># add significance to highlight predictors that are significant</span></span> 5195<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a>fixed_effects.plt_df <span class="ot"><-</span></span> 5196<span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a> fixed_effects <span class="sc">%>%</span></span> 5197<span id="cb17-4"><a href="#cb17-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(</span> 5198<span id="cb17-5"><a href="#cb17-5" aria-hidden="true" tabindex="-1"></a> <span class="at">Predictor =</span> <span class="fu">rownames</span>(.),</span> 5199<span id="cb17-6"><a href="#cb17-6" aria-hidden="true" tabindex="-1"></a> <span class="at">Significance =</span> <span class="fu">ifelse</span>(p_value <span class="sc"><</span> <span class="fl">0.05</span>, <span class="st">'Significant'</span>, <span class="st">'Non Significant'</span>)</span> 5200<span id="cb17-7"><a href="#cb17-7" aria-hidden="true" tabindex="-1"></a> )</span> 5201<span id="cb17-8"><a href="#cb17-8" aria-hidden="true" tabindex="-1"></a></span> 5202<span id="cb17-9"><a href="#cb17-9" aria-hidden="true" tabindex="-1"></a><span class="co"># plot fixed effects with confidence intervals and significance</span></span> 5203<span id="cb17-10"><a href="#cb17-10" aria-hidden="true" tabindex="-1"></a>fixed_effects_plt <span class="ot"><-</span></span> 5204<span id="cb17-11"><a href="#cb17-11" aria-hidden="true" tabindex="-1"></a> <span class="fu">ggplot</span>(fixed_effects.plt_df, <span class="fu">aes</span>(<span class="at">x =</span> <span class="fu">reorder</span>(Predictor, Odds_Ratio), <span class="at">y =</span> Odds_Ratio)) <span class="sc">+</span></span> 5205<span id="cb17-12"><a href="#cb17-12" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_point</span>(<span class="fu">aes</span>(<span class="at">color =</span> Significance), <span class="at">size =</span> <span class="dv">3</span>) <span class="sc">+</span></span> 5206<span id="cb17-13"><a href="#cb17-13" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_errorbar</span>(<span class="fu">aes</span>(<span class="at">ymin =</span> CI_Lower, <span class="at">ymax =</span> CI_Upper, <span class="at">color =</span> Significance), </span> 5207<span id="cb17-14"><a href="#cb17-14" aria-hidden="true" tabindex="-1"></a> <span class="at">width =</span> <span class="fl">0.2</span>) <span class="sc">+</span></span> 5208<span id="cb17-15"><a href="#cb17-15" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_hline</span>(<span class="at">yintercept =</span> <span class="dv">1</span>, <span class="at">linetype =</span> <span class="st">'dashed'</span>, <span class="at">color =</span> <span class="st">'red'</span>) <span class="sc">+</span></span> 5209<span id="cb17-16"><a href="#cb17-16" aria-hidden="true" tabindex="-1"></a> <span class="fu">scale_y_log10</span>() <span class="sc">+</span> <span class="co"># log scale for more condense plot</span></span> 5210<span id="cb17-17"><a href="#cb17-17" aria-hidden="true" tabindex="-1"></a> <span class="fu">coord_flip</span>() <span class="sc">+</span></span> 5211<span id="cb17-18"><a href="#cb17-18" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span> 5212<span id="cb17-19"><a href="#cb17-19" aria-hidden="true" tabindex="-1"></a>
5212 <span class="at">title =</span> <span class="st">'Odds Ratios with 95% Confidence Intervals'</span>,</span> 5213<span id="cb17-20"><a href="#cb17-20" aria-hidden="true" tabindex="-1"></a> <span class="at">subtitle =</span> <span class="st">'Logit Mixed-Effects Model (glmmTMB) with Spatial Lag'</span>,</span> 5214<span id="cb17-21"><a href="#cb17-21" aria-hidden="true" tabindex="-1"></a> <span class="at">x =</span> <span class="st">'Predictor'</span>,</span> 5215<span id="cb17-22"><a href="#cb17-22" aria-hidden="true" tabindex="-1"></a> <span class="at">y =</span> <span class="st">'Odds Ratio (log scale)'</span>,</span> 5216<span id="cb17-23"><a href="#cb17-23" aria-hidden="true" tabindex="-1"></a> <span class="at">color =</span> <span class="st">'Significance'</span></span> 5217<span id="cb17-24"><a href="#cb17-24" aria-hidden="true" tabindex="-1"></a> )</span> 5218<span id="cb17-25"><a href="#cb17-25" aria-hidden="true" tabindex="-1"></a></span> 5219<span id="cb17-26"><a href="#cb17-26" aria-hidden="true" tabindex="-1"></a>fixed_effects_plt</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div> 5220</details> 5221<div class="cell-output-display"> 5222<div> 5223<figure class="figure"> 5224<p><img role="img" 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89e/RzU2fGDLv7N6e903XxDrVs/5xsH2LPbny+7+gF3f6lXvgjH5X++3ko3X0Tj/NmT498X+5nPYp0+a/amAfbLH/v8yfFPVf719c/g5S8Ct/CGGhONqWHYIKtNMuwwB9h9YbVBDrC0Xz/A
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Since all predictors were scaled (i.e., standardized), interpretations are based on 1 standard deviation changes. Population density, unemployment rate, divorced rate, and percentage developed land cover are all strong, statistically significant predictors of distress and are all associated with a large increased odds that a block group is distressed. The highly increased odds of distress associated with higher population density and percentage developed land cover suggests that denser development and urbanization play a significant role in distress. At the same time, average rent plays the role of a minor yet statistically significant protective factor and would suggest that a block group with higher rent may be more stable.</p> 5230<p>The 2019-2023 year group is statistically significant, which shows that a trend of higher distressed odds started especially after 2018. This would suggest that various socioeconomic factors have gotten worse in the Southern Tier at least since 2018, leading to increased distress across the region over time and may have been exacerbated (or even caused) by the COVID pandemic.<
5230/p> 5231<p>Distress risk is not just temporal. The spatially lagged covariate of unemployment rate is also a strong, significant predictor of distress. It suggests that higher rates of unemployment in neighboring block groups are strongly associated with increased odds of distress in a given block group. More specifically, a 1 standard deviation increase in the spatially lagged unemployment rate is associated with a threefold increase in the odds that a block group is classified as distressed, independent of its own unemployment rate. Even if a block groupâs own unemployment is more moderate, being surrounded by high-unemployment areas still substantially increases distress risk.</p> 5232</section> 5233<section id="model-prediction-accuracy" class="level3"> 5234<h3 class="anchored" data-anchor-id="model-prediction-accuracy">Model Prediction Accuracy</h3> 5235<p>A confusion matrix is created from predicted probabilities that are converted to binary predictions, and then the modelâs overall accuracy, sensitivity, and specificity are calculated by using the remaining 20% of unique block group GEOIDs as test data.</p> 5236<div class="cell"> 5237<details class="code-fold"> 5238<summary>Code</summary> 5239<div class="sourceCode cell-code" id="cb18"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a><span class="co"># get predicted probabilities</span></span> 5240<span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a>pred_probs <span class="ot"><-</span> <span class="fu">predict</span>(model_glmm, <span class="at">newdata =</span> test_data,</span> 5241<span id="cb18-3"><a href="#cb18-3" aria-hidden="true" tabindex="-1"></a> <span class="at">type =</span> <span class="st">'response'</span>, <span class="at">allow.new.levels =</span> <span class="cn">TRUE</span>)</span> 5242<span id="cb18-4"><a href="#cb18-4" aria-hidden="true" tabindex="-1"></a></span> 5243<span id="cb18-5"><a href="#cb18-5" aria-hidden="true" tabindex="-1"></a><span class="co"># convert probabilities to binary predictions</span></span> 5244<span id="cb18-6"><a href="#cb18-6" aria-hidden="true" tabindex="-1"></a>pred_class <span class="ot"><-</span> <span class="fu">ifelse</span>(pred_probs <span class="sc">></span> <span class="fl">0.5</span>, <span class="dv">1</span>, <span class="dv">0</span>)</span> 5245<span id="cb18-7"><a href="#cb18-7" aria-hidden="true" tabindex="-1"></a></span> 5246<span id="cb18-8"><a href="#cb18-8" aria-hidden="true" tabindex="-1"></a><span class="co"># create confusion matrix, then extract values</span></span> 5247<span id="cb18-9"><a href="#cb18-9" aria-hidden="true" tabindex="-1"></a>conf_matrix <span class="ot"><-</span> <span class="fu">table</span>(<span class="at">Predicted =</span> pred_class, <span class="at">Actual =</span> test_data<span class="sc">$</span>is_distressed)</span> 5248<span id="cb18-10"><a href="#cb18-10" aria-hidden="true" tabindex="-1"></a></span> 5249<span id="cb18-11"><a href="#cb18-11" aria-hidden="true" tabindex="-1"></a>TN <span class="ot"><-</span> conf_matrix[<span class="dv">1</span>,<span class="dv">1</span>]</span> 5250<span id="cb18-12"><a href="#cb18-12" aria-hidden="true" tabindex="-1"></a>FP <span class="ot"><-</span> conf_matrix[<span class="dv">2</span>,<span class="dv">1</span>]</span> 5251<span id="cb18-13"><a href="#cb18-13" aria-hidden="true" tabindex="-1"></a>FN <span class="ot"><-</span> conf_matrix[<span class="dv">1</span>,<span class="dv">2</span>]</span> 5252<span id="cb18-14"><a href="#cb18-14" aria-hidden="true" tabindex="-1"></a>TP <span class="ot"><-</span> conf_matrix[<span class="dv">2</span>,<span class="dv">2</span>]</span> 5253<span id="cb18-15"><a href="#cb18-15" aria-hidden="true" tabindex="-1"></a></span> 5254<span id="cb18-16"><a href="#cb18-16" aria-hidden="true" tabindex="-1"></a><span class="co"># create confusion matrix dataframe</span></span> 5255<span id="cb18-17"><a href="#cb18-17" aria-hidden="true" tabindex="-1"></a>confusion_df <span class="ot"><-</span> <span class="fu">tibble</span>(</span> 5256<span id="cb18-18"><a href="#cb18-18" aria-hidden="true" tabindex="-1"></a> <span class="at">Outcome =</span> <span class="fu">c</span>(<span class="st">'True Negatives'</span>, <span class="st">'False Positives'</span>,</span> 5257<span id="cb18-19"><a href="#cb18-19" aria-hidden="true" tabindex="-1"></a>
5257 <span class="st">'False Negatives'</span>, <span class="st">'True Positives'</span>),</span> 5258<span id="cb18-20"><a href="#cb18-20" aria-hidden="true" tabindex="-1"></a> <span class="at">Count =</span> <span class="fu">c</span>(TN, FP, FN, TP)</span> 5259<span id="cb18-21"><a href="#cb18-21" aria-hidden="true" tabindex="-1"></a>)</span> 5260<span id="cb18-22"><a href="#cb18-22" aria-hidden="true" tabindex="-1"></a></span> 5261<span id="cb18-23"><a href="#cb18-23" aria-hidden="true" tabindex="-1"></a><span class="co"># create gt table for confusion matrix</span></span> 5262<span id="cb18-24"><a href="#cb18-24" aria-hidden="true" tabindex="-1"></a>confusion_gt <span class="ot"><-</span></span> 5263<span id="cb18-25"><a href="#cb18-25" aria-hidden="true" tabindex="-1"></a> confusion_df <span class="sc">%>%</span></span> 5264<span id="cb18-26"><a href="#cb18-26" aria-hidden="true" tabindex="-1"></a> <span class="fu">gt</span>() <span class="sc">%>%</span></span> 5265<span id="cb18-27"><a href="#cb18-27" aria-hidden="true" tabindex="-1"></a> <span class="fu">tab_header</span>(<span class="at">title =</span> <span class="st">'Confusion Matrix: Distressed Status Prediction Model'</span>) <span class="sc">%>%</span></span> 5266<span id="cb18-28"><a href="#cb18-28" aria-hidden="true" tabindex="-1"></a> <span class="fu">fmt_number</span>(<span class="at">columns =</span> Count, <span class="at">decimals =</span> <span class="dv">0</span>)</span> 5267<span id="cb18-29"><a href="#cb18-29" aria-hidden="true" tabindex="-1"></a></span> 5268<span id="cb18-30"><a href="#cb18-30" aria-hidden="true" tabindex="-1"></a><span class="co"># calculate metrics</span></span> 5269<span id="cb18-31"><a href="#cb18-31" aria-hidden="true" tabindex="-1"></a>accuracy <span class="ot"><-</span> (TP <span class="sc">+</span> TN) <span class="sc">/</span> <span class="fu">sum</span>(conf_matrix)</span> 5270<span id="cb18-32"><a href="#cb18-32" aria-hidden="true" tabindex="-1"></a>sensitivity <span class="ot"><-</span> TP <span class="sc">/</span> (TP <span class="sc">+</span> FN)</span> 5271<span id="cb18-33"><a href="#cb18-33" aria-hidden="true" tabindex="-1"></a>specificity <span class="ot"><-</span> TN <span class="sc">/</span> (TN <span class="sc">+</span> FP)</span> 5272<span id="cb18-34"><a href="#cb18-34" aria-hidden="true" tabindex="-1"></a></span> 5273<span id="cb18-35"><a href="#cb18-35" aria-hidden="true" tabindex="-1"></a><span class="co"># create metrix dataframe</span></span> 5274<span id="cb18-36"><a href="#cb18-36" aria-hidden="true" tabindex="-1"></a>metrics_df <span class="ot"><-</span> <span class="fu">tibble</span>(</span> 5275<span id="cb18-37"><a href="#cb18-37" aria-hidden="true" tabindex="-1"></a> <span class="at">Metric =</span> <span class="fu">c</span>(<span class="st">'Accuracy'</span>, <span class="st">'Sensitivity (Recall)'</span>, <span class="st">'Specificity'</span>),</span> 5276<span id="cb18-38"><a href="#cb18-38" aria-hidden="true" tabindex="-1"></a> <span class="at">Value =</span> <span class="fu">c</span>(accuracy, sensitivity, specificity)</span> 5277<span id="cb18-39"><a href="#cb18-39" aria-hidden="true" tabindex="-1"></a>)</span> 5278<span id="cb18-40"><a href="#cb18-40" aria-hidden="true" tabindex="-1"></a></span> 5279<span id="cb18-41"><a href="#cb18-41" aria-hidden="true" tabindex="-1"></a><span class="co"># create gt table for metrics</span></span> 5280<span id="cb18-42"><a href="#cb18-42" aria-hidden="true" tabindex="-1"></a>metrics_gt <span class="ot"><-</span></span> 5281<span id="cb18-43"><a href="#cb18-43" aria-hidden="true" tabindex="-1"></a> metrics_df <span class="sc">%>%</span></span> 5282<span id="cb18-44"><a href="#cb18-44" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">Value =</span> <span class="fu">round</span>(Value, <span class="dv">4</span>)) <span class="sc">%>%</span></span> 5283<span id="cb18-45"><a href="#cb18-45" aria-hidden="true" tabindex="-1"></a> <span class="fu">gt</span>() <span class="sc">%>%</span></span> 5284<span id="cb18-46"><a href="#cb18-46" aria-hidden="true" tabindex="-1"></a> <span class="fu">tab_header</span>(<span class="at">title =</span> <span class="st">'Distressed Status Prediction Model Performance Metrics'</span>) <span class="sc">%>%</span></span> 5285<span id="cb18-47"><a href="#cb18-47" aria-hidden="true" tabindex="-1"></a> <span class="fu">fmt_percent</span>(<span class="at">columns =</span> Value, <span class="at">decimals =</span> <span class="dv">2</span>)</span> 5286<span id="cb18-48"><a href="#cb18-48" aria-hidden="true" tabindex="-1"></a></span> 5287<span id="cb18-49"><a href="#cb18-49" aria-hidden="true" tabindex="-1"></a><span class="co"># show gt tables of confusion matrix and metrics</span></span> 5288<span id="cb18-50"><a href="#cb18-50" aria-hidden="true" tabindex="-1"></a>confusion_gt</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div> 5289</details> 5290<div class="cell-output-display"> 5291<div id="veyggzpzdb" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;"> 5292<style>#veyggzpzdb table { 5293font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji'; 5294-webkit-font-smoothing: antialiased; 5295-moz-osx-font-smoothing: grayscale; 5296} 5297#veyggzpzdb thead, #veyggzpzdb tbody, #veyggzpzdb tfoot, #veyggzpzdb tr, #veyggzpzdb td, #veyggzpzdb th { 5298border-style: none; 5299} 5300#veyggzpzdb p { 5301margin: 0; 5302padding: 0; 5303} 5304#veyggzpzdb .gt_table { 5305display: table; 5306border-collapse: collapse; 5307line-height: normal; 5308margin-left: auto; 5309margin-right: auto; 5310color: #333333; 5311font-size: 16px;
5312font-weight: normal; 5313font-style: normal; 5314background-color: #FFFFFF; 5315width: auto; 5316border-top-style: solid; 5317border-top-width: 2px; 5318border-top-color: #A8A8A8; 5319border-right-style: none; 5320border-right-width: 2px; 5321border-right-color: #D3D3D3; 5322border-bottom-style: solid; 5323border-bottom-width: 2px; 5324border-bottom-color: #A8A8A8; 5325border-left-style: none; 5326border-left-width: 2px; 5327border-left-color: #D3D3D3; 5328} 5329#veyggzpzdb .gt_caption { 5330padding-top: 4px; 5331padding-bottom: 4px; 5332} 5333#veyggzpzdb .gt_title { 5334color: #333333; 5335font-size: 125%; 5336font-weight: initial; 5337padding-top: 4px; 5338padding-bottom: 4px; 5339padding-left: 5px; 5340padding-right: 5px; 5341border-bottom-color: #FFFFFF; 5342border-bottom-width: 0; 5343} 5344#veyggzpzdb .gt_subtitle { 5345color: #333333; 5346font-size: 85%; 5347font-weight: initial; 5348padding-top: 3px; 5349padding-bottom: 5px; 5350padding-left: 5px; 5351padding-right: 5px; 5352border-top-color: #FFFFFF; 5353border-top-width: 0; 5354} 5355#veyggzpzdb .gt_heading { 5356background-color: #FFFFFF; 5357text-align: center; 5358border-bottom-color: #FFFFFF; 5359border-left-style: none; 5360border-left-width: 1px; 5361border-left-color: #D3D3D3; 5362border-right-style: none; 5363border-right-width: 1px; 5364border-right-color: #D3D3D3; 5365} 5366#veyggzpzdb .gt_bottom_border { 5367border-bottom-style: solid; 5368border-bottom-width: 2px; 5369border-bottom-color: #D3D3D3; 5370} 5371#veyggzpzdb .gt_col_headings { 5372border-top-style: solid; 5373border-top-width: 2px; 5374border-top-color: #D3D3D3; 5375border-bottom-style: solid; 5376border-bottom-width: 2px; 5377border-bottom-color: #D3D3D3; 5378border-left-style: none; 5379border-left-width: 1px; 5380border-left-color: #D3D3D3; 5381border-right-style: none; 5382border-right-width: 1px; 5383border-right-color: #D3D3D3; 5384} 5385#veyggzpzdb .gt_col_heading { 5386color: #333333; 5387background-color: #FFFFFF; 5388font-size: 100%; 5389font-weight: normal; 5390text-transform: inherit; 5391border-left-style: none; 5392border-left-width: 1px; 5393border-left-color: #D3D3D3; 5394border-right-style: none; 5395border-right-width: 1px; 5396border-right-color: #D3D3D3; 5397vertical-align: bottom; 5398padding-top: 5px; 5399padding-bottom: 6px; 5400padding-left: 5px; 5401padding-right: 5px; 5402overflow-x: hidden; 5403} 5404#veyggzpzdb .gt_column_spanner_outer { 5405color: #333333; 5406background-color: #FFFFFF; 5407font-size: 100%; 5408font-weight: normal; 5409text-transform: inherit; 5410padding-top: 0; 5411padding-bottom: 0; 5412padding-left: 4px; 5413padding-right: 4px; 5414} 5415#veyggzpzdb .gt_column_spanner_outer:first-child { 5416padding-left: 0; 5417} 5418#veyggzpzdb .gt_column_spanner_outer:last-child { 5419padding-right: 0; 5420} 5421#veyggzpzdb .gt_column_spanner { 5422border-bottom-style: solid; 5423border-bottom-width: 2px; 5424border-bottom-color: #D3D3D3; 5425vertical-align: bottom; 5426padding-top: 5px; 5427padding-bottom: 5px; 5428overflow-x: hidden; 5429display: inline-block; 5430width: 100%; 5431} 5432#veyggzpzdb .gt_spanner_row { 5433border-bottom-style: hidden; 5434} 5435#veyggzpzdb .gt_group_heading { 5436padding-top: 8px; 5437padding-bottom: 8px; 5438padding-left: 5px; 5439padding-right: 5px; 5440color: #333333; 5441background-color: #FFFFFF; 5442font-size: 100%; 5443font-weight: initial; 5444text-transform: inherit; 5445border-top-style: solid; 5446border-top-width: 2px; 5447border-top-color: #D3D3D3; 5448border-bottom-style: solid; 5449border-bottom-width: 2px; 5450border-bottom-color: #D3D3D3; 5451border-left-style: none; 5452border-left-width: 1px; 5453border-left-color: #D3D3D3; 5454border-right-style: none; 5455border-right-width: 1px; 5456border-right-color: #D3D3D3; 5457vertical-align: middle; 5458text-align: left; 5459} 5460#veyggzpzdb .gt_empty_group_heading { 5461padding: 0.5px; 5462color: #333333; 5463background-color: #FFFFFF; 5464font-size: 100%; 5465font-weight: initial; 5466border-top-style: solid; 5467border-top-width: 2px; 5468border-top-color: #D3D3D3; 5469border-bottom-style: solid; 5470border-bottom-width: 2px; 5471border-bottom-color: #D3D3D3; 5472vertical-align: middle; 5473} 5474#veyggzpzdb .gt_from_md > :first-child { 5475margin-top: 0; 5476} 5477#veyggzpzdb .gt_from_md > :last-child { 5478margin-bottom: 0; 5479} 5480#veyggzpzdb .gt_row { 5481padding-top: 8px; 5482padding-bottom: 8px; 5483padding-left: 5px; 5484padding-right: 5px; 5485margin: 10px; 5486border-top-style: solid; 5487border-top-width: 1px; 5488border-top-color: #D3D3D3; 5489border-left-style: none; 5490border-left-width: 1px; 5491border-left-color: #D3D3D3; 5492border-right-style: none; 5493border-right-width: 1px; 5494border-right-color: #D3D3D3; 5495vertical-align: middle; 5496overflow-x: hidden; 5497} 5498#veyggzpzdb .gt_stub { 5499color: #333333; 5500background-color: #FFFFFF; 5501font-size: 100%;
5502font-weight: initial; 5503text-transform: inherit; 5504border-right-style: solid; 5505border-right-width: 2px; 5506border-right-color: #D3D3D3; 5507padding-left: 5px; 5508padding-right: 5px; 5509} 5510#veyggzpzdb .gt_stub_row_group { 5511color: #333333; 5512background-color: #FFFFFF; 5513font-size: 100%; 5514font-weight: initial; 5515text-transform: inherit; 5516border-right-style: solid; 5517border-right-width: 2px; 5518border-right-color: #D3D3D3; 5519padding-left: 5px; 5520padding-right: 5px; 5521vertical-align: top; 5522} 5523#veyggzpzdb .gt_row_group_first td { 5524border-top-width: 2px; 5525} 5526#veyggzpzdb .gt_row_group_first th { 5527border-top-width: 2px; 5528} 5529#veyggzpzdb .gt_summary_row { 5530color: #333333; 5531background-color: #FFFFFF; 5532text-transform: inherit; 5533padding-top: 8px; 5534padding-bottom: 8px; 5535padding-left: 5px; 5536padding-right: 5px; 5537} 5538#veyggzpzdb .gt_first_summary_row { 5539border-top-style: solid; 5540border-top-color: #D3D3D3; 5541} 5542#veyggzpzdb .gt_first_summary_row.thick { 5543border-top-width: 2px; 5544} 5545#veyggzpzdb .gt_last_summary_row { 5546padding-top: 8px; 5547padding-bottom: 8px; 5548padding-left: 5px; 5549padding-right: 5px; 5550border-bottom-style: solid; 5551border-bottom-width: 2px; 5552border-bottom-color: #D3D3D3; 5553} 5554#veyggzpzdb .gt_grand_summary_row { 5555color: #333333; 5556background-color: #FFFFFF; 5557text-transform: inherit; 5558padding-top: 8px; 5559padding-bottom: 8px; 5560padding-left: 5px; 5561padding-right: 5px; 5562} 5563#veyggzpzdb .gt_first_grand_summary_row { 5564padding-top: 8px; 5565padding-bottom: 8px; 5566padding-left: 5px; 5567padding-right: 5px; 5568border-top-style: double; 5569border-top-width: 6px; 5570border-top-color: #D3D3D3; 5571} 5572#veyggzpzdb .gt_last_grand_summary_row_top { 5573padding-top: 8px; 5574padding-bottom: 8px; 5575padding-left: 5px; 5576padding-right: 5px; 5577border-bottom-style: double; 5578border-bottom-width: 6px; 5579border-bottom-color: #D3D3D3; 5580} 5581#veyggzpzdb .gt_striped { 5582background-color: rgba(128, 128, 128, 0.05); 5583} 5584#veyggzpzdb .gt_table_body { 5585border-top-style: solid; 5586border-top-width: 2px; 5587border-top-color: #D3D3D3; 5588border-bottom-style: solid; 5589border-bottom-width: 2px; 5590border-bottom-color: #D3D3D3; 5591} 5592#veyggzpzdb .gt_footnotes { 5593color: #333333; 5594background-color: #FFFFFF; 5595border-bottom-style: none; 5596border-bottom-width: 2px; 5597border-bottom-color: #D3D3D3; 5598border-left-style: none; 5599border-left-width: 2px; 5600border-left-color: #D3D3D3; 5601border-right-style: none; 5602border-right-width: 2px; 5603border-right-color: #D3D3D3; 5604} 5605#veyggzpzdb .gt_footnote { 5606margin: 0px; 5607font-size: 90%; 5608padding-top: 4px; 5609padding-bottom: 4px; 5610padding-left: 5px; 5611padding-right: 5px; 5612} 5613#veyggzpzdb .gt_sourcenotes { 5614color: #333333; 5615background-color: #FFFFFF; 5616border-bottom-style: none; 5617border-bottom-width: 2px; 5618border-bottom-color: #D3D3D3; 5619border-left-style: none; 5620border-left-width: 2px; 5621border-left-color: #D3D3D3; 5622border-right-style: none; 5623border-right-width: 2px; 5624border-right-color: #D3D3D3; 5625} 5626#veyggzpzdb .gt_sourcenote { 5627font-size: 90%; 5628padding-top: 4px; 5629padding-bottom: 4px; 5630padding-left: 5px; 5631padding-right: 5px; 5632} 5633#veyggzpzdb .gt_left { 5634text-align: left; 5635} 5636#veyggzpzdb .gt_center { 5637text-align: center; 5638} 5639#veyggzpzdb .gt_right { 5640text-align: right; 5641font-variant-numeric: tabular-nums; 5642} 5643#veyggzpzdb .gt_font_normal { 5644font-weight: normal; 5645} 5646#veyggzpzdb .gt_font_bold {
5647font-weight: bold; 5648} 5649#veyggzpzdb .gt_font_italic { 5650font-style: italic; 5651} 5652#veyggzpzdb .gt_super { 5653font-size: 65%; 5654} 5655#veyggzpzdb .gt_footnote_marks { 5656font-size: 75%; 5657vertical-align: 0.4em; 5658position: initial; 5659} 5660#veyggzpzdb .gt_asterisk { 5661font-size: 100%; 5662vertical-align: 0; 5663} 5664#veyggzpzdb .gt_indent_1 { 5665text-indent: 5px; 5666} 5667#veyggzpzdb .gt_indent_2 { 5668text-indent: 10px; 5669} 5670#veyggzpzdb .gt_indent_3 { 5671text-indent: 15px; 5672} 5673#veyggzpzdb .gt_indent_4 { 5674text-indent: 20px; 5675} 5676#veyggzpzdb .gt_indent_5 { 5677text-indent: 25px; 5678} 5679#veyggzpzdb .katex-display { 5680display: inline-flex !important; 5681margin-bottom: 0.75em !important; 5682} 5683#veyggzpzdb div.Reactable > div.rt-table > div.rt-thead > div.rt-tr.rt-tr-group-header > div.rt-th-group:after { 5684height: 0px !important; 5685} 5686</style> 5687 5688<table class="gt_table caption-top table table-sm table-striped small" data-quarto-postprocess="true" data-quarto-disable-processing="false" data-quarto-bootstrap="false"> 5689<thead> 5690<tr class="header gt_heading"> 5691<th colspan="2" class="gt_heading gt_title gt_font_normal gt_bottom_border">Confusion Matrix: Distressed Status Prediction Model</th> 5692</tr> 5693<tr class="odd gt_col_headings"> 5694<th id="Outcome" class="gt_col_heading gt_columns_bottom_border gt_left" data-quarto-table-cell-role="th" scope="col">Outcome</th> 5695<th id="Count" class="gt_col_heading gt_columns_bottom_border gt_right" data-quarto-table-cell-role="th" scope="col">Count</th> 5696</tr> 5697</thead> 5698<tbody class="gt_table_body"> 5699<tr class="odd"> 5700<td class="gt_row gt_left" headers="Outcome">True Negatives</td> 5701<td class="gt_row gt_right" headers="Count">2,708</td> 5702</tr> 5703<tr class="even"> 5704<td class="gt_row gt_left" headers="Outcome">False Positives</td> 5705<td class="gt_row gt_right" headers="Count">25</td> 5706</tr> 5707<tr class="odd"> 5708<td class="gt_row gt_left" headers="Outcome">False Negatives</td> 5709<td class="gt_row gt_right" headers="Count">202</td> 5710</tr> 5711<tr class="even"> 5712<td class="gt_row gt_left" headers="Outcome">True Positives</td> 5713<td class="gt_row gt_right" headers="Count">80</td> 5714</tr> 5715</tbody> 5716</table> 5717 5718</div> 5719</div> 5720<details class="code-fold"> 5721<summary>Code</summary> 5722<div class="sourceCode cell-code" id="cb19"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a>metrics_gt</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div> 5723</details> 5724<div class="cell-output-display"> 5725<div id="aqeeubkbho" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;"> 5726<style>#aqeeubkbho table { 5727font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji'; 5728-webkit-font-smoothing: antialiased; 5729-moz-osx-font-smoothing: grayscale; 5730} 5731#aqeeubkbho thead, #aqeeubkbho tbody, #aqeeubkbho tfoot, #aqeeubkbho tr, #aqeeubkbho td, #aqeeubkbho th { 5732border-style: none; 5733} 5734#aqeeubkbho p { 5735margin: 0; 5736padding: 0; 5737} 5738#aqeeubkbho .gt_table { 5739display: table; 5740border-collapse: collapse; 5741line-height: normal; 5742margin-left: auto; 5743margin-right: auto; 5744color: #333333; 5745font-size: 16px; 5746font-weight: normal; 5747font-style: normal; 5748background-color: #FFFFFF; 5749width: auto; 5750border-top-style: solid; 5751border-top-width: 2px; 5752border-top-color: #A8A8A8; 5753border-right-style: none; 5754border-right-width: 2px; 5755border-right-color: #D3D3D3; 5756border-bottom-style: solid; 5757border-bottom-width: 2px; 5758border-bottom-color: #A8A8A8; 5759border-left-style: none; 5760border-left-width: 2px; 5761border-left-color: #D3D3D3; 5762} 5763#aqeeubkbho .gt_caption { 5764padding-top: 4px; 5765padding-bottom: 4px; 5766} 5767#aqeeubkbho .gt_title { 5768color: #333333; 5769font-size: 125%; 5770font-weight: initial; 5771padding-top: 4px; 5772padding-bottom: 4px; 5773padding-left: 5px; 5774padding-right: 5px; 5775border-bottom-color: #FFFFFF; 5776border-bottom-width: 0; 5777} 5778#aqeeubkbho .gt_subtitle { 5779color: #333333; 5780font-size: 85%; 5781font-weight: initial; 5782padding-top: 3px; 5783padding-bottom: 5px; 5784padding-left: 5px; 5785padding-right: 5px; 5786border-top-color: #FFFFFF; 5787border-top-width: 0; 5788} 5789#aqeeubkbho .gt_heading { 5790background-color: #FFFFFF; 5791text-align: center; 5792border-bottom-color: #FFFFFF; 5793border-left-style: none; 5794border-left-width: 1px; 5795border-left-color: #D3D3D3; 5796border-right-style: none; 5797border-right-width: 1px; 5798border-right-color: #D3D3D3; 5799} 5800#aqeeubkbho .gt_bottom_border { 5801border-bottom-style: solid; 5802border-bottom-width: 2px; 5803border-bottom-color: #D3D3D3; 5804} 5805#aqeeubkbho .gt_col_headings { 5806border-top-style: solid; 5807border-top-width: 2px; 5808border-top-color: #D3D3D3; 5809border-bottom-style: solid; 5810border-bottom-width: 2px; 5811border-bottom-color: #D3D3D3; 5812border-left-style: none; 5813border-left-width: 1px; 5814border-left-color: #D3D3D3; 5815border-right-style: none; 5816border-right-width: 1px; 5817border-right-color: #D3D3D3; 5818} 5819#aqeeubkbho .gt_col_heading { 5820color: #333333; 5821background-color: #FFFFFF; 5822font-size: 100%;
5823font-weight: normal; 5824text-transform: inherit; 5825border-left-style: none; 5826border-left-width: 1px; 5827border-left-color: #D3D3D3; 5828border-right-style: none; 5829border-right-width: 1px; 5830border-right-color: #D3D3D3; 5831vertical-align: bottom; 5832padding-top: 5px; 5833padding-bottom: 6px; 5834padding-left: 5px; 5835padding-right: 5px; 5836overflow-x: hidden; 5837} 5838#aqeeubkbho .gt_column_spanner_outer { 5839color: #333333; 5840background-color: #FFFFFF; 5841font-size: 100%; 5842font-weight: normal; 5843text-transform: inherit; 5844padding-top: 0; 5845padding-bottom: 0; 5846padding-left: 4px; 5847padding-right: 4px; 5848} 5849#aqeeubkbho .gt_column_spanner_outer:first-child { 5850padding-left: 0; 5851} 5852#aqeeubkbho .gt_column_spanner_outer:last-child { 5853padding-right: 0; 5854} 5855#aqeeubkbho .gt_column_spanner { 5856border-bottom-style: solid; 5857border-bottom-width: 2px; 5858border-bottom-color: #D3D3D3; 5859vertical-align: bottom; 5860padding-top: 5px; 5861padding-bottom: 5px; 5862overflow-x: hidden; 5863display: inline-block; 5864width: 100%; 5865} 5866#aqeeubkbho .gt_spanner_row { 5867border-bottom-style: hidden; 5868} 5869#aqeeubkbho .gt_group_heading { 5870padding-top: 8px; 5871padding-bottom: 8px; 5872padding-left: 5px; 5873padding-right: 5px; 5874color: #333333; 5875background-color: #FFFFFF; 5876font-size: 100%; 5877font-weight: initial; 5878text-transform: inherit; 5879border-top-style: solid; 5880border-top-width: 2px; 5881border-top-color: #D3D3D3; 5882border-bottom-style: solid; 5883border-bottom-width: 2px; 5884border-bottom-color: #D3D3D3; 5885border-left-style: none; 5886border-left-width: 1px; 5887border-left-color: #D3D3D3; 5888border-right-style: none; 5889border-right-width: 1px; 5890border-right-color: #D3D3D3; 5891vertical-align: middle; 5892text-align: left; 5893} 5894#aqeeubkbho .gt_empty_group_heading { 5895padding: 0.5px; 5896color: #333333; 5897background-color: #FFFFFF; 5898font-size: 100%; 5899font-weight: initial; 5900border-top-style: solid; 5901border-top-width: 2px; 5902border-top-color: #D3D3D3; 5903border-bottom-style: solid; 5904border-bottom-width: 2px; 5905border-bottom-color: #D3D3D3; 5906vertical-align: middle; 5907} 5908#aqeeubkbho .gt_from_md > :first-child { 5909margin-top: 0; 5910} 5911#aqeeubkbho .gt_from_md > :last-child { 5912margin-bottom: 0; 5913} 5914#aqeeubkbho .gt_row { 5915padding-top: 8px; 5916padding-bottom: 8px; 5917padding-left: 5px; 5918padding-right: 5px; 5919margin: 10px; 5920border-top-style: solid; 5921border-top-width: 1px; 5922border-top-color: #D3D3D3; 5923border-left-style: none; 5924border-left-width: 1px; 5925border-left-color: #D3D3D3; 5926border-right-style: none; 5927border-right-width: 1px; 5928border-right-color: #D3D3D3; 5929vertical-align: middle; 5930overflow-x: hidden; 5931} 5932#aqeeubkbho .gt_stub { 5933color: #333333; 5934background-color: #FFFFFF; 5935font-size: 100%; 5936font-weight: initial; 5937text-transform: inherit; 5938border-right-style: solid; 5939border-right-width: 2px; 5940border-right-color: #D3D3D3; 5941padding-left: 5px; 5942padding-right: 5px; 5943} 5944#aqeeubkbho .gt_stub_row_group { 5945color: #333333; 5946background-color: #FFFFFF; 5947font-size: 100%; 5948font-weight: initial; 5949text-transform: inherit; 5950border-right-style: solid; 5951border-right-width: 2px; 5952border-right-color: #D3D3D3; 5953padding-left: 5px; 5954padding-right: 5px; 5955vertical-align: top; 5956} 5957#aqeeubkbho .gt_row_group_first td { 5958border-top-width: 2px; 5959} 5960#aqeeubkbho .gt_row_group_first th { 5961border-top-width: 2px; 5962} 5963#aqeeubkbho .gt_summary_row { 5964color: #333333; 5965background-color: #FFFFFF; 5966text-transform: inherit; 5967padding-top: 8px; 5968padding-bottom: 8px; 5969padding-left: 5px; 5970padding-right: 5px; 5971} 5972#aqeeubkbho .gt_first_summary_row { 5973border-top-style: solid; 5974border-top-color: #D3D3D3; 5975} 5976#aqeeubkbho .gt_first_summary_row.thick { 5977border-top-width: 2px; 5978} 5979#aqeeubkbho .gt_last_summary_row { 5980padding-top: 8px; 5981padding-bottom: 8px; 5982padding-left: 5px; 5983padding-right: 5px; 5984border-bottom-style: solid; 5985border-bottom-width: 2px; 5986border-bottom-color: #D3D3D3; 5987} 5988#aqeeubkbho .gt_grand_summary_row { 5989color: #333333; 5990background-color: #FFFFFF; 5991text-transform: inherit; 5992padding-top: 8px; 5993padding-bottom: 8px; 5994padding-left: 5px; 5995padding-right: 5px; 5996} 5997#aqeeubkbho .gt_first_grand_summary_row { 5998padding-top: 8px; 5999padding-bottom: 8px; 6000padding-left: 5px; 6001padding-right: 5px; 6002border-top-style: double; 6003border-top-width: 6px; 6004border-top-color: #D3D3D3; 6005} 6006#aqeeubkbho .gt_last_grand_summary_row_top { 6007padding-top: 8px; 6008padding-bottom: 8px; 6009padding-left: 5px; 6010padding-right: 5px; 6011border-bottom-style: double; 6012border-bottom-width: 6px; 6013border-bottom-color: #D3D3D3; 6014} 6015#aqeeubkbho .gt_striped { 6016background-color: rgba(128, 128, 128, 0.05); 6017} 6018#aqeeubkbho .gt_table_body { 6019border-top-style: solid; 6020border-top-width: 2px; 6021border-top-color: #D3D3D3; 6022border-bottom-style: solid; 6023border-bottom-width: 2px; 6024border-bottom-color: #D3D3D3; 6025} 6026#aqeeubkbho .gt_footnotes { 6027color: #333333; 6028background-color: #FFFFFF; 6029border-bottom-style: none; 6030border-bottom-width: 2px; 6031border-bottom-color: #D3D3D3; 6032border-left-style: none; 6033border-left-width: 2px; 6034border-left-color: #D3D3D3; 6035border-right-style: none; 6036border-right-width: 2px; 6037border-right-color: #D3D3D3; 6038} 6039#aqeeubkbho .gt_footnote { 6040margin: 0px; 6041font-size: 90%; 6042padding-top: 4px; 6043padding-bottom: 4px; 6044padding-left: 5px; 6045padding-right: 5px; 6046} 6047#aqeeubkbho .gt_sourcenotes { 6048color: #333333; 6049background-color: #FFFFFF; 6050border-bottom-style: none; 6051border-bottom-width: 2px; 6052border-bottom-color: #D3D3D3; 6053border-left-style: none; 6054border-left-width: 2px; 6055border-left-color: #D3D3D3; 6056border-right-style: none; 6057border-right-width: 2px; 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6078font-weight: normal; 6079} 6080#aqeeubkbho .gt_font_bold { 6081font-weight: bold; 6082} 6083#aqeeubkbho .gt_font_italic { 6084font-style: italic; 6085} 6086#aqeeubkbho .gt_super { 6087font-size: 65%; 6088} 6089#aqeeubkbho .gt_footnote_marks { 6090font-size: 75%; 6091vertical-align: 0.4em; 6092position: initial; 6093} 6094#aqeeubkbho .gt_asterisk { 6095font-size: 100%; 6096vertical-align: 0; 6097} 6098#aqeeubkbho .gt_indent_1 { 6099text-indent: 5px; 6100} 6101#aqeeubkbho .gt_indent_2 { 6102text-indent: 10px; 6103} 6104#aqeeubkbho .gt_indent_3 { 6105text-indent: 15px; 6106} 6107#aqeeubkbho .gt_indent_4 { 6108text-indent: 20px; 6109} 6110#aqeeubkbho .gt_indent_5 { 6111text-indent: 25px; 6112} 6113#aqeeubkbho .katex-display { 6114display: inline-flex !important; 6115margin-bottom: 0.75em !important; 6116} 6117#aqeeubkbho div.Reactable > div.rt-table > div.rt-thead > div.rt-tr.rt-tr-group-header > div.rt-th-group:after { 6118height: 0px !important; 6119} 6120</style> 6121 6122<table class="gt_table caption-top table table-sm table-striped small" data-quarto-postprocess="true" data-quarto-disable-processing="false" data-quarto-bootstrap="false"> 6123<thead> 6124<tr class="header gt_heading"> 6125<th colspan="2" class="gt_heading gt_title gt_font_normal gt_bottom_border">Distressed Status Prediction Model Performance Metrics</th> 6126</tr> 6127<tr class="odd gt_col_headings"> 6128<th id="Metric" class="gt_col_heading gt_columns_bottom_border gt_left" data-quarto-table-cell-role="th" scope="col">Metric</th> 6129<th id="Value" class="gt_col_heading gt_columns_bottom_border gt_right" data-quarto-table-cell-role="th" scope="col">Value</th> 6130</tr> 6131</thead> 6132<tbody class="gt_table_body"> 6133<tr class="odd"> 6134<td class="gt_row gt_left" headers="Metric">Accuracy</td> 6135<td class="gt_row gt_right" headers="Value">92.47%</td> 6136</tr> 6137<tr class="even"> 6138<td class="gt_row gt_left" headers="Metric">Sensitivity (Recall)</td> 6139<td class="gt_row gt_right" headers="Value">28.37%</td> 6140</tr> 6141<tr class="odd"> 6142<td class="gt_row gt_left" headers="Metric">Specificity</td> 6143<td class="gt_row gt_right" headers="Value">99.09%</td> 6144</tr> 6145</tbody> 6146</table> 6147 6148</div> 6149</div> 6150</div> 6151<p>Overall, the model has a prediction accuracy rate of about 92%. However, this is mostly due to the modelâs high level of ability at ruling out distress evidenced by its 99% specificity. Accuracy suffers substantially at predicting block groups that are in distress, where it is only ~28% accurate. This suggests that the model is conservative and is extremely cautious about labeling a block group as distressed unless strong evidence is present. The pattern of false negatives is in line with the fact that distressed areas are a bit rare in the data and the model prioritizes minimizing false positives over catching all distress cases.</p> 6152</section> 6153<section id="random-effects-evaluate-areas-with-unexpectedly-high-risk-of-distress" class="level3"> 6154<h3 class="anchored" data-anchor-id="random-effects-evaluate-areas-with-unexpectedly-high-risk-of-distress">Random Effects (Evaluate Areas with Unexpectedly High Risk of Distress)</h3> 6155<div class="cell"> 6156<details class="code-fold"> 6157<summary>Code</summary> 6158<div class="sourceCode cell-code" id="cb20"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a><span class="co"># get ordered unique GEOIDs</span></span> 6159<span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a>geoid_levels <span class="ot"><-</span> <span class="fu">unique</span>(model_glmm<span class="sc">$</span>frame<span class="sc">$</span>GEOID)</span> 6160<span id="cb20-3"><a href="#cb20-3" aria-hidden="true" tabindex="-1"></a></span> 6161<span id="cb20-4"><a href="#cb20-4" aria-hidden="true" tabindex="-1"></a><span class="co"># extract random effects (each unique GEOID and its random intercept)</span></span> 6162<span id="cb20-5"><a href="#cb20-5" aria-hidden="true" tabindex="-1"></a>ranef_data <span class="ot"><-</span></span> 6163<span id="cb20-6"><a href="#cb20-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">ranef</span>(model_glmm)<span class="sc">$</span>cond<span class="sc">$</span>GEOID <span class="sc">%>%</span></span> 6164<span id="cb20-7"><a href="#cb20-7" aria-hidden="true" tabindex="-1"></a> <span class="fu">
6164as_tibble</span>() <span class="sc">%>%</span></span> 6165<span id="cb20-8"><a href="#cb20-8" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">GEOID =</span> geoid_levels) <span class="sc">%>%</span></span> 6166<span id="cb20-9"><a href="#cb20-9" aria-hidden="true" tabindex="-1"></a> <span class="fu">rename</span>(<span class="at">random_intercept =</span> <span class="st">`</span><span class="at">(Intercept)</span><span class="st">`</span>) <span class="sc">%>%</span></span> 6167<span id="cb20-10"><a href="#cb20-10" aria-hidden="true" tabindex="-1"></a> <span class="fu">relocate</span>(GEOID, <span class="at">.before =</span> random_intercept)</span> 6168<span id="cb20-11"><a href="#cb20-11" aria-hidden="true" tabindex="-1"></a></span> 6169<span id="cb20-12"><a href="#cb20-12" aria-hidden="true" tabindex="-1"></a><span class="co"># join random effects to block groups</span></span> 6170<span id="cb20-13"><a href="#cb20-13" aria-hidden="true" tabindex="-1"></a>st_bgs.ranef <span class="ot"><-</span></span> 6171<span id="cb20-14"><a href="#cb20-14" aria-hidden="true" tabindex="-1"></a> st_bgs_all <span class="sc">%>%</span></span> 6172<span id="cb20-15"><a href="#cb20-15" aria-hidden="true" tabindex="-1"></a> <span class="fu">distinct</span>(GEOID, <span class="at">.keep_all =</span> <span class="cn">TRUE</span>) <span class="sc">%>%</span></span> 6173<span id="cb20-16"><a href="#cb20-16" aria-hidden="true" tabindex="-1"></a> <span class="fu">left_join</span>(ranef_data, <span class="at">by =</span> <span class="st">'GEOID'</span>)</span> 6174<span id="cb20-17"><a href="#cb20-17" aria-hidden="true" tabindex="-1"></a> <span class="co">#mutate(region = sapply(county, get_region)) %>%</span></span> 6175<span id="cb20-18"><a href="#cb20-18" aria-hidden="true" tabindex="-1"></a> <span class="co">#relocate(region, .after = county)</span></span> 6176<span id="cb20-19"><a href="#cb20-19" aria-hidden="true" tabindex="-1"></a></span> 6177<span id="cb20-20"><a href="#cb20-20" aria-hidden="true" tabindex="-1"></a><span class="co"># make map of random effects to see which aras have a higher unexplained risk of</span></span> 6178<span id="cb20-21"><a href="#cb20-21" aria-hidden="true" tabindex="-1"></a><span class="co"># being distressed due to reasons not measured in the model</span></span> 6179<span id="cb20-22"><a href="#cb20-22" aria-hidden="true" tabindex="-1"></a>ranef_map <span class="ot"><-</span></span> 6180<span id="cb20-23"><a href="#cb20-23" aria-hidden="true" tabindex="-1"></a> <span class="fu">ggplot</span>() <span class="sc">+</span></span> 6181<span id="cb20-24"><a href="#cb20-24" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_sf</span>(<span class="at">data =</span> st_bgs.ranef, <span class="fu">aes</span>(<span class="at">fill =</span> random_intercept), <span class="at">color =</span> <span class="cn">NA</span>) <span class="sc">+</span></span> 6182<span id="cb20-25"><a href="#cb20-25" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_sf</span>(<span class="at">data =</span> st_full_study_area, <span class="at">color=</span> <span class="st">'grey80'</span>, <span class="at">fill =</span> <span class="cn">NA</span>, <span class="at">linewidth =</span> <span class="fl">0.1</span>) <span class="sc">+</span></span> 6183<span id="cb20-26"><a href="#cb20-26" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_sf_text</span>(<span class="at">data =</span> cities, <span class="fu">aes</span>(<span class="at">label =</span> NAME), </span> 6184<span id="cb20-27"><a href="#cb20-27" aria-hidden="true" tabindex="-1"></a> <span class="at">angle =</span> <span class="fl">22.5</span>, <span class="at">size =</span> <span class="fl">3.75</span>) <span class="sc">+</span></span> 6185<span id="cb20-28"><a href="#cb20-28" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_sf_text</span>(<span class="at">data =</span> civil, <span class="fu">aes</span>(<span class="at">label =</span> NAME), </span> 6186<span id="cb20-29"><a href="#cb20-29" aria-hidden="true" tabindex="-1"></a>
6186 <span class="at">angle =</span> <span class="sc">-</span><span class="fl">22.5</span>, <span class="at">size =</span> <span class="dv">3</span>) <span class="sc">+</span></span> 6187<span id="cb20-30"><a href="#cb20-30" aria-hidden="true" tabindex="-1"></a> <span class="fu">scale_fill_gradientn</span>(</span> 6188<span id="cb20-31"><a href="#cb20-31" aria-hidden="true" tabindex="-1"></a> <span class="at">colors =</span> <span class="fu">c</span>(<span class="st">'darkblue'</span>, <span class="st">'white'</span>, <span class="st">'red'</span>),</span> 6189<span id="cb20-32"><a href="#cb20-32" aria-hidden="true" tabindex="-1"></a> <span class="at">values =</span> <span class="fu">rescale</span>(<span class="fu">c</span>(<span class="fu">min</span>(st_bgs.ranef<span class="sc">$</span>random_intercept, <span class="at">na.rm =</span> T), <span class="sc">-</span><span class="dv">3</span>, </span> 6190<span id="cb20-33"><a href="#cb20-33" aria-hidden="true" tabindex="-1"></a> <span class="dv">3</span>, <span class="fu">max</span>(st_bgs.ranef<span class="sc">$</span>random_intercept, <span class="at">na.rm =</span> T))),</span> 6191<span id="cb20-34"><a href="#cb20-34" aria-hidden="true" tabindex="-1"></a> <span class="at">limits =</span> <span class="fu">c</span>(<span class="fu">min</span>(st_bgs.ranef<span class="sc">$</span>random_intercept),</span> 6192<span id="cb20-35"><a href="#cb20-35" aria-hidden="true" tabindex="-1"></a> <span class="fu">max</span>(st_bgs.ranef<span class="sc">$</span>random_intercept)),</span> 6193<span id="cb20-36"><a href="#cb20-36" aria-hidden="true" tabindex="-1"></a> <span class="at">oob =</span> squish,</span> 6194<span id="cb20-37"><a href="#cb20-37" aria-hidden="true" tabindex="-1"></a> <span class="at">name =</span> <span class="st">'Random Intercept</span><span class="sc">\n</span><span class="st">(Log-Odds)'</span>,</span> 6195<span id="cb20-38"><a href="#cb20-38" aria-hidden="true" tabindex="-1"></a> <span class="at">na.value =</span> <span class="st">'white'</span></span> 6196<span id="cb20-39"><a href="#cb20-39" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">+</span></span> 6197<span id="cb20-40"><a href="#cb20-40" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span> 6198<span id="cb20-41"><a href="#cb20-41" aria-hidden="true" tabindex="-1"></a> <span class="at">title =</span> <span class="st">'Unexplained Distress Risk by Block Group'</span>,</span> 6199<span id="cb20-42"><a href="#cb20-42" aria-hidden="true" tabindex="-1"></a> <span class="at">subtitle =</span> <span class="st">'Baseline Distress Risk After Controlling for Predictors'</span></span> 6200<span id="cb20-43"><a href="#cb20-43" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">+</span></span> 6201<span id="cb20-44"><a href="#cb20-44" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme_map</span>() <span class="sc">+</span></span> 6202<span id="cb20-45"><a href="#cb20-45" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme</span>(</span> 6203<span id="cb20-46"><a href="#cb20-46" aria-hidden="true" tabindex="-1"></a> <span class="at">plot.margin =</span> <span class="fu">margin</span>(<span class="dv">10</span>, <span class="dv">10</span>, <span class="dv">10</span>, <span class="dv">10</span>),</span> 6204<span id="cb20-47"><a href="#cb20-47" aria-hidden="true" tabindex="-1"></a> <span class="at">legend.position =</span> <span class="st">'bottom'</span></span> 6205<span id="cb20-48"><a href="#cb20-48" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">+</span></span> 6206<span id="cb20-49"><a href="#cb20-49" aria-hidden="true" tabindex="-1"></a> <span class="fu">guides</span>(<span class="at">fill =</span> <span class="fu">guide_legend</span>(<span class="at">nrow =</span> <span class="dv">1</span>)) <span class="sc">+</span></span> 6207<span id="cb20-50"><a href="#cb20-50" aria-hidden="true" tabindex="-1"></a> <span class="fu">coord_sf</span>(<span class="at">expand =</span> <span class="cn">FALSE</span>)</span> 6208<span id="cb20-51"><a href="#cb20-51" aria-hidden="true" tabindex="-1"></a></span> 6209<span id="cb20-52"><a href="#cb20-52" aria-hidden="true" tabindex="-1"></a>ranef_map</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div> 6210</details> 6211<div class="cell-output-display"> 6212<div> 6213<figure class="figure"> 6214<p><img role="img" 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6214dYtihttMThyzuHbKHcwZD+sCH9GGRghH5da7+zKeTj0FvyFHTN780V47KO+uPV1t+8WD55eZAxOnS/g/xbjIQ8ccf32QgkuWYR02hcJ4flv6svxwCRAAAAAAAgHz5Z//wX0bEb/7T3x7WDBFgFHTTIEKOqA9pyx+TZETpxsr7j3ZujkftvY+aqy1HRETt9s8kAxFvHKy2fO+LP1GNKH/pJ663v68+2ntUH8EM8XwNYn8IEAEAAAAAAPJKgwgwgtoOQYRsym4v86S8bOdw6Dw9bNQbyUdPt2fklUpjUbqxsvdoZ/vNg+GGu3fvVCNi9jPN2vDeO8lAxOfG2t7JAIxghlgoFLKZIQoQAQAAAAAAckyDSF40RndR1ZTV9+zJIZHiEMRCITIZJAAcdqb6sKdb0h+ZXHn5OKXSwWe19+5UI8pfvHkQJNZu/8xqPDEQMStGrUGMjkch9m395RAgAgAAAAAA5N2/+q3fGfQmAHAe524QDUGk37ooXLMfx2Z/C4dD58suH5162PiBHGV8w6B0Y6PR2Pm5GwdF4u7aLx4aiJglozkKcdCb8AQBIgAAAAAAQG78s3/4L9te3mwQL37M2R9g6GTrJHtWHG0Qi39elUgWZXyKnReY/jh3ephruRp/+ITihULE2McvFi9cLEbE7uGBiFk0ahniyQ1iP8cfRsTFvn0ngGza3NyMiH6+8gIAAAAA9ELSIP6+3//dg94QgLQNT4uSssKtzzZe+4ppiGRc0sj4OR5Zo7bmclPO68PHLlwsjt14/9GN2m6UjvuS7GhtEJN6kj4QIAKcLokUD9EsAgAAAAD99wf/6O+NJ+cg/qHvbP+LeHpwWwQdaTSGKimAFHVZEA5rfdhoRMaWl6Qr3gNGU+fpYQy8Pnz2h+IbX0vrzvKbHsaR+jBx4WJx71FprP9b04VRqA+TIYhHf83e2nrQ5y0RIALE91z4vhPmICZXfc+F72t7uQwRAAAAAOi/P/Sd7UFvAgCpMcWQoVfIaoMoc+2dM9WHmfDsD0VEfONr+580nSVMzHV6GMfUh4mk5xupNY7zonUt5maM2OeUZfhjT4BOfM+F70syxEPDDo+rDyPiavG3+rRxAAAAAACjoT5c6+4BdK7x2lcGvQlwIm/RdKxYLOSvPvzG1/ZDw0P1YVvH3GaI68OmvIwVzMt29sLW1oP+D9IyAREYaZubm61xYfL5oQaxbX041vjWZv17er15AAAAAABHFXY2T7/RQNTWb7++PbGycH3QG0LW9GH95UKxEANfvbFfOukDyKOkQTQKkWGVwSGIXkxTd+7usJN38MYPfLrwrV853/13qm2D2PzP46+t72Xt6D6bM/1qceFi0RzEzGqdhthPAkSAw9oWh01jjW81P7f+MgAAAACQNf/vP//Xv+/3f/cAvnFt+drEYjWi/PTz12+UBrABjKTCk6FDoVgYkQaRIdY6ClGMSFak9MqawQaR7nU/7DBz793HjUJsvaSTQYk5MXx/2DDK4w8fPNgcSMcyunsc4ND4w1ONNb7VWh9Gy6zEzRZpbiIAAAAAwGn+xe97etCbEBERpedfKkdEVO/crQ16WxgFhWKh0K54aHshkEe9n52aSRl81OltUqYenHeLVORvqeXONRdlHmrDVx/22YAGDrY3qPowTEAERlaHpeCh4rDV1eJvbda/p3k/l57+wYh4+OE3U9k8AAAAgOHQhz/XzNEiFX3+49Uc7RlSN6AhiKUbt+YWZypRvbNWWzADkR7RF0ZEfa8hFwD6JFPNYHqG+DW0b89YWulh5sYfnl1O11/u5ncJqzBziAARGF0njD88oTtsdbX4WxHxO5/446ltEwAAAMDQuXqlhxnc5oPUkr5+tJJn3BXdnMV6kN6eIdM+/sci4t9pd019IKdyr78wG5XVqC6+vn5jZXoAG0A7hcLgp4gVCoXGkY1oTQk7jA86rA9HYSFmDeKIaLz2lc5XYa7/ha8U/7wlm0lV2i+llmAeDilOPcz6+/Vp6yznND2MoR58OLLrLw9w/GEIEIHRdMK/JneYHgIAAADQiatXr25ubva2QUwvHOzpdkIineFtH/9jp96kWCwMokGcvrlUXl2sxuq791amr/f925NdhSh0vUCd2YeHaBA5qv4XvjLoTWC4pB0MZqTV8tJ5bukuuJyj+vDoe6700BBEWgkQgZGzubl5dPZhWt3hpad/cHNzkF05AAAAwEiRDDIQKqgTlGZeKi9Wq1F57fbN65ZhpmN+rM5nVBrEvDYe/aM7JPv8HOdUusVhqy7rw/oznz50SfGDX+nmDtv4xtea32ho3m3TfSAZbBCNPxyUEd3vAE1jjW91Ux9afxkAAACAXnCOlu717oTxIbV7y/PXpi5cKF64MHXt9bhUjoio3rlb68+3J+MKJk3RHe+IjKBMHfZpvIZn6gExBI7Wh8dd2ItvFPkcf1i8UBiajPI4I1sfZkGh0cjfTwXA+STL8Vy9enX3G7+ZXNL94MOjAeLDD7+ZpOXJKEQDEQEAAAB6vQrzUOry3+4HPv+gSymurN0L+dq3PV6IuXZ7/uXF1Wrb62bX6itnX4a5b91k97K+bmCLgZ0PHNyTmaNnJxXnSAr2Hu3vogsXM/xDN1pPYxuFW5/t8JbDOgSx68Xbcys7DzyNH8NM/ShnZ9f2SFp7u6e/kp37bbrzX2m6GYjYZrxiy/tsTuvD3t15doYg9jNAzFptN/B/AbAEM5A/JyR9h/5dsvVmzfqweUkv6sM4WIW5+YlFmQEAAAA4q76dy8hy53f58pVBb0J7Ocrj+uDe/PjiakSUZ5du3bwxXYqI2vr852aSInH1nfWV69OD3UKgrWZ9eOjzbMWIGTu1PxCN177SYYNY/POfHdYGEVKUpde4jOrD77rd/JFAodBp+NUaEXYSI3Y4QFF9eFRGFmI2/nCwTEAEcqb5T6Jtk75DqV/rv58+cfutv9P9lpxp8eXmWEQAAACAEWQC4ll1/w/3Dx50VBZe6e55OXkoUY8H7w1M7gLEHj4R9+YvzFSizaTD2r358ZnViCgvbW/cKJ3tXnO0h/M1Y28ApwQH+kzm69lJRYd5QWtueJxMZIgj9wS213Y3FNslicNaH5qAOHjpTUAspHZ/55ed/do7597DGU8PH9/Jee/jaIbY+arNyZus+vAEg20Q+1wfZjC1G/gERAEikCebm5tXrlyJiAcPHhx99ex00GAf68OHH36z+bkAEQAAABhlGsTO5ehf7U9tAoayQcxRHpfo3bNwb744sxpRXtp+f+FIZLg+fyG5cuf9MxaIOdrD+Urc+npKMAPPYb6enbScGhl0Uh/GwAPEnD91hZYXse6Pw+O+/miDWH/tK3nfdccZ0QYxU4+660Or0fKABIi9do493LffvlJ5d27+clvo46tDHgPEvqWHCQHiYA08QLQEM5Abh8YZHl0dJiP1YWt0GLpDAAAAAM4ieycyGG212r21u+98+FHEUy/eXLjetiS8NNHu4umbS+XVxWp18fV7N54cj8jQG4W4I586rA+bt8zEHMS8KaQa8ZzwhNVfazfvMAvz5Rg+aRxR6sNs6vNffaRbH0ZEo9HoZ4OoPjzZABdiVh9mgQmIQD40Zx8mWn+T6GjwYRrdYeLU2YdJgKg7BAAAADjEEMRO5Ouf7Ds53zd8QxBzNJ+v6VzPQu32/MuLq9XWiw6NM9yfgDi7trcy3e4e9ocgHlmg+RQ52sP5mrHXp1OCmXn28vXspKhtcNB5ethqMAFizp+3FMcfnv+Lc74P2zIEccDSO6gEiH3QyU7OY3oYJ/5O288SMfv6Xx82DaRBFCAOfPxhRPT1OQA41eaTmpec8CWnvJJu/Z1+1ocRcenpH4wONhsAAAAAIKvW56+N79eH5XK5vH9pdXF8/t6R266+e/SyiIiYfnE2ImL1teVab7aSzHHqP5POVx8mX3jurz2n7J3RP5N0xx+eU873IVnkoBoixWIh+ejnN+3PnwQ0WvTh22VT8UIh+Rj0hgyzET6+TiFABDIkKfauHIiDwYfN/2z1r//Vd5KPY+8u1fQwOqsPE5ee/sHkQ4MIAAAAAHTpd//NXvOjp9+odm95/lpx/l7Ubr+2Wo0oL61t1/fe33j//fre9lJSIbbWhNdfnIuIiMo7xxSI40+XIyKqd9YUiNAvrQtEplIQ9qlBbOQ+cspEfQhwjP53h6nrfKT3aMaIGekO+zyMkOywBDOQCc30sJMb/5vffnT0wgsXix/7ty48/u9U08M4S33Y6uGH3xz4qFsAAACA7LAK86ny9U/2HS50NmSrMKd47vYcQeET/wrasd1v/uahS77/mX/36M1qt6cmFqtRn
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
6214+PjLS0uRA9rzR1KFFOUqCfeyS5Beb6gQJEolahw+UODN1uGoPXzS7RrcJgkRRDr+7k1cjQoyxohe+jQ5OUxj7ynCTUpOEKWpKHVeQ0GELWQpmf0BgOIju9SjTCeIxdfSZE+BAOIRFRA/PQhSg3BbCjYqVQOIAqVPoS3AUQrFkQkIiIi8gADiP6po9HD50GlbeJhPBEpiQFEk0St9gwgVuZMBnH8jjNPuWEXAOBjd/zuHzuBogAisH35tIVxAFdv23PVB4y7fvKVv7lmK4BPDd3+zqhx10s7tXv2GDfb23tOwQubRkYAoL1ncLPzJQ8b6W+TKGUiUQARTvWtyvPtBJMUMUQGEB14CQm+Z4dJtGtwmJdvvN71qvHfWq2HhVKsDspED+H5AY+8pwn1Ubt6nx11ZxDNX5nNE7RyXAogouEMoizpQ3AIZiIymVXxJE0f1kS09KGPjBGOwP5LIiIiokDKZDKNZxAF5ltLZSODStvU+NjTdvA0gYg8xoGYA8h+L5GkfZC1j8WcHh8fG0ujNRydKkPY0nXJvBt27So1+0+2mjcXfRjxx4B7Hv7JVR/4EHDy/idPPuKUa7AH+Neva7+J6l8EgJPOGVv0+9at/wVgZCQ+AgBoXza4eWPUqfAhR1gm8l3xdWLmxnbiYKNDHJId3sR0NC2IGURyXf5AzMLm5xxfLJd+TIJ+fLVj+tBVTB8i9yHUGkO0bqOM1aa+GKKr+1Nd1wNyisQKiEQESFL40FAhgGinAqKw0UO/KiAaWAeRiIiIyFViVkB0JHoocAXEups7hH1HPvCglGOteOZCpDwGEA0Stdp71pMiWjdkTT1bdjKI4zvu+OqXb7gnL2XYfvW6r39tZRgAdlwzfdEmAHNv/OmDl/+tdaYZr/8we+tXQ52fe3sgEwAAIABJREFU2pwEOq7rH/7o3wAAnl1+zpfiAE44Y+z6OWHzMa+9MnTk32zde9LMRR/uXlDzeMve9J9J1EkXrAqI8nwvZFW8yfI9iah2BUTPNgtB6+eXaNfgMM/fuF9ZGTvHe/Wli3w5kmywHpuD/vyHA2991/Q6HujXQY5oR/6uYvqwgM0MYoX9e00/PZf2pMVJPE3Li+fZ3MxKVP4QrIBIRCYp0ofl2NwxC5s+JCIiIiJyXCa4hS0C1EbptrpLObrXVeFq6UeJWvSIFMYiiKSCdDoxuG7tlj3ZHqZZi9f0ffi8Y/+2YK5p00MADh7IANi+8kML7x3J/mFe+7xdI7sAYOSevvff8/NH994ZbZn/kaux6R5g9/cf/8/Llx9veZ597zg7m0E8pm3xyZuTLyL5o9H0R7vCADCnb9F74lv/C6+MDb42J3ZE7jFHnLBg1YYF9t5NQMp1EKktk9ELOuObp4V8zyASkQDE2suHQlrl5Jk4kT7x/fkPBwr+W1MG0cfrK5g+DLjKn4kRT6x8dUHxYU8BX+L7QSgOyAqIRCRT+UPkV0CsvPspqIAoePqQFRCJiIiIVOV9+cOC6GGmfBSxwSKIQpY/bLyVQ8A3JR8Zgwo8LSISCjOIkCdQzwqIedKJ5Uu74slSD2jvGfxmiXGODx7IjN/5oZl9IwDmrXv0vpW5WcYT1119oVEQcd66nz+5Mpyb7e9u2Ta09Li8J5kqgvjTTdpnnwDQcV3/5nkY+/ajmPXm2i/9OAmg/SP6JSeYD8n0brD5Tv0KIEp0OBGgCojyfClUrORWy68MomflD8EKiGqRaNfgsLreuHbX/9WvvbXOF/Tvo65wyFd3+tCvw0h/45IFAUQARgCx4P7D33GId8tkg2jH/O5h9LBukxMZOzv3kj9Ab/abdmJ4dk6yjKFpJGquZACRKOjkSh+iaAhmOxlEwaOHqHbicIj72UT2tBERERG5x8sAYsmoYZACiI40cYj2pqQkXbcQz4mIBMQMoiwN94ENIBpv3NpvlO7vbO01s4ftPT2zAAAvxOMj5p3rx4Zj+RnEg3s3nHnKDbtyKcP8F0n/y9nvv2kXgNM+n9hxBTZeHL1lNzD3pp8++ImpeopT6UMA+E3/9bHeFy13tH9kCN/rGnlPx6LTNn/oBPMF7AcQ4VMGUaLDiaAEEOX5RqhYuRAMA4gu8XKzELSufon2Dk4q/661u/5v5YfWl0H093MuedTXSJgvgAHE4vRhSaKlDyHeMb9LmD5skM31xPob9HJ3WTWGp+T4y+AQzEQEqdKHBarum8WPHtrx5v6J6jPleJBWJCIiIiIx1THmciaTaTCDSERERBRkRt+SpmlT6cP2nsFv9kVbpmKGGzeO93e2rh4BMLLlsfFYfgJxPPHQLgBY9tnC9CGA8Ef/4Yv/Fr1lFM/8431PXPGFFdd+9JZl38Hu2+LPf+bO83MzZWZfan3M9be+2Nsdz/2vfdma2+e33j7Z0gIgtKEXNUYPibKCmfhRSLmxCH0ZiFn59KHHzAxD0JKIgVI1ZagSx9OHAWQnfShg9DAImDt0StXh2g3m8Y9Q6UOFMadCFCxGvUMAkUjEvC2vyYlMhf10MDfuTCsSERERBVMd6cPcA5XJIAbx+J8cwfKHRGIyGq8CXgSRZKGnNywx0oc9g2MlhlluiQ1nZqxYsa9vY6zob2M/HwGAeSe15t//65f/BADvXXH9pbdc8RDw0ODP7r7w0kuvWvade4F7B5+48/zzyizNgo2T+/q2j+1Da3RB/svVHT00c5b1PZykx29eac3TQvCvFKKrgpA+JHKJrvtZBLEgWtRg+jAgFfWs3vqu6RUyiCJHDwP4ZVHd7GcQ4eGJTDADKiZGT4gUV5wybGtrGx0dlW7k5ZoEfMtun5lWnDVzNrvciIiIiAJLiQwiTwGIiBTEDCJJYrx/yeoRAOgZLJE+zIpu3Bgt9dixF/P+n80dWpx17lI8tBnYPPjExvPPW/2l0+797DP4xu13rjpv5YnllqilZUFLuQUhqhETXMHgSylE9wQzemiEK9g/Rk7xK4NoxA3NaJG86UO/qjYWRA9FjhsGCmsf+kvXdQ8yiMyocC0nUpmZMrSy3uP3AjaqYFet5/i1PFIzMoh+LwURERER1SyTqVT+MGOvNKLN2Uhcul56IiKSXCQSSY2yvYLENv7YFjN+WPODW1pPBgDsemmsVPoQAI5/3+kAgL3pcaBl/sJ5APDMd3a8DABvfef0+pa6Dmx3DaIghriCq3layJj8XpBGBTN9aGKxWiXp13zFn9f1dc8fCmkcebkmf/7DAWv68PB3HCJX+lDV8odNzSGmD0XgdozE8ScfHZWvehRXdCJllapxOJ7oX9EZCoVWJPxaqtroJaZQk2ZOej6/F5eIyAHG1juVSlXOBKdyvFouIiISlIO5QWYQiYhITMwgkuBy+cP2k2bU/NiDBzKt728HAGx6cHOJ9OE7jzrsnafNnGm558QVn/k0AOz67J07al9Yolow9RFUrmYQm5rcXbFESB/qvmZo2FdGzpL62sZAlT80oodG6FC66CGUTh/6vQhE3uHqTqSUlAWAyO9fyv97C16KjwCIbyufQBxPJMZdXUhbdA6hRkRBZKYPrfcUbNutM0h37QsRETnL2cSg/KMwO8L/zioiIiKSy76XRgAAs2bUMujxwQOZgwcyAFqilxpXkD/wgycK5nnnUYeVeuj87iUA5p0+Ay/XvrQN4kXgQaHxuDjoJK2DKEL6kIjIe3/+wwEZQ4fKY/rQJZmMbkx1PNZmWSu9jArz17EwFchY/hAMIBLJrjiYEvn9S+ZUPH+0b307AMTX9henDMf7V3SGQq3bUEtTmRvYiuWHF/Y+J+NujEgl2c148gFjQn4SMZJ+EpbKiMZ/iYgoyBpPH4byObFQRPJJpaRs0SMKGhZBJMUY0UPDr7VPX38pAOChdV//xdQ8U+nDJx79BgDMu+iCbLPt+V/d//prP3x8xfwTvVhYCh4muEhOTB8GBJPwvpDxY1e1ol5JskcPlfyymD50T+MVRsulCe0EDQv+Wt9Anape1tXs9wIQUT3yIimRSCqVKhk3LKHlw4vbV4+MYGTLY+OxmDVpON6/ZHV8BO3r+6IOL20N1NzSEhHZZeQOC28feXT2HoYOiYgCr9bQIUdVJug6NPbDEZEKsi1gbQFKDGtsKpPEjJPagRHghX3jiNq4sjubPhxPoyX865f/BOCsK7/Y9tAto3jmH6Or/zax/qLTLIUPX97R+/F7AWDJZ64RI2+o67rGowuV8MukMpqnhSYOOn9GOTmpuz0Ks784/jKpqnjt0rSpO3loYOXx+Mv+bnaoGKOHsqg7BehqfHB0NAVpR8Djqk8kn2ztqxyUGGq5gpbYmh4AGFm9Lm8Y5sS61SMA2hd/2Lf6hzw6IqLgKR5euVAufUhERIrJZOqZGhEq4tBbISIi8gjrIJKYWmbMAgCMvLTP1vzTdl47/bDm6ae8/9Ob/5S9670rvvqZ0wAAm6+IdvVu3PHyy+Mvv7zj7hvOvuD0Rd8AcNpVD311fvFTvfWd0xtffgo05kUCo740TPO0kKRjMQcT04fkMesqp+tirYFKVtQrpmd0BdKHKn1ZTc0hpg/r08ioyiqROn0IVkAkko6ZPqz/KaILexCPA/FtiY3RXLXDxLY4APSsifmUPwz63kQIZv5J3r0akZjKbbrNMZd9WCYiIvKVZ3UJg5QyDHaVqMrN/CyCWB7HXyaSTgDrIFINqh4LuLQ/zJVAzGtuteGV/b8H3m3cPv5TO36E+Wd9+Rlg1zfWLPrGmqnZTl+ybuNXV4pR/ZCIgsilUohEVEXuRF6762Z/F4TEpED0kMhgzR0at0teOKF8PHF0VPpWyuB0RRCpo2i7k0589+ud8z+l9T9X7iHjif4VnZ2dnZ2dKxJAtG99OwDE1/aPZ2cw8of+DL+sB7qjUByzZs42Jr8XhEhZqSJg+pCIKEicKmRIVELVfKFotQiIiBrAOohUP93RydTy4cXtAPKaW+2YNaPl+JPeYf73+E/teP3prV9aMu90oxjiafNOX7Luoaf3P14mfcjyh9QQTebyhzywrVHjg4GKXwdR83bA03L8zQNpGi89k5zxFZqThNj2AK/GX1Ymfah8pIyqsrkOqLeqFIzjrED6EKyASKSEMH6xMwlgaDQRm10iQZhY0d
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
6214R0hkoCx/GGJiOUKqA0sfEh24ovLLPEziEQCy0wP9SqRSP/YWCKRSCTG+iNKb6NKhYXkYSTSn0ilAPT0xBfSmra4eKDYnLm653KpLOJqKJEk84P5rz8PoOfqdX2/M/K5r68snzl23x9GAfWBia0b3rGzcXG0C680mRizsfwhkUguvNz2JLHxwvqatcGG19lDIyf0Psvq+NZIsKt3eOf08WTyeDJ5fGZ6+FMJANF9E33FrbMLB1UAg9uKa7Iz+jaD87N9IcO9xmbn40BizwzDxnXZWiWRyHL+iSHayvOXxxk9tIV41y5Y9dBdPryc5b1r6xb2X/ZP52V32y6T596FbevkWwQsfGhO6x2ZW3HxgsWN73XGkofVYTB9ZWkbO3aAnOHyWVx17pDRQ5voYypesJMIY4iWy61wzEIkOL5Jhcbyhz4n5sk/S25QPQ43X64ogrjuVV3FnySHeiNjxRKGPT2FtamxyJAhKBjZUKhfeOtQr2JIHsanypOHmWShfGJ1z+VCWcSeng1gDUQp/f2RsTMA3r37ptetffooAIRid332xPKZY/ddA5yc23Yno6VEDmk4WRLq27+4dGxyMApAnZsbH9g6MLB1YGDX+JwKILqjv5QsTO7dpQLRybuLFREzjx9UAUCdGwgEe4eNRRQj66OAenYJAJBtPYeoPfSEx+ogWoW1D0kEzCB2jtfGSXbm04eK7bUPwfKHniN+9FBzNZbn2JVtEdKH7u6AkY8nFKx5GXj4+TNONJT+LeDsg1xKMUShOi9X5A4bh8FK2zCGKC93TuGYOxSfP7swk/fkVrTS4va+EFFjfJMKzccXC4isoXB07SBn0ofr0our/y5FDy9R1l2ifP7ej25brwwlkZnek0gBPYUg4eKipqX1ZsmG7CAQ7t4IAEilClHFQvLwwGix5iEy072KEukfO5wGULPncmwirWmLi6s3IZkkn/wyAGx9m55UKmQQAYRidz2w7xoAf3G0VATxxNFvO76DDT1/yu09ILJYIKg0iCGGYqOzJ/K58+n5Y5ODg3F9mdw3P39+caR0CE4emQMQ3TFQXFMsiDifno9HoSbGByLBQO/wdDILpM+pxdtlZ26JBAPDx1tNISq3vd3Mb0hO0Rou7d2Rq3g6SGQhdl5uUeOP47rcPmCWtJ0+VBzKHfqHJekLKbowix89NK3F8oddaxtddHOs8CGYPqxFKXJ7R+Tj+edMr3egH6s7r30gUuLOZV1rLJiHsKQIYuu5w2r69hIVRJRiJ53R1XwTq5Vyh84/tN9UfDJ5/oOKqAJDhySXlYt5S8aFRJbjBQISE2sfkov09GEpg5h58fTePYfOANh4z+JsDC/MHz0P7Pq9H55PYdMnv574r79diqSERx+eOhgZSyE1tjc5ekCPm8W2x5FIAOiJLzx8oFaEMH02Vfb/4YEdPWOpsnsJM3korRcf33MMwGXx664qrVv79NELV98IAKHIRuAkzqSzMHZtJSKrrFkbrHltPRBUGg02wqG+8GhfrObPCt2WB+8ZLb1r9ZBhfHss1BdbzM1ms8m9t+5JzI0PzI3rG0Q3dAPZxw+qQBRLjw/vOfgcgI07dk/cEAvxze9NGkMmXqcEFHerPZFomDs0ocnHcU1uH17NFD7k5wGZ1eDtIcLL6uKFPIA1DQOC6KzPcr30ofMXtJk+bEz/i2iapiiKUOXZ7NHp34ITMiY0fVl5+1nVDMPMNWuDsK2TclOlKF6HeTDjzfWA2ZWbrqq/uZv0nKXbeyEKd4IO/APYzZJWyyyCSEQiu7jszsiJ7OH5E075KAZu7wtRDUwfUj3OlD9cjvSu/s9T90UGdiVUNaWqekrw9VcPbARw/tGjwM1Dw28AvvOtn5Q2L3RLBhKHS011SxUNE4UShxUyS2cAAPHtxagLey57SCa1mAKA5xMT7+39wGzyxR+U/fhLRx8AgI0RQwDpxNFvGxcHd5ZIdBdeNvMpEAgogYCSW8nnVsqiIWYKL6HUbXn1mF0tFIrNPrWYz6XnB6NRAMAV3SEc3zuuAlAT4+PPAYCqzo0PdEd6hwVqwq79JZs+ExG1jVUPnebeBQPzbZfJUhamm0Qugmj6t1Qe/JiV+wFcvJDXl4qVFf+oqZP0oTjcTR8Gg4rg6cMSH8w1WFDJVuonKZ/ThL1u33mdRWFptY6ya9YG9SSiOe3mF9tqtdyuUl9mC+/TKkwfVmDEjBphBpGIiIiIqEPsv+wg2y/Rlaoenv3OqaFbfnvoqReTf/e4vqZncDC+IQIAr7v++ssb3Edse1UCcTWV2N87XZkoTA7dOpYC0DM1YciysOeyV5ze+5nnAfRsvKwHSJ35cv/NH1Q+8JGhQ6e/dvjA/8/evce3Ud154/+MZCcpvW13WXphHwqR7CYQLqVbGjlcSoFgxeQCCynb7YaSJkoIxI5JXS6blqWkQJumthMItmzSht22NKQ0CThyw6UFYiuFJ1xKSGgksWG3LNBfdtluH26xpfn9MZYs6zqS5nLOzOf9mj8SaTQ6kiXNnDOf+Z7Bu645b0EfgJnrVjcDKD7/8huv/llb8u/60Dv/YV7rOf8yOU/tGURttuXADzuas25snhfChB9+jS/YMzSUTMUGBjqaEdkeBoBAZyyZGtozNLQnlTrUGQCifS3LB0E6pZya8LH7VJnU50FtpHj4xrmaljtk9NAedsxKW2X6kL8TRnNqviRb2Q94sY+VsvkWM9KHOf8uGEYcOZrKzhoefS+pLcY2RuOClNsESVHzXsU49w9kwOty7HsjDCftI9TU2FJCLRlEnczLHeYQJ4OYSVsyfZjPhimYiUg/bx3PVxPlYu1DJ0n3piZ0qlxQgZ+IaiLsZZQFMX3oPFoG8dWfrA7/Dgj/DL8DcMmu5PascOCnr7jy7zf8888AvLjvdzjntJwtBDu6msKrhhFe290RbNMChMHeXaHwnDAwvKpB2RrqWrOgpQGxge1rt4aHhwGgqWtL24SsIedcdobfPh0GgNlrNi0PAvHf9ly1Zffw/t+H968Nb9TWCCxZ96O7ryv65/7C7BMK5g6JyC6+1qFkayKBifMmB+cvRbivr2X5glhPZj71RGT5ukPzO9qCzUEkuhf1AQh0bhmfuBm+tvs6tza0R8PbIz1F5nsmInKJzDh5TlJcTAwdGqiaWZgzLJyOmbUPRWDSoPJ774xOOYbn0wvLr25oe71DF06+DCCZVGUpgqhx3ETMMr35JLvSicN8mQyi4ZMya3FAJyXw8gOOmXmfs+9y0ks2HA+YnMbwwyqPx7mXDpOjJUeddORK45g+dJJi+6zMGIGzuqAyce41iFQTfi6qwPShtaz4jGYmX/7St9fP3LZ67+8eDgOhu76Rmwo586J5+NlOYP+ex19ddZo2C/NJp/zl2L3+loVNq4aHMbx1IN6WjhUGe2O7cNWc8DAwHF41J7xqfGtNoV1belnq0Ile796yGwDmnqV9hPxfWD70heV47dll310b3g/MWHzgmfDEENOrr70BAMd/4tPa/3PSh2+8+udPfPrD2bf8v2P+j1lFEFn+kBwqOZrKvhy0qtyDz5d7S7Dn5c7909rDLQ3hQCA041TgxXBfFEB4P2J72jCwNQpg6Zq2vEcWo1x9XoWtIiIynvYLWeWc9eUUvDhfu1HYGCKjh8KxJIMoXfrwvbdH7G6C8UwdSBYkg6jzJVr2cSydNbSeLWPaIqQPJaX9vRxxDkiyXQDJq9LoYQ4tiWhIDDGTxrM+ivfc889mQoFVPBZZmcL8u5D3ivbt21fwdirG/qMlMpaq8twwEdOHRBIou7dyRM9TdAUvNGT6kAQnV/lDElli9yBmN+uLecQTiXg8Ab+veTxR4ptz+czVe/fmrbr/rU98+sPA3PYbPr/ze8/g5cHH/33V1SfkrOVvWxNaNSeM4VXrIm296fSiP9g7FOuIDKxbu3W/dsuMGQsXdHCWZed6bd/W/QA+0/WViSNfx5/Zu+nBBesum/PQ5sVd33hsxdjHbnfbeZf+ePwzd0rjl//h2ts+/Znyz2NKBpHpQ3IQb53HiiCLv20oeUlk+aK1fdFwNKrdFlg6cF9P0IfI8vYoEOjsyA20HzoQBRA4udH05hGVoyiOmrCMZGdLQcSchHo+x6QPR0dSdfUOupzO5AyidOlDR3LDHsrYl6hsvkVdfKuhm7QZ04fSFUF0CiPfc+5PqIQa04cZNcYQ7a16qD2vFgqsKIaY3ex9+/blPLbETMrMHVbKQV0IAhSFeyYipg+djOUPiQynKEr26AzThyQ4idKHioflDy1Wyc/X7g1f/GD9KZfO/eHuMismdm+49oL6KR+cfsppc+dfOveU0+qnfPDca++OAwD8192wRFtt/6uv4cU98f1vxfe/lXnsp89bcCYAPDPwxL8V2HRwQQgAEN4emXC73x9s6x1K6+1l+tDJ4sNDwwBmzGo5vsC9wa8sbgL23rRK+5wmNmnpw5mL79jxy20/WHzWzJcO/fzmtsaFax/Le2hi0+wpH/7YlNZHxm/6f28ejZvzKoiEMukDgl9r7Qv2DA0lU6lDsdihWCqZGuoJ+gAM7ggDCCy8JCcXn+he2wcgtKZVd11EcjBpDoTJZh6Poi12N8Qi3jpPicXKljgmfUgFeRRFW/JvtKtJlGFN+vC9d0ateJoieBRQmjjpQ3v3wkl5Bk4zZD4fobD2YT6TqmKTUenDjPpJ3szUzDo99/yzWlDP9kye1oac6ZK15uXPoZzfbO2x2pqZe61puRvwnJgxJDoVWgUPPyYkD6YPHYzpQ8dgXF5ASprdDSEqRfBDbi1xmFnIWmM/X0ff1Xc+YHbjKQCAe7+3IVF8rV+tPPeUS1ffq9WbmxmYOXZz9N5vTp+ycjABYPb8rwMAhn8VjQP+GR/zz/hY5uHJ40MrLgeAZ7+38TcFNh/s6GoCgPDabubCXOrZdRt/D6Dp/M+VTplOfmEAgG/FE7/b9oNfPvfExhXNsy9aufGRJ95+bsdi4LltVy78ySvZ67+x55f3PwNg0azGP3/onf/40Dv/8aF39ty79JSG4xuWPW7eyyFylJx6WsafRPH5fFmzNEe2hwGE8qZfTozNyzw/ty4iFZFi+Mdk7DKSdCyLJDJ9aIZUUtUWe5uRkzLMJBEZPXQhezOI+hX7ziibbzHwWcSZf9muMe1M1jB7yb7XllbJSM5zE3K11lK2ZBC1046OOfmopsaWzL9NUjaGmEn1CRI9zJbJIGbShJlgYtlmZ9YU7UU5AM+MkS7MIBIRkSF0Hv1z/mUiAdn+vRT5AkomDm1VxQejed5iAMDebZEiCcTE3efO3xwFMPP7D7309sh7jz35m7dH3vvdQ1/Xcoib53797jjQfP33AwDwu18P/Afw4h5MjCGed/4iAMCWR54CgH976b+zn8LfsrAJAIa3DjCBaJqnfml3C4r77dNhAMDwxmuVFT3dv3095/50fcS/aQCOeWHgmBcGfBetnD01a42pzRu3LQHw3KOP/eeUuklT6ia92XfL1l//67+ueQ743M2rzsusefjXW/cBuHLBl0x+UURUhXSZw/nNOXdE1hWZlxmAcvV5+TeS86msgERUq+wYooGRREdNWCwG23OHYIFDSdg+WmU2AXf+I0dTgqQP6yd7RU6t2ZJBlLEIYobIf80sZhU+lOPVy8CKd9KEp8iEDrPjhuZFD7PlxxD3H3hh/4EXMvk8YVN62SHC7AKHOpst5ouSHTtFDuGYQDcRUUEsf2gWa3ceTB9Wjnt3IgkwfSiji1etnwkA0W90DRa4O7Hh69/U0ocHf3Nt83g9Kl/z3Y8d/MFMANj7zcV3JeBrvrwJAF7a+uRrAOAZG6wZiyGefe23zgCAn/X2vAogJ4Pob1nT1bUrpg61cZZlV/rCcvXJu2PrZjfNAPbvXtVxrXLuTbPWPRx57fX4a69HHripIbs+4mvPdq/raf/q3L41tyVeAYAjr79z5PV3jvz3KAA0+k8EABx+4pc/vX3hrT8H8PnPnHiS/6//R3uqyD3fHQaa/nkJi6iRswk//3JhiYGDhcscFpuXmYg9ZnmohSZntAxrL5VgUgyRHIAFDqk0K4sgChg9JCkwg2gmwZsnBBEuITA3OWNO+tB2WgxRix6KHDrMIUUjXUWx/hz/vn37nPc5sP2X1ILdsVGTmHiy8tMpkQJF3joeNEhP/PmXvQ/dY3cTTHDpClM376rooQ1XS1vYndP/VAwgZjH3DyR6d95wknyyhG2mCB8Y24+6C3JuADHzJxfxbc8y/tHMTMGc2D2I2c0lAxvxuy6Y/o29AJbsePueiyfel7j73FO+GS14FwAkNnzxtNV7ASx+6L2NzY9fVT9nG4BLdqkPBZHbyYlvOKehPQp8/lu7Bq8+AQBOOuUvK36JVDWRKyBmib/27LrvPhDe//vcO+auUTvOjKfDiNk+99mvfSbYiMjNP30OXwm/dce5Y7cfvm/2ebc/k17pn2I7v+bHE8uOXxLGZ7uGtradaFyjf7/PuG0ZL3nOFVY+nZi7J2vYm63JUUsAMWfO5WxaKsWQv3Lxo6lEAr6Ju61E99kN7VGEBlI9uZURAfsqIKo/fsKW59WpxtFL69NatT6fTcfn7LLrZ8uPZNlPcirdKm3NlBGNFLlcvX4l9gUZJaKKDpuF2caajqYcWZX8hNqcNbTjyd97e8SGZzWBXbukKceYft1Lja8s/2OVP/+yuvjWqrcvTgVEu5tQniH72Up5Zd4vC3x6yMR3VYSBdwNZP0hS+g3UPlOGvclG/7FESB9m02oK2t0KkpVjz5WR4WqchdnjHVvK3khUHfHTh1SpkfeTrkofOpvDuk8W4i8b0QTOOK0lg5zJRMyaW6RmBRqW2H3NFz9Yf8qlc3+4u/Rj/cHLAgCA/jvuzp0BOf5yFABmTi9cmdDXepM2g/PmHb8Cgl+8BADw8PYIAOR0b/ytNy8CgGdu+9GjAD7yl1NKN4vcyX/8mb2b7lCfvHvXysWhGZ9pmvGZphmfCa27W+04E3h2nVYKceWa2M/ujv1sza6uUKipafJzP/7p7Tf/9DkAX5t9bmZL//Z45BkAXwk/G/uXK0OrzvcD8b67wwC+eq170odExtKTR6mZLzc0n3h4axSF5mUmR6m1v8f+IhVSNujg8Sjakvmv+Y2SQ42lEDkLsyHMiiwU2iorHZKYrCl8WEv6EED9JP7i6cWJmB1B2KFREQl4iaawU4nmTLgsiM+ecea+NLvbQvLh8YFDCHsxgM6IIWOIRJTDhdFDBw9TinlkT0SSYgbRMPk9iLFB7mLvsCLYcFvhlvh807V/3PtQobmVs9e89uavAwD2PrgrMeGeeOxAmee+eO4SAED/zt3A2X/fdRoAhNd2jyUZf/NAZs3/evNLc74KYObnZ0w6wvQhlfHJ4BWX9G66Y2jTHUOb7uj9wicB4LdPhwHMWLxlw23+K1f4r7wt2NbbOzQ0pKq7QgDQ1PVP52c28OSGW58DPnv7snNP8n/1Z71fvQB4deCh5wA04d+WzVs4a97CWTf8OHL4VVtenoNx3
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62146thSkPhHXByjJyLHY/nDGpi0WxXlgJxswfQhEdVOncju5lD1nD84Jj9ZptcQJOPleKkr2jNTMxtl0o5uYzcIIJVM6VwMf2qRFZvwqiYFZ6LSfeqmuvO5ShqAxK6DUQBL5zeXexTVIn8AmcdgRC7BAyySRokztcZsvRBGD7OJf0a8BMWMA2UiSivcF40smxMG0NQVG+ptC/rTN6u+1i2dAQCIbn04YVUTa2J/v4g7I+expsutJ3pYV+/RFrMbYypZjlJkH2Zh+pCIbMbyh+JSRDhmJmt477vNsudi+pDkZkYfRbYehcfr0Ra7G0K6cPSJqBac36kYLYZoYBKxugyiLLFCoQbfDM4gGvEVUUoquEL2w32tPcnR2KF7ytU/JHMwhlgjlj8k8bHjR5IptlcqGy9TPEpm0blp9rfzyXJ2v1Ksg7fe09EAACAASURBVEhUNY9HKdYLjWwPA0BoTZsfyLrcHEgcOqitEm1fF7GooURCMrUAQ9nNOiB3aAjLBj04ukJEJD6WP6wWd3Li8kqb+2H6kLKpt95ndxOoJswgykLkMXKR20bZZBlr8tZ5WAfRYgYmEfMziE4qXujkDKLJ9J3q9flMa0CNE8Q7Q9kPMGOIRA5WZ3cDiCqmqoW7mopHUfMuMKqs7p2qQpIcWtXHytljPalkqoqhH49XkfH4Kf+zkU9RFB7x2IgXCApO+23MfEUyP5VaoDCROHQIjY2+8Z6boiQO7c9eJy0RWb6oJRxN/ze8I9ITlOFyM9Wc2gW6SLFjohqpqmrsIYie9KGBTycC7TIJYY9S3HuIsb7V7haQM9U9xhQCGc+x6UMnHEzaeTRO1mP0kKiUYoPjpFt2f1mOkwHWUhQRO7D8Q5FJtAyiIXkvj0fhOQ6dsjOIngc6q9uI8o1Lk9/7hUEtIiJj6P85zRyP8WBMJ5Y/JCkwgEiOojNuWDCqqBl5PzlpitfQRoklJ7lo6EU/4h4f6EkfEgkllYJHpGhQ5vg/pyOQiHSvW9s+niYEAqHO+3rafICq+hquCISuWDP/kubx1TPZw0BnbA0WtbRHEV7b3RFsM++SMwPxrKeZvrvU7hbYz8BTIG7O0wt4pYRj/hqlx4+SoylWDiAiEhEPYEk2rkkfsoNJROIqkUEsm4AVNoBV98T9xe5Sv/T3VraEamTGsI+BMUSqVCaMWEES8emnzGqNMIQaZzMmXCvozoGMVN2vqOGlGYjIRgwgkpRMus5z5Ch7F7Wo+uBRgZmTO1eUPtQOcdyc2yDKV+z3NtE9q6E9nT0MBBCNAoiG2xvCBwdSPUEg2DaUKW2YnT0Mda7paAv6kFgYaI9GEd36cKJNjgSiLaeI2PPKl7opbHcTTKftiarreOvci42OpJxXBLFSon2/zDscskaBsVGWP6zZ6Ah7KEQWcWb5Q7l3LO4i0d/KkICg977bzNs4OY/E8y8r5pxuZxFEMl9FdRCFDR3qpDz+M7ubUMSFX7G7BVWqq/eY0Zk1+4rT/LxXpWEaFkGsha4koguihw7E74Q71JLhZgaxLJY/JFm4/aQjycvwhFgmfXj0vaTBm6YyVPMOP6urfagokkzFTWS+sunDQOdALJVKDQ2lUrGBzgAA4MUdkUT2ypHlsxq09GGgcyA21NMW9AGAr/FUAEC0fV3EvFdgOHaXTcDyh0WoqlpRJr7S9UdHUtpSedOEpj/Gp6qmlyd0zEUNOoeQkqMpbTG7PWNWb7DoiYhIBA/ebd62nZk+dBrh9qkcN6hFwaAh04dCUW75R7ubMEbi9CFN5PF6PF6ekHKaVEpl3IoKMvyqV1vmu/DWebRF/0M8HsWjb642KiZ1Rbu25N6Rlz703vB3FrXJDkKV5OSnmkqr/eOqphnSHiKyCysg0gTq38lUpMTASz1Z+9B5OO0yUY1K/cAmuheNpQ9jQ+O1C33BtqFYYwTB4IRyhpHl6fBh9soTSDQNM4D0uU8rutw8p2kG6aY6y/S6S5zkrrFn7oxqiKmkmokeVjQRc4k3r8bvoJsHTISaKYaIHOKya03NIDqQXEc87ubOv1Vy0bey6yAyfUjO5OIeATlAsSKIqsoZhMh1Kh3iYClEQ4zXROy4rNg62RnE5Pd+YXqbrCXU8Bo/1VSMsWHZWuaGciqWPySJiLLTIqqOSf1cwYsgCnXVi5iYPpQO+y2iKX1sn3h4axQAQmvyQoO+nPQhENkRBoBA5325KycOvZj+p2RFEGulptTMUvvWUkm12FL7xq3A8oe6qcXZ3TR75H/gsz/2hkxnrJVILLsUe6BjVHfwyUNWIoIJhU+InMfN51UyoUO3pg8ddLxI1nNSf4Nk49pRCKKKaKUQM4vdzZFYifShGwg1vMZPMuUz6SPq5rMeOZg+JLlwIJikV/veh+UPHcao9KFRV1corj6bQFIa++wnEpHu7uXLly9f3j1xRmUcOhgFgMD0xvIbS6cMT23MjR92L2qPItA5Nm9zeO3ynGcRXsU/NQVDh9lhxJyl9Nb0pAwlyCDalD4U/n0hXQpGDG382JeOJFrG2HfA0vmUiYjKuuxaM7bqwPmXFXeH2gyipJn7LKZuXQbJRd9ya/pQw64J1YAnZcl8+btBpgFIP16MlI1JxOpUlD509ozMguBnmLKZlj60f4ydiKrDg78qebffZXcTaFwte6Bi6UPBiyBSMcbWPix7pkEppMBqUIot/z979x7fVH3/D/x1koKg369uA8fmLihJqiCIivg1rahMxYRw88Z3yrTKaCgITdOuiNL99t33i3hBKC1KS1Iv1a+X4Y1bScUpU2wPE3Ff5aZNojL3ndOJm/pVC22S3x8nPT3Nrbmc+3k/H+eh6cnJySc0zbm98n6LOFpCRMEwAMINFaUmm83l9fp8Pp/P67KZShvyiwdaiicAAHwrBSsIBypKbV4W9vrHPE5PRzAYjEY7mhNrJ+pLHp9OsWja0oYaSBYSIot0GUTub0SUIojZEOuESE5/2ik/DZIrQeaHyx1S9JAQokbSZBB1hY4yxSA8tE954E8NoQghhBgQpQ9JriiDmIzyW1ky1V6TR+1DXWYQ6QQdMaxoNGbwBnpU/pBoDu35EUOj2oc6I1vn5czXG3K6DpEhm5jfJNJLJAbFMAACFaU2r4+rcWi32+N3sV5bRV+X5OKx
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class="img-fluid figure-img" style="width:1728cm"></p> 6215</figure> 6216</div> 6217</div> 6218</div> 6219<p>A variance of 8.415 and standard deviation of 2.901 in the random effects show a high level of between-group variability, which suggests that unobserved block group-level factors play a large role in predicting distress in this model. It was thus justified to include the random intercepts to account for this heterogeneity.</p> 6220<p>The map highlights spatial variation in unexplained distress risk across block groups. Several areas of the Southern Tier exhibit significantly higher baseline distress risk (in red) even after controlling for socioeconomic, demographic, and built environment predictors. This suggests the presence of unmeasured local factors contributing to persistent distress. Conversely, pockets of lower-than-expected risk (in blue) indicate potential resilience factors that buffer against socioeconomic vulnerabilities. These spatial patterns warrant further investigation to i
6220dentify contextual drivers of distress beyond those captured in the current model, as well as methods and policy implementations that could potentially offset the effects of poverty, unemployment rate, and other socioeconomic conditions.</p> 6221<p>Most of the larger cities in the Southern Tier (Binghamton, Jamestown, Ithaca, and Elmira) and other urban centers (such as Oneonta and Olean) express a particular âurban divideâ among its census tracts, where pockets of neighborhoods have lower-than-expected distress risk and others have higher-than-expected distress risk. There is also an urban/suburban divide between more stable suburban block groups and socioeconomically vulnerable urban block groups. A vast majority of the higher-risk block groups were urban in nature. A handfull of rural areas in each county exhibited higher-than-expected distress risk, including the entirety of the Allegany Indian Reservation centered around the small but vulnerable urban core of Salamanca. Still, urban cores were significantly more likely to be distressed than rural areas.</p> 6222</section> 6223</section> 6224</section> 6225<section id="discussion-and-conclusions" class="level1"> 6226<h1>Discussion and Conclusions</h1> 6227<p>The model revealed several statistically significant predictors of distress that align with and extend existing literature on rural vulnerability. Notably, the percentage of individuals not working in the past year emerged as a robust predictor, with both a block groupâs own unemployment rate and the spatially lagged rate in neighboring areas being associated with significantly increased odds of having distressed status. This finding underscores the importance of accounting for spatial spillover effects; communities do not exist in isolation, and economic hardship in one area often correlates with, and potentially contributes to, hardship in adjacent areas. The spatial lag term specifically highlights the diffusive nature of socioeconomic stress, validating recent methodological recommendations to include spatial structure in GLMMs (Dormann et al., 2007; Zuur et al., 2009).</p> 6228<p>In addition, the model identified population density, divorce rate, and urban development intensity as significant positive predictors of distress. These findings complicate conventional narratives that associate urbanization and density with economic opportunity. In the context of the Southern Tier, where many small cities and formerly industrial urban areas have suffered from long-term economic decline, these variables may reflect concentrated disadvantage, aging population and/or infrastructure, and systemic disinvestment. Conversely, higher average rent was associated with slightly lower odds of distress, potentially serving as a proxy for neighborhood stability or housing demand, although the effect was relatively modest.</p> 6229<p>The inclusion of year-group fixed effects also confirmed a significant temporal shift: block groups in the 2019â2023 period were far more likely to be classified as distressed, even after accounting for structural and spatial factors. This supports evidence that distress risk has especially been increasing in recent years, likely due to COVID-19 disruptions, continued population decline, and limited regional economic diversification.</p> 6230<p>Despite the strong predictive performance of the model, which achieved an accuracy rate of 92% and a specificity above 99%, some limitations remain. Sensitivity was lower at ~28%, suggesting that an overwhelming majority of distressed block groups may remain undetected by the model. These false negatives could reflect areas where unmeasured protective factors or data limitations obscure underlying distress. Additionally, spatial autocorrelation remains present in the residuals, indicating that the model may not fully capture all spatial dependencies. Incorporating spatially structured random effects or moving toward spatial error models may be necessary to account for latent clustering in future research.</p> 6231<p>Moreover, the method of interpolating variables across changing block group boundaries introduces further limitations. This study used population-weighted areal interpolation through centroid assignment which may produce bias in high-variance areas. As Hallisey et al. (2017) note, centroid-based interpolation can contribute to large absolute errors in count data. Future work could explore hybrid approaches, including combined population and areal weighting or dasymetric interpolation using ancillary data like land cover, to enhance spatial accuracy.</p> 6232<p>By applying the ARCâs Distressed Areas Classification System at the census block group level, this study tested its efficacy in identifying localized socioeconomic vulnerability. While the ARCâs original formulation is effective for broader regional comparisons, it may miss important within-county variation in distress patterns, particularly in geographically and demographically heterogeneous areas like the Southern Tier. The results suggest that the ARCâs thresholds alone (67% of national median family income and 150% of the national poverty rate) do not identify many of the most vulnerable communities. The random intercepts in the GLMM, which are interpreted as âunexplainedâ risk after accounting for observed predictors, revealed significant residual variation across block groups. In other words, certain block groups experienced higher or lower distress risk than would be predicted solely from ARC-defined economic indicators and the modelâs covariates.</p> 6233<p>Mapping these random intercepts revealed both inter-county variation and stark intra-urban disparities. While rural pockets in counties like Allegany, Delaware, and Schuyler exhibited elevated unexplained distress, several cities (including Binghamton, Ithaca, Jamestown, Elmira, and Oneonta) showed a pronounced internal divide. In these cities, some block groups had much higher-than-expected baseline distress risk, while adjacent ones had negative random effects, indicating lower-than-expected risk. This âurban divideâ suggests that distress may concentrate in historically segregated or economically isolated neighborhoods within cities, reinfor
6233cing the idea that urban centers in rural regions are not uniformly distressed but fractured by block-level variation in resilience, opportunity, and historical disinvestment. These observations raise important questions about the adequacy of county-averaged or citywide metrics for targeting interventions. While the ARCâs framework provides a valuable starting point, the findings here emphasize the value of block group-level analysis to surface hidden pockets of vulnerability or resilience that are invisible at coarser scales.</p> 6234<p>To build on these insights, future analyses should explore additional explanatory variables. Potential protective factors may include social capital (e.g., volunteerism, civic engagement), access to green space, proximity to community services, or the presence of mutual aid networks. Structural variables such as redlining history, credit access, school quality, or broadband availability may also help explain residual variation. For instance, changes in volunteerism over time, particularly during crises, offer natural experiments that could be exploited to assess causal effects on community vulnerability. Studies like Makridis and Wu (2021) have demonstrated that counties with greater civic engagement and volunteerism had stronger economic resilience during the COVID-19 pandemic, suggesting shifts in social capital could be causally linked to socioeconomic outcomes. Additionally, the sudden expansion of mutual aid networks during the pandemic provides a quasi-experimental setting to evaluate whether localized increases in social support structures reduced vulnerability. One such study by Xu and Zhang (2025) utilized structural equation modeling to analyze data from participants in mutual aid groups in China during the pandemic. The research found that participation in mutual aid significantly enhanced individualsâ subjective well-being. This effect was mediated through increased access to material resources and the expansion of social networks, which in turn boosted self-esteem and self-efficacy. The study highlights the mechanisms by which mutual aid can reduce vulnerability and promote resilience in times of crisis.</p> 6235<p>Integrating dynamic measuresâsuch as industry closures, pandemic impacts, or population churnâcould further improve temporal responsiveness and causal interpretation. For example, differences-in-differences designs exploiting the staggered rollout of broadband access across rural areas (Bertschek et al., 2016) could be adapted to assess how changes in structural resources affect economic distress trajectories. Methodologically, sensitivity could be improved through more flexible classification thresholds, bootstrapped uncertainty intervals, or temporal interaction terms. Moving beyond cross-sectional associations to leverage these natural variations would offer stronger evidence on causal pathways and actionable intervention points.</p> 6236<p>This study contributes to a growing body of research calling for more spatially nuanced approaches to understanding and addressing rural economic distress. By integrating predictive modeling with the ARC classification framework, it underscores both the value and the limitations of existing policy tools and demonstrates the power of glmmTMB-based modeling for applied regional analysis. The inclusion of spatial random effects and the visual analysis of residuals provide a roadmap for targeting interventions more precisely, improving both the scientific understanding and the policy relevance of distress classification systems.</p> 6237<div style="page-break-after: always;"></div> 6238</section> 6239<section id="references" class="level1"> 6240<h1>References</h1> 6241<section id="works-cited" class="level2"> 6242<h2 class="anchored" data-anchor-id="works-cited">Works Cited</h2> 6243<ol type="1"> 6244<li><p>Appalachian Regional Commission. (2023). County Economic Status and Distressed Areas by State, FY 2024. <a href="https://www.arc.gov/about-the-appalachian-region/county-economic-status-and-distressed-areas-by-state-fy-2024" class="uri">https://www.arc.gov/about-the-appalachian-region/county-economic-status-and-distressed-areas-by-state-fy-2024</a></p></li> 6245<li><p>Appalachian Regional Commission. (2024). Distressed Areas Classification System. <a href="https://www.arc.gov/distressed-areas-classification-system/" class="uri">https://www.arc.gov/distressed-areas-classification-system/</a></p></li> 6246<li><p>
6246Barrett, M., Aslebagh, S., Vuong, V., et al. (2023). Spatially resolved air pollution models identify disparities in exposure by socioeconomic status. <em>European Respiratory Journal</em>, <em>62</em>(67). <a href="https://doi.org/10.1183/13993003.congress-2023.PA1608" class="uri">https://doi.org/10.1183/13993003.congress-2023.PA1608</a></p></li> 6247<li><p>Bertschek, I., Briglauer, W., Hüschelrath, K., Kauf, B., & Niebel, T. (2016). The Economic Impacts of Broadband Internet: A Survey. <em>Review of Network Economics</em>, <em>14</em>(4), 201â227. <a href="https://doi.org/10.1515/rne-2016-0032" class="uri">https://doi.org/10.1515/rne-2016-0032</a></p></li> 6248<li><p>Bivand, R. S., & Piras, G. (2015). Comparing implementations of estimation methods for spatial econometrics. Journal of Statistical Software, <em>63</em>(18), 1â36. <a href="https://doi.org/10.18637/jss.v063.i18" class="uri">https://doi.org/10.18637/jss.v063.i18</a></p></li> 6249<li><p>Brooks M.E., Kristensen K., van Benthem K.J., et al. (2017). glmmTMB Balances Speed and Flexibility Among Packages for Zero-inflated Generalized Linear Mixed Modeling. <em>The R Journal</em>, <em>9</em>(2), 378â400. <a href="https://doi.org/doi:10.32614/RJ-2017-066" class="uri">https://doi.org/doi:10.32614/RJ-2017-066</a>.</p></li> 6250<li><p>Dormann, C. F., McPherson, J. M., Araújo, M. B., et al. (2007). Methods to account for spatial autocorrelation in the analysis of species distributional data: A review. <em>Ecography</em>, <em>30</em>(5), 609â628. <a href="https://doi.org/10.1111/j.2007.0906-7590.05171.x" class="uri">https://doi.org/10.1111/j.2007.0906-7590.05171.x</a></p></li> 6251<li><p>Ellis, G.F., Burk, D., Roberts, F. (2025). ipumsr: An R Interface for Downloading, Reading, and Handling IPUMS Data. R package version 0.8.2, <a href="https://github.com/ipums/ipumsr" class="uri">https://github.com/ipums/ipumsr</a>, <a href="https://www.ipums.org" class="uri">https://www.ipums.org</a>, <a href="https://tech.popdata.org/ipumsr/" class="uri">https://tech.popdata.org/ipumsr/</a>.</p></li> 6252<li><p>Flowerdew, R., & Green, M. (1992). Developments in areal interpolation methods and GIS. The Annals of Regional Science, 26(1), 67â78. <a href="https://doi.org/10.1007/BF01581870" class="uri">https://doi.org/10.1007/BF01581870</a></p></li> 6253<li><p>Galili, T., OâCallaghan, A., Sidi, J., & Sievert, C. (2017). heatmaply: an R package for creating interactive cluster heatmaps for online publishing. <em>Bioinformatics</em>, <em>34</em>(9), 1600-1602. <a href="https://doi.org/10.1093/bioinformatics/btx657" class="uri">https://doi.org/10.1093/bioinformatics/btx657</a></p></li> 6254<li><p>Gelman, A. & Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press. <a href="https://doi.org/10.1017/CBO9780511790942" class="uri">https://doi.org/10.1017/CBO9780511790942</a></p></li> 6255<li><p>Goodchild, M. F., & Lam, N. S. (1980). Areal interpolation: A variant of the traditional spatial problem. <em>Geo-Processing</em>, <em>1</em>(3), 297-312. <a href="https://www.researchgate.net/publication/239654534_Areal_Interpolation_A_Variant_of_the_Traditional_Spatial_Problem" class="uri">https://www.researchgate.net/publication/239654534_Areal_Interpolation_A_Variant_of_the_Traditional_Spatial_Problem</a></p></li> 6256<li><p>Gregory, I. N. (2002). The accuracy of areal interpolation techniques: standardising the crosswalk. <em>Computers, Environment and Urban Systems</em>, <em>26</em>(3), 293-314. <a href="https://www.researchgate.net/publication/223083878_The_accuracy_of_areal_interpolation_techniques_Standardising_19th_and_20th_century_census_data_to_allow_long-term_comparisons" class="uri">https://www.researchgate.net/publication/223083878_The_accuracy_of_areal_interpolation_techniques_Standardising_19th_and_20th_century_census_data_to_allow_long-term_comparisons</a></p></li> 6257<li><p>Hallisey, E., Tai, E., Berens, A., et al. (2017). Transforming geographic scale: a comparison of combined population and areal weighting to other interpoaltion methods. <em>International Journal of Health Geographics</em>, <em>16</em>(29). <a href="https://doi.org/10.1186/s12942-017-0102-z" class="uri">https://doi.org/10.1186/s12942-017-0102-z</a></p></li> 6258<li><p>Hartig, F. (2024). DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models. R package version 0.4.7, <a href="https://github.com/florianhartig/dharma" class="uri">https://github.com/florianhartig/dharma</a>.</p></li> 6259<li><p>Hijmans, R. (2025). terra: Spatial Data Analysis. R package version 1.8-6. <a href="https://github.com/rspatial/terra" class="uri">https://github.com/rspatial/terra</a></p></li> 6260<li><p>Johnson, K. M., & Lichter, D. T. (2019). Rural depopulation: Growth and decline processes over the past century. Rural Sociology, 84(1), 3â27. <a href="https://doi.org/10.1111/ruso.12266" class="uri">https://doi.org/10.1111/ruso.12266</a></p></li> 6261<li><p>Jokela, M., Batty, G. D., Vahtera, J., Elovainio, M., &
6261 Kivimäki, M. (2013). Socioeconomic inequalities in common mental disorders and psychotherapy treatment in the UK between 1991 and 2009. The British Journal of Psychiatry, 202(2), 115â120. <a href="https://doi.org/10.1192/bjp.bp.111.098863" class="uri">https://doi.org/10.1192/bjp.bp.111.098863</a></p></li> 6262<li><p>K C, S., Gyawali, B. R., Lucas, S., Antonious, G. F., Chiluwal, A., & Zourarakis, D. (2024). Assessing Land-Cover Change Trends, Patterns, and Transitions in Coalfield Counties of Eastern Kentucky, USA. <em>Land</em>, <em>13</em>(9), 1541. <a href="https://doi.org/10.3390/land13091541" class="uri">https://doi.org/10.3390/land13091541</a></p></li> 6263<li><p>Ludke, R. L., Obermiller, P. J., & Rademacher, E. W. (2012). Demographic Change in Appalachia: A Tentative Analysis. <em>Journal of Appalachian Studies</em>, <em>18</em>(1/2), 48â92. <a href="http://www.jstor.org/stable/23337708" class="uri">http://www.jstor.org/stable/23337708</a></p></li> 6264<li><p>Makridis, C. A. & Wu, C. (2021). How social capital helps communities weather the COVID-19 pandemic. <em>PLOS ONE</em>, <em>16</em>(1). <a href="https://doi.org/10.1371/journal.pone.0245135" class="uri">https://doi.org/10.1371/journal.pone.0245135</a></p></li> 6265<li><p>Manson, S., Schroeder, J., Van Riper, D., et al. (2024). IPUMS National Historical Geographic Information System: Version 19.0 [dataset]. Minneapolis, MN: IPUMS. <a href="http://doi.org/10.18128/D050.V19.0" class="uri">http://doi.org/10.18128/D050.V19.0</a>.</p></li> 6266<li><p>McMahon, E.J. (2024). Eight in 10 New York towns and cities have lost population since 2020. <em>Empire Center</em>. <a href="https://www.empirecenter.org/publications/eight-in-10-new-york-towns-and-cities-have-lost-population-since-2020" class="uri">https://www.empirecenter.org/publications/eight-in-10-new-york-towns-and-cities-have-lost-population-since-2020</a></p></li> 6267<li><p>Partridge, M. D., Rickman, D. S., Ali, K., & Olfert, M. R. (2008). Lost in space: Population growth in the American hinterlands and small cities. Journal of Economic Geography, 8(6), 727â757. <a href="https://doi.org/10.1093/jeg/lbn038" class="uri">https://doi.org/10.1093/jeg/lbn038</a></p></li> 6268<li><p>Thiede, B. C., Lichter, D. T., & Slack, T. (2018). Working, but poor: The good life in rural America? Journal of Rural Studies, 59, 183â193. <a href="https://doi.org/10.1016/j.jrurstud.2016.02.007" class="uri">https://doi.org/10.1016/j.jrurstud.2016.02.007</a></p></li> 6269<li><p>U.S. Geological Survey. (2024). Annual National Land Cover Database (NLCD) Collection 1 Science Products (2008â2023) [Data set]. Multi-Resolution Land Characteristics Consortium (<a href="https://www.mrlc.gov/sites/default/files/docs/LSDS-2103%20Annual%20National%20Land%20Cover%20Database%20%28NLCD%29%20Collection%201%20Science%20Product%20User%20Guide%20-v1.0%202024_10_15.pdf">MRLC</a>).</p></li> 6270<li><p>Walker, K. (2023). â7.3 Small area time-series analysis,â in <em>Analyzing US Census Data: Methods, Maps, and Models in R</em>. CRC Press: Boca Raton, Florida, <a href="https://walker-data.com/census-r/spatial-analysis-with-us-census-data.html#small-area-time-series-analysis" class="uri">https://walker-data.com/census-r/spatial-analysis-with-us-census-data.html#small-area-time-series-analysis</a></p></li> 6271<li><p>Walker, K. & Herman, M. (2024). tidycensus: Load US Census Boundary and Attribute Data as âtidyverseâ and âsfâ-Ready Data Frames. R package version 1.6.6. <a href="https://walker-data.com/tidycensus" class="uri">https://walker-data.com/tidycensus</a></p></li> 6272<li><p>Xu, A., & Zhang, Y. (2025). The effect and mechanism of mutual aid on the subjective well-being of participants under the COVID-19 pandemic. BMC Psychology, 13, Article 48. https://doi.org/10.1186/s40359-025-02360-5</p></li> 6273<li><p>Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A., & Smith, G. M. (2009). Mixed Effects Models and Extensions in Ecology with R. Springer Science & Business Media. <a href="https://doi.org/10.1007/978-0-387-87458-6" class="uri">https://doi.org/10.1007/978-0-387-87458-6</a></p></li> 6274</ol> 6275<div style="page-break-after: always;"></div> 6276</section> 6277<section id="data-sources-and-methodology" class="level2"> 6278<h2 class="anchored" data-anchor-id="data-sources-and-methodology">Data Sources and Methodology</h2> 6279<p><u>2009 U.S. Data</u></p> 6280<ul> 6281<li>Total Population: <a href="https://api.census.gov/data/2009/acs/acs5?get=B02001_001E,NAME&for=us" class="uri">https://api.census.gov/data/2009/acs/acs5?get=B02001_001E,NAME&for=us</a></li> 6282<li>Median Family Income: U.S. Department of Housing and Urban Development. (19 March 2009). âEstimated Median Family Incomes for Fiscal Year 2009,â PDR-2009-01. <a href="https://www.huduser.gov/portal/datasets/il/il09/Medians2009.pdf" class="uri">https://www.huduser.gov/portal/datasets/il/il09/Medians2009.pdf<
6282/a></li> 6283<li>Poverty Rate: Bishaw, A. & Macartney, S. (September 2010). âPoverty: 2008 and 2009,â American Community Survey Briefs, ACSBR/09-1. U.S. Census Bureau, Washington, D.C. <a href="https://www2.census.gov/library/publications/2010/acs/acsbr09-01.pdf" class="uri">https://www2.census.gov/library/publications/2010/acs/acsbr09-01.pdf</a></li> 6284</ul> 6285<p><u>2010 U.S. Data</u></p> 6286<ul> 6287<li><p>Total Population: U.S. Census Bureau. (2010). 2010 Summary File 1 Tables [All 50 States & DC]. <a href="https://api.census.gov/data/2010/dec/sf1?" class="uri">https://api.census.gov/data/2010/dec/sf1?</a></p></li> 6288<li><p>Median Family Income: U.S. Department of Housing and Urban Development. (14 May 2010). âEstimated Median Family Incomes for Fiscal Year 2010,â PDR-2010-01. <a href="https://www.huduser.gov/portal/datasets/il/il10/Medians2010.pdf" class="uri">https://www.huduser.gov/portal/datasets/il/il10/Medians2010.pdf</a></p></li> 6289<li><p>Poverty Rate: Bishaw, A. (September 2012). âPoverty: 2010 and 2011,â American Community Survey Briefs, ACSBR/11-01. U.S. Census Bureau, Washington, D.C. <a href="https://www2.census.gov/library/publications/2012/acs/acsbr11-01.pdf" class="uri">https://www2.census.gov/library/publications/2012/acs/acsbr11-01.pdf</a>, pg. 3.</p></li> 6290<li><p>2009 & 2010 Per Capita Income and Havenât Worked Population: <a href="https://apps.bea.gov/iTable/?reqid=70&step=30&isuri=1&major_area=0&area=xx&year=2018,2017,2016,2015,2014,2013,2012,2011,2010,2009,2008,2007,2006,2005,2004,2003,2002,2001,2000,1999,1998,1997,1996&tableid=21&category=421&area_type=0&year_end=-1&classification=non-industry&state=0&statistic=3&yearbegin=-1&unit_of_measure=levels">U.S. Bureau of Economic Analysis</a>.</p></li> 6291</ul> 6292<p><u>American Community Survey (ACS) Data</u></p> 6293<ul> 6294<li>U.S. Census Bureau. (2009-2023). American Community Survey 5-Year Estimates: Comparison Profiles 5-Year. <a href="http://api.census.gov/data/2010/acs/acs5" class="uri">http://api.census.gov/data/2010/acs/acs5</a></li> 6295<li>U.S. Census Bureau. (2010). âSelected Economic Characteristics.â American Community Survey 5-Year Estimates Subject Tables, Table DP03. <a href="https://data.census.gov/table/ACSDP5YSPT2010.DP03" class="uri">https://data.census.gov/table/ACSDP5YSPT2010.DP03</a></li> 6296<li>U.S. Census Bureau. (2019). âSelected Economic Characteristics.â American Community Survey 5-Year Estimates Subject Tables, Table DP03. <a href="https://data.census.gov/table/ACSDP5Y2019.DP03" class="uri">https://data.census.gov/table/ACSDP5Y2019.DP03</a></li> 6297</ul> 6298<p><u>Land Cover Data</u></p> 6299<ul> 6300<li>U.S. Geological Survey (USGS). (2008-2023). Annual National Land Cover Data Collection 1 Science Products: U.S. Geological Survey data releases. Data downloaded from: <a href="https://www.mrlc.gov/data?f%5B0%5D=category%3ALand%20Cover" class="uri">https://www.mrlc.gov/data?f[0]=category%3ALand%20Cover</a></li> 6301<li>Land Cover Classifications from the <a href="https://www.mrlc.gov/data/type/land-cover">Multi-Resolution Land Characteristics Consortium</a></li> 6302</ul> 6303<!-- --> 6304 6305</section> 6306</section> 6307 6308</main> 6309<!-- /main column -->
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6604 // This is a special case and we should probably do some content thinning / targeting 6605 fetch(url) 6606 .then(res => res.text()) 6607 .then(html => { 6608 const parser = new DOMParser(); 6609 const htmlDoc = parser.parseFromString(html, "text/html"); 6610 const note = htmlDoc.querySelector('main.content'); 6611 if (note !== null) { 6612 // This should only happen for chapter cross references 6613 // (since there is no id in the URL) 6614 // remove the first header 6615 if (note.children.length > 0 && note.children[0].tagName === "HEADER") { 6616 note.children[0].remove(); 6617 } 6618 const html = processXRef(null, note); 6619 instance.setContent(html); 6620 } 6621 }).finally(() => { 6622 instance.enable(); 6623 instance.show(); 6624 }); 6625 } 6626 }, function(instance) { 6627 }); 6628 } 6629 let selectedAnnoteEl; 6630 const selectorForAnnotation = ( cell, annotation) => { 6631 let cellAttr = 'data-code-cell="' + cell + '"'; 6632 let lineAttr = 'data-code-annotation="' + annotation + '"'; 6633 const selector = 'span[' + cellAttr + '][' + lineAttr + ']'; 6634 return selector; 6635 } 6636 const selectCodeLines = (annoteEl) => { 6637 const doc = window.document; 6638 const targetCell = annoteEl.getAttribute("data-target-cell"); 6639 const targetAnnotation = annoteEl.getAttribute("data-target-annotation"); 6640 const annoteSpan = window.document.querySelector(selectorForAnnotation(targetCell, targetAnnotation)); 6641 const lines = annoteSpan.getAttribute("data-code-lines").split(","); 6642 const lineIds = lines.map((line) => { 6643 return targetCell + "-" + line; 6644 }) 6645 let top = null; 6646 let height = null; 6647 let parent = null; 6648 if (lineIds.length > 0) { 6649 //compute the position of the single el (top and bottom and make a div) 6650 const el = window.document.getElementById(lineIds[0]); 6651 top = el.offsetTop; 6652 height = el.offsetHeight; 6653 parent = el.parentElement.parentElement; 6654 if (lineIds.length > 1) { 6655 const lastEl = window.document.getElementById(lineIds[lineIds.length - 1]); 6656 const bottom = lastEl.offsetTop + lastEl.offsetHeight; 6657 height = bottom - top; 6658 } 6659 if (top !== null && height !== null && parent !== null) { 6660 // cook up a div (if necessary) and position it 6661 let div = window.document.getElementById("code-annotation-line-highlight"); 6662 if (div === null) { 6663 div = window.document.createElement("div"); 6664 div.setAttribute("id", "code-annotation-line-highlight"); 6665 div.style.position = 'absolute'; 6666 parent.appendChild(div); 6667 } 6668 div.style.top = top - 2 + "px"; 6669 div.style.height = height + 4 + "px"; 6670 div.style.left = 0; 6671 let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter"); 6672 if (gutterDiv === null) { 6673 gutterDiv = window.document.createElement("div"); 6674 gutterDiv.setAttribute("id", "code-annotation-line-highlight-gutter"); 6675 gutterDiv.style.position = 'absolute'; 6676 const codeCell = window.document.getElementById(targetCell); 6677 const gutter = codeCell.querySelector('.code-annotation-gutter'); 6678 gutter.appendChild(gutterDiv); 6679 } 6680 gutterDiv.style.top = top - 2 + "px"; 6681 gutterDiv.style.height = height + 4 + "px"; 6682 } 6683 selectedAnnoteEl = annoteEl; 6684 } 6685 }; 6686 const unselectCodeLines = () => { 6687 const elementsIds = ["code-annotation-line-highlight", "code-annotation-line-highlight-gutter"]; 6688 elementsIds.forEach((elId) => { 6689 const div = window.document.getElementById(elId); 6690 if (div) { 6691 div.remove(); 6692 } 6693 }); 6694 selectedAnnoteEl = undefined; 6695 }; 6696 // Handle positioning of the toggle 6697 window.addEventListener( 6698 "resize", 6699 throttle(() => { 6700 elRect = undefined; 6701 if (selectedAnnoteEl) { 6702 selectCodeLines(selectedAnnoteEl); 6703 } 6704 }, 10) 6705 ); 6706 function throttle(fn, ms) { 6707 let throttle = false;
6708 let timer; 6709 return (...args) => { 6710 if(!throttle) { // first call gets through 6711 fn.apply(this, args); 6712 throttle = true; 6713 } else { // all the others get throttled 6714 if(timer) clearTimeout(timer); // cancel #2 6715 timer = setTimeout(() => { 6716 fn.apply(this, args); 6717 timer = throttle = false; 6718 }, ms); 6719 } 6720 }; 6721 } 6722 // Attach click handler to the DT 6723 const annoteDls = window.document.querySelectorAll('dt[data-target-cell]'); 6724 for (const annoteDlNode of annoteDls) { 6725 annoteDlNode.addEventListener('click', (event) => { 6726 const clickedEl = event.target; 6727 if (clickedEl !== selectedAnnoteEl) { 6728 unselectCodeLines(); 6729 const activeEl = window.document.querySelector('dt[data-target-cell].code-annotation-active'); 6730 if (activeEl) { 6731 activeEl.classList.remove('code-annotation-active'); 6732 } 6733 selectCodeLines(clickedEl); 6734 clickedEl.classList.add('code-annotation-active'); 6735 } else { 6736 // Unselect the line 6737 unselectCodeLines(); 6738 clickedEl.classList.remove('code-annotation-active'); 6739 } 6740 }); 6741 } 6742 const findCites = (el) => { 6743 const parentEl = el.parentElement; 6744 if (parentEl) { 6745 const cites = parentEl.dataset.cites; 6746 if (cites) { 6747 return { 6748 el, 6749 cites: cites.split(' ') 6750 }; 6751 } else { 6752 return findCites(el.parentElement) 6753 } 6754 } else { 6755 return undefined; 6756 } 6757 }; 6758 var bibliorefs = window.document.querySelectorAll('a[role="doc-biblioref"]'); 6759 for (var i=0; i<bibliorefs.length; i++) { 6760 const ref = bibliorefs[i]; 6761 const citeInfo = findCites(ref); 6762 if (citeInfo) { 6763 tippyHover(citeInfo.el, function() { 6764 var popup = window.document.createElement('div'); 6765 citeInfo.cites.forEach(function(cite) { 6766 var citeDiv = window.document.createElement('div'); 6767 citeDiv.classList.add('hanging-indent'); 6768 citeDiv.classList.add('csl-entry'); 6769 var biblioDiv = window.document.getElementById('ref-' + cite); 6770 if (biblioDiv) { 6771 citeDiv.innerHTML = biblioDiv.innerHTML; 6772 } 6773 popup.appendChild(citeDiv); 6774 }); 6775 return popup.innerHTML; 6776 }); 6777 } 6778 } 6779}); 6780</script>
6780<div class="modal fade" id="quarto-embedded-source-code-modal" tabindex="-1" aria-labelledby="quarto-embedded-source-code-modal-label" aria-hidden="true"><div class="modal-dialog modal-dialog-scrollable"><div class="modal-content"><div class="modal-header"><h5 class="modal-title" id="quarto-embedded-source-code-modal-label">Source Code</h5><button class="btn-close" data-bs-dismiss="modal"></button></div><div class="modal-body"><div class> 6781<div class="sourceCode" id="cb21" data-shortcodes="false"><pre class="sourceCode markdown code-with-copy"><code class="sourceCode markdown"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span> 6782<span id="cb21-2"><a href="#cb21-2" aria-hidden="true" tabindex="-1"></a><span class="an">title:</span><span class="co"> "Mapping the Margins: A Block Group-Level Analysis of Unexplained Economic Distress in New Yorkâs Southern Tier (2009-2023)"</span></span> 6783<span id="cb21-3"><a href="#cb21-3" aria-hidden="true" tabindex="-1"></a><span class="an">author:</span><span class="co"> Stephen C. Sanders</span></span> 6784<span id="cb21-4"><a href="#cb21-4" aria-hidden="true" tabindex="-1"></a><span class="an">date:</span><span class="co"> today</span></span> 6785<span id="cb21-5"><a href="#cb21-5" aria-hidden="true" tabindex="-1"></a><span class="an">date-format:</span><span class="co"> long</span></span> 6786<span id="cb21-6"><a href="#cb21-6" aria-hidden="true" tabindex="-1"></a><span class="an">format:</span></span> 6787<span id="cb21-7"><a href="#cb21-7" aria-hidden="true" tabindex="-1"></a><span class="co"> html:</span></span> 6788<span id="cb21-8"><a href="#cb21-8" aria-hidden="true" tabindex="-1"></a><span class="co"> #css: pdf-style.css</span></span> 6789<span id="cb21-9"><a href="#cb21-9" aria-hidden="true" tabindex="-1"></a><span class="co"> #include-after-body: print.html</span></span> 6790<span id="cb21-10"><a href="#cb21-10" aria-hidden="true" tabindex="-1"></a><span class="co"> smooth-scroll: true</span></span> 6791<span id="cb21-11"><a href="#cb21-11" aria-hidden="true" tabindex="-1"></a><span class="co"> toc: true</span></span> 6792<span id="cb21-12"><a href="#cb21-12" aria-hidden="true" tabindex="-1"></a><span class="co"> toc-location: left</span></span> 6793<span id="cb21-13"><a href="#cb21-13" aria-hidden="true" tabindex="-1"></a><span class="co"> toc-title: 'Table of Contents'</span></span> 6794<span id="cb21-14"><a href="#cb21-14" aria-hidden="true" tabindex="-1"></a><span class="co"> #code-fold: false</span></span> 6795<span id="cb21-15"><a href="#cb21-15" aria-hidden="true" tabindex="-1"></a><span class="co"> #code-tools: false</span></span> 6796<span id="cb21-16"><a href="#cb21-16" aria-hidden="true" tabindex="-1"></a><span class="co"> code-fold: true</span></span> 6797<span id="cb21-17"><a href="#cb21-17" aria-hidden="true" tabindex="-1"></a><span class="co"> code-tools: true</span></span> 6798<span id="cb21-18"><a href="#cb21-18" aria-hidden="true" tabindex="-1"></a><span class="co"> embed-resources: true</span></span> 6799<span id="cb21-19"><a href="#cb21-19" aria-hidden="true" tabindex="-1"></a><span class="co"> self-contained-math: true</span></span> 6800<span id="cb21-20"><a href="#cb21-20" aria-hidden="true" tabindex="-1"></a><span class="co"> page-layout: full</span></span> 6801<span id="cb21-21"><a href="#cb21-21" aria-hidden="true" tabindex="-1"></a><span class="co"> # pdf:</span></span> 6802<span id="cb21-22"><a href="#cb21-22" aria-hidden="true" tabindex="-1"></a><span class="co"> # pdf-engine: xelatex</span></span> 6803<span id="cb21-23"><a href="#cb21-23" aria-hidden="true" tabindex="-1"></a><span class="co"> # documentclass: report</span></span> 6804<span id="cb21-24"><a href="#cb21-24" aria-hidden="true" tabindex="-1"></a><span class="co"> # papersize: letter</span></span> 6805<span id="cb21-25"><a href="#cb21-25" aria-hidden="true" tabindex="-1"></a><span class="co"> # colorlinks: true</span></span> 6806<span id="cb21-26"><a href="#cb21-26" aria-hidden="true" tabindex="-1"></a><span class="co"> # toc: true</span></span> 6807<span id="cb21-27"><a href="#cb21-27" aria-hidden="true" tabindex="-1"></a><span class="co"> # toc-title: 'Table of Contents'</span></span> 6808<span id="cb21-28"><a href="#cb21-28" aria-hidden="true" tabindex="-1"></a><span class="co"> # number-sections: true</span></span> 6809<span id="cb21-29"><a href="#cb21-29" aria-hidden="true" tabindex="-1"></a><span class="co"> # number-depth: 2</span></span> 6810<span id="cb21-30"><a href="#cb21-30" aria-hidden="true" tabindex="-1"></a><span class="co"> # highlight-style: github</span></span> 6811<span id="cb21-31"><a href="#cb21-31" aria-hidden="true" tabindex="-1"></a><span class="an">execute:</span></span> 6812<span id="cb21-32"><a href="#cb21-32" aria-hidden="true" tabindex="-1"></a><span class="co">
6812 message: false</span></span> 6813<span id="cb21-33"><a href="#cb21-33" aria-hidden="true" tabindex="-1"></a><span class="co"> warning: false</span></span> 6814<span id="cb21-34"><a href="#cb21-34" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span> 6815<span id="cb21-35"><a href="#cb21-35" aria-hidden="true" tabindex="-1"></a></span> 6816<span id="cb21-36"><a href="#cb21-36" aria-hidden="true" tabindex="-1"></a><span class="fu"># Introduction</span></span> 6817<span id="cb21-37"><a href="#cb21-37" aria-hidden="true" tabindex="-1"></a></span> 6818<span id="cb21-38"><a href="#cb21-38" aria-hidden="true" tabindex="-1"></a>The Southern Tier of New York, comprising fourteen counties designated as part of Northern Appalachia by the Appalachian Regional Commission (ARC), has long faced significant socioeconomic challenges. These counties have experienced some of the steepest population declines in New York State outside of New York City, driven by prolonged economic restructuring and out-migration (McMahon, 2024; Johnson & Lichter, 2019). Historically reliant on manufacturing, agriculture, and extractive industries, the region has struggled to adapt to post-industrial economic realities. As a result, many communities across the Southern Tier endure persistently high poverty rates and low median incomes relative to national benchmarks (ARC, 2023).</span> 6819<span id="cb21-39"><a href="#cb21-39" aria-hidden="true" tabindex="-1"></a></span> 6820<span id="cb21-40"><a href="#cb21-40" aria-hidden="true" tabindex="-1"></a><span class="al"></span>{fig-alt="Map of three subregions of the New York Southern Tier region."}</span> 6821<span id="cb21-41"><a href="#cb21-41" aria-hidden="true" tabindex="-1"></a></span> 6822<span id="cb21-42"><a href="#cb21-42" aria-hidden="true" tabindex="-1"></a>To monitor and address such disparities, the ARC employs its Distressed Areas Classification System, which is traditionally applied at the county level (ARC, 2024). This system designates areas as "distressed" if they exhibit a median family income no greater than 67% of the U.S. average and a poverty rate at least 150% of the national average. While sufficient for broad regional assessments, scholars have noted that county-level classifications risk obscuring critical intra-county variations, particularly in regions marked by sharp spatial heterogeneity (Partridge et al., 2008; Thiede et al., 2017). Applying this framework at the census block group level allows for a more granular understanding of localized socioeconomic distress, capturing neighborhood-level vulnerabilities often masked in aggregate statistics.</span> 6823<span id="cb21-43"><a href="#cb21-43" aria-hidden="true" tabindex="-1"></a></span> 6824<span id="cb21-44"><a href="#cb21-44" aria-hidden="true" tabindex="-1"></a>This study adopts such a fine-scaled approach by applying the ARC's criteria at the block group level across the Southern Tier. Beyond mere classification, however, this analysis seeks to model the risk of socioeconomic distress by incorporating key demographic, economic, and spatial predictors. Using a Generalized Linear Mixed Model (GLMM) framework implemented via the glmmTMB package in R, this study not only identifies predictors of distress but also evaluates the spatial adequacy of the ARC's classification system.</span> 6825<span id="cb21-45"><a href="#cb21-45" aria-hidden="true" tabindex="-1"></a></span> 6826<span id="cb21-46"><a href="#cb21-46" aria-hidden="true" tabindex="-1"></a>The glmmTMB framework is particularly well-suited for this analysis due to its flexibility in handling binary outcomes, accounting for hierarchical spatial structures, and integrating spatially lagged covariates all of which encompass a methodological advancement increasingly adopted in socioeconomic and public health research (Bivand & Piras, 2015; Brooks et al., 2017; Dormann et al., 2007). Recent studies have leveraged glmmTMB to model spatial patterns of poverty (Jokela et al., 2019) and health disparities (Barrett et al., 2023), highlighting its capacity to address complex data structures characterized by spatial autocorrelation and unobserved heterogeneity.</span> 6827<span id="cb21-47"><a href="#cb21-47" aria-hidden="true" tabindex="-1"></a></span> 6828<span id="cb21-48"><a href="#cb21-48" aria-hidden="true" tabindex="-1"></a>By combining the ARC's established distress criteria with predictive modeling, this study aims to (1) assess the underlying risk factors contributing to distress in the Southern Tier, and (2) critically evaluate where the ARC's classification aligns (or falls short) in capturing the true geography of socioeconomic vulnerability. This approach provides a nuanced understanding of distress that can inform more targeted policy interventions within New York'
6828s Appalachian region.</span> 6829<span id="cb21-49"><a href="#cb21-49" aria-hidden="true" tabindex="-1"></a></span> 6830<span id="cb21-50"><a href="#cb21-50" aria-hidden="true" tabindex="-1"></a><span class="fu"># Materials and Methods</span></span> 6831<span id="cb21-51"><a href="#cb21-51" aria-hidden="true" tabindex="-1"></a></span> 6832<span id="cb21-52"><a href="#cb21-52" aria-hidden="true" tabindex="-1"></a><span class="fu">## Data</span></span> 6833<span id="cb21-53"><a href="#cb21-53" aria-hidden="true" tabindex="-1"></a></span> 6834<span id="cb21-54"><a href="#cb21-54" aria-hidden="true" tabindex="-1"></a>The analysis looked at various socioeconomic variables along with land cover classifications. Specifically, data concerning average family income, poverty rate, people over the age of 16 who hadn't worked in the previous 12 month period (hereon referred to as "unemployment rate"), average rent, people over the age of 15 who are divorced (hereon referred to as "divorced rate"), and population density were pulled and/or aggregated at the block group, county, and country levels.</span> 6835<span id="cb21-55"><a href="#cb21-55" aria-hidden="true" tabindex="-1"></a></span> 6836<span id="cb21-56"><a href="#cb21-56" aria-hidden="true" tabindex="-1"></a>Census data was pulled for years between 2009 and 2023. All block group-level data was gathered using the <span class="co">[</span><span class="ot">ipumsr</span><span class="co">](https://cran.r-project.org/web/packages/ipumsr/index.html)</span> package, and all of it originally came from American Community Survey (ACS) 5-year estimates housed by the <span class="co">[</span><span class="ot">NHGIS</span><span class="co">](https://www.nhgis.org/)</span> in its <span class="co">[</span><span class="ot">IPUMS</span><span class="co">](https://www.ipums.org/)</span> collection. For distressed status classification, some country-level data for 2009 and 2010 was pulled using the <span class="co">[</span><span class="ot">tidycensus</span><span class="co">](https://walker-data.com/tidycensus/)</span> package, while the rest of it had to be sourced from governmental reports and news releases since ACS data may not have been readily available for this year at the country-level. Block group-level data was aggregated at the county-level to view changes in these socioeconomic indicators and land cover.</span> 6837<span id="cb21-57"><a href="#cb21-57" aria-hidden="true" tabindex="-1"></a></span> 6838<span id="cb21-58"><a href="#cb21-58" aria-hidden="true" tabindex="-1"></a>National land cover data for years between 2008 and 2023 come from the <span class="co">[</span><span class="ot">Multi-Resolution Land Characteristics Consortium</span><span class="co">](https://www.mrlc.gov/data/project/annual-nlcd)</span>'s National Land Cover Database and were downloaded programmatically from their <span class="co">[</span><span class="ot">Data page</span><span class="co">](https://www.mrlc.gov/data?f[0]=category%3ALand%20Cover)</span>.</span> 6839<span id="cb21-59"><a href="#cb21-59" aria-hidden="true" tabindex="-1"></a></span> 6840<span id="cb21-60"><a href="#cb21-60" aria-hidden="true" tabindex="-1"></a><span class="fu">## Processing Methods</span></span> 6841<span id="cb21-61"><a href="#cb21-61" aria-hidden="true" tabindex="-1"></a></span> 6842<span id="cb21-62"><a href="#cb21-62" aria-hidden="true" tabindex="-1"></a>To ensure the uniform block group boundaries across the 14-county study area, the interpolate_pw function within tidycensus was used for population-weighted areal interpolation via centroid assignment (Walker, 2023). The use of scaling factors applied to each individual extensive variable was required to ensure the preservation of total counts across each of the variables by correcting for biases introduced by differing spatial distributions (Gregory, 2002; Goodchild & Lam, 1980; Flowerdew & Green, 1992).</span> 6843<span id="cb21-63"><a href="#cb21-63" aria-hidden="true" tabindex="-1"></a></span> 6844<span id="cb21-64"><a href="#cb21-64" aria-hidden="true" tabindex="-1"></a>As mentioned previously, the methodology used to determine distressed status of each census tract derives from the <span class="co">[</span><span class="ot">Appalachian Regional Commission</span><span class="co">](https://www.arc.gov/distressed-areas-classification-system/)</span>'s Distressed Areas Classification System. According to the ARC, the key attributes of a distressed census tract are:</span> 6845<span id="cb21-65"><a href="#cb21-65" aria-hidden="true" tabindex="-1"></a></span> 6846<span id="cb21-66"><a href="#cb21-66" aria-hidden="true" tabindex="-1"></a><span class="ss">1. </span>A median family income no greater than 67% of the U.S. average.</span> 6847<span id="cb21-67"><a href="#cb21-67" aria-hidden="true" tabindex="-1"></a><span class="ss">2. </span>A poverty rate of 150% of the U.S. average or greater.</span> 6848<span id="cb21-68"><a href="#cb21-68" aria-hidden="true" tabindex="-1"></a></span> 6849<span id="cb21-69"><a href="#cb21-69" aria-hidden="true" tabindex="-1"></a>Since median family incomes cannot be appropriately interpolated, average family income at the national level was used to determine distressed status of each block group rather than the median.</span> 6850<span id="cb21-70"><a href="#cb21-70" aria-hidden="true" tabindex="-1"></a></span> 6851<span id="cb21-71"><a href="#cb21-71" aria-hidden="true" tabindex="-1"></a>Land cover data was processed using the <span class="co">[</span><span class="ot">terra</span><span class="co">](https://rspatial.github.io/terra/)</span> package. All land cover classifications were grouped into 9 groups (including Developed, Agriculture, Forest, etc.). Zonal statistics were calculated for each class within each block group, and the coverage percentage of each class was calculated for each block group. Developed land ultimately became the only land cover classification that was considered in the predictive model.</span> 6852<span id="cb21-72"><a href="#cb21-72" aria-hidden="true" tabindex="-1"></a></span> 6853<span id="cb21-73"><a href="#cb21-73" aria-hidden="true" tabindex="-1"></a><span class="fu">## Model Using glmmTMB</span></span> 6854<span id="cb21-74"><a href="#cb21-74" aria-hidden="true" tabindex="-1"></a></span> 6855<span id="cb21-75"><a href="#cb21-75" aria-hidden="true" tabindex="-1"></a>A logistic generalized linear mixed model was estimated using the <span class="co">[</span><span class="ot">glmmTMB</span><span class="co">](https://github.com/glmmTMB/glmmTMB)</span> package to assess distress risk across the 1,004 uniform block groups. Population density, unemployment rate, average rent, and divorced rate were included as socioeconomic/demographic predictors. Additionally, spatial dependence was accounted for by incorporating a spatially lagged covariate for unemployment rate as a fixed effect in the GLMM model. This approach allows for partial control of spatial autocorrelation within glmmTMB, and was especially necessary given the socioeconomic nature of this study, the expected clustering of socioeconomic conditions, and the exp
6855ansive study area.</span> 6856<span id="cb21-76"><a href="#cb21-76" aria-hidden="true" tabindex="-1"></a></span> 6857<span id="cb21-77"><a href="#cb21-77" aria-hidden="true" tabindex="-1"></a>Including time period fixed effects in a glmmTMB model is a common approach to control for unobserved temporal factors that could bias estimates, such as policy changes, economic cycles, or external shocks affecting all units over time (Zuur et al., 2009). This method isolates the impact of key predictors by accounting for systematic variations across different periods within generalized linear mixed models.</span> 6858<span id="cb21-78"><a href="#cb21-78" aria-hidden="true" tabindex="-1"></a></span> 6859<span id="cb21-79"><a href="#cb21-79" aria-hidden="true" tabindex="-1"></a>Including GEOID as a random effect in a glmmTMB model accounts for unobserved, unit-specific heterogeneity across spatial units, such as census block groups or tracts, allowing the model to control for clustering and repeated measures within geographic areas (Gelman & Hill, 2007). This approach improves inference by addressing potential correlation within groups and capturing latent spatial characteristics not explained by fixed effects. The model assigns each unique block group a random intercept, and these random intercepts represent the baseline log-odds of distress for each block group after accounting for all fixed effects. In other words, this is the unexplained spatial variation - the residual risk attributable to block group-level factors not captured by the model's included predictors.</span> 6860<span id="cb21-80"><a href="#cb21-80" aria-hidden="true" tabindex="-1"></a></span> 6861<span id="cb21-81"><a href="#cb21-81" aria-hidden="true" tabindex="-1"></a>Scaling predictor variables in a glmmTMB model is a standard practice to improve model convergence, interpretability, and comparability of effect sizes, especially when predictors are on different scales or have large variances (Schielzeth, 2010). Standardization centers variables around zero and ensures that coefficients represent the effect of a one standard deviation change, facilitating more meaningful interpretation in generalized linear mixed models.</span> 6862<span id="cb21-82"><a href="#cb21-82" aria-hidden="true" tabindex="-1"></a></span> 6863<span id="cb21-83"><a href="#cb21-83" aria-hidden="true" tabindex="-1"></a>After successfully estimating the GLMM, the <span class="co">[</span><span class="ot">DHARMa</span><span class="co">](https://github.com/florianhartig/DHARMa)</span> package was used for diagnostic tests to evaluate the assumptions. The <span class="co">[</span><span class="ot">spdep</span><span class="co">](https://r-spatial.github.io/spdep/)</span> package was used to conduct a Moran's I test to determine the presence of spatial autocorrelation.</span> 6864<span id="cb21-84"><a href="#cb21-84" aria-hidden="true" tabindex="-1"></a></span> 6865<span id="cb21-85"><a href="#cb21-85" aria-hidden="true" tabindex="-1"></a><span class="fu">## Other Methods</span></span> 6866<span id="cb21-86"><a href="#cb21-86" aria-hidden="true" tabindex="-1"></a></span> 6867<span id="cb21-87"><a href="#cb21-87" aria-hidden="true" tabindex="-1"></a>The <span class="co">[</span><span class="ot">tidyverse</span><span class="co">](https://www.tidyverse.org/)</span> collection of packages was primarily used to easily process a large amount of data. The <span class="co">[</span><span class="ot">tigris</span><span class="co">](https://github.com/walkerke/tigris)</span> package was used directly to pull study counties to create a map of the study area, as well as to pull block-level data for use as the weights in the interpolation process. The <span class="co">[</span><span class="ot">sf</span><span class="co">](https://r-spatial.github.io/sf/)</span> package helped with processing spatial features, including data pulled from the ACS. The maps and general plots were created using <span class="co">[</span><span class="ot">ggplot2</span><span class="co">](https://ggplot2.tidyverse.org/)</span>. The <span class="co">[</span><span class="ot">gt</span><span class="co">](https://gt.rstudio.com/)</span> package allowed for the creation of better looking tables.</span> 6868<span id="cb21-88"><a href="#cb21-88" aria-hidden="true" tabindex="-1"></a></span> 6869<span id="cb21-89"><a href="#cb21-89" aria-hidden="true" tabindex="-1"></a><span class="fu"># Data Gathering and Processing</span></span> 6870<span id="cb21-90"><a href="#cb21-90" aria-hidden="true" tabindex="-1"></a></span> 6871<span id="cb21-91"><a href="#cb21-91" aria-hidden="true" tabindex="-1"></a><span class="in">```{r libraries, message=F, warning=F, echo=F}</span></span> 6872<span id="cb21-92"><a href="#cb21-92" aria-hidden="true" tabindex="-1"></a><span class="in"># load necessary libraries</span></span> 6873<span id="cb21-93"><a href="#cb21-93" aria-hidden="true" tabindex="-1"></a><span class="in">library(tidyverse)</span></span> 6874<span id="cb21-94"><a href="#cb21-94" aria-hidden="true" tabindex="-1"></a><span class="in">library(tidycensus)</span></span> 6875<span id="cb21-95"><a href="#cb21-95" aria-hidden="true" tabindex="-1"></a><span class="in">library(sf)</span></span> 6876<span id="cb21-96"><a href="#cb21-96" aria-hidden="true" tabindex="-1"></a><span class="in">library(tigris)</span></span> 6877<span id="cb21-97"><a href="#cb21-97" aria-hidden="true" tabindex="-1"></a><span class="in">library(ipumsr)</span></span> 6878<span id="cb21-98"><a href="#cb21-98" aria-hidden="true" tabindex="-1"></a><span class="in">library(basemaps)</span></span> 6879<span id="cb21-99"><a href="#cb21-99" aria-hidden="true" tabindex="-1"></a><span class="in">library(leaflet)</span></span> 6880<span id="cb21-100"><a href="#cb21-100" aria-hidden="true" tabindex="-1"></a><span class="in">library(mapview)</span></span> 6881<span id="cb21-101"><a href="#cb21-101" aria-hidden="true" tabindex="-1"></a><span class="in">library(terra)</span></span> 6882<span id="cb21-102"><a href="#cb21-102" aria-hidden="true" tabindex="-1"></a><span class="in">library(future)</span></span> 6883<span id="cb21-103"><a href="#cb21-103" aria-hidden="true" tabindex="-1"></a><span class="in">library(future.apply)</span></span> 6884<span id="cb21-104"><a href="#cb21-104" aria-hidden="true" tabindex="-1"></a><span class="in">library(furrr)</span></span> 6885<span id="cb21-105"><a href="#cb21-105" aria-hidden="true" tabindex="-1"></a><span class="in">library(car)</span></span> 6886<span id="cb21-106"><a href="#cb21-106" aria-hidden="true" tabindex="-1"></a><span class="in">library(glmmTMB)</span></span> 6887<span id="cb21-107"><a href="#cb21-107" aria-hidden="true" tabindex="-1"></a><span class="in">library(DHARMa)</span></span> 6888<span id="cb21-108"><a href="#cb21-108" aria-hidden="true" tabindex="-1"></a><span class="in">library(spdep)</span></span> 6889<span id="cb21-109"><a href="#cb21-109" aria-hidden="true" tabindex="-1"></a><span class="in">library(tidyterra)</span></span> 6890<span id="cb21-110"><a href="#cb21-110" aria-hidden="true" tabindex="-1"></a><span class="in">library(cowplot)</span></span> 6891<span id="cb21-111"><a href="#cb21-111" aria-hidden="true" tabindex="-1"></a><span class="in">library(ggspatial)</span></span> 6892<span id="cb21-112"><a href="#cb21-112" aria-hidden="true" tabindex="-1"></a><span class="in">library(gt)</span></span> 6893<span id="cb21-113"><a href="#cb21-113" aria-hidden="true" tabindex="-1"></a><span class="in">library(RColorBrewer)</span></span> 6894<span id="cb21-114"><a href="#cb21-114" aria-hidden="true" tabindex="-1"></a><span class="in">library(gridExtra)</span></span> 6895<span id="cb21-115"><a href="#cb21-115" aria-hidden="true" tabindex="-1"></a><span class="in">library(reshape2)</span></span> 6896<span id="cb21-116"><a href="#cb21-116" aria-hidden="true" tabindex="-1"></a><span class="in">library(scales)</span></span> 6897<span id="cb21-117"><a href="#cb21-117" aria-hidden="true" tabindex="-1"></a><span class="in">library(downloader)</span></span> 6898<span id="cb21-118"><a href="#cb21-118" aria-hidden="true" tabindex="-1"></a><span class="in">library(knitr)</span></span> 6899<span id="cb21-119"><a href="#cb21-119" aria-hidden="true" tabindex="-1"></a></span> 6900<span id="cb21-120"><a href="#cb21-120" aria-hidden="true" tabindex="-1"></a><span class="in">source('R/funcs.R')</span></span> 6901<span id="cb21-121"><a href="#cb21-121" aria-hidden="true" tabindex="-1"></a></span> 6902<span id="cb21-122"><a href="#cb21-122" aria-hidden="true" tabindex="-1"></a><span class="in">knitr::opts_chunk$set(echo = TRUE, # cache the results for quick compiling</span></span> 6903<span id="cb21-123"><a href="#cb21-123" aria-hidden="true" tabindex="-1"></a><span class="in"> fig.width = unit(18, 'cm'),</span></span> 6904<span id="cb21-124"><a href="#cb21-124" aria-hidden="true" tabindex="-1"></a><span class="in"> fig.height = unit(11, 'cm'))</span></span> 6905<span id="cb21-125"><a href="#cb21-125" aria-hidden="true" tabindex="-1"></a></span> 6906<span id="cb21-126"><a href="#cb21-126" aria-hidden="true" tabindex="-1"></a><span class="in"># pull in api tokens</span></span> 6907<span id="cb21-127"><a href="#cb21-127" aria-hidden="true" tabindex="-1"></a><span class="in">census_token = Sys.getenv("CENSUS_TOKEN")</span></span> 6908<span id="cb21-128"><a href="#cb21-128" aria-hidden="true" tabindex="-1"></a><span class="in">ipums_token = Sys.getenv("IPUMS_TOKEN")</span></span> 6909<span id="cb21-129"><a href="#cb21-129" aria-hidden="true" tabindex="-1"></a><span class="in">census_api_key(census_token)</span></span> 6910<span id="cb21-130"><a href="#cb21-130" aria-hidden="true" tabindex="-1"></a><span class="in">set_ipums_api_key(ipums_token)</span></span> 6911<span id="cb21-131"><a href="#cb21-131" aria-hidden="true" tabindex="-1"></a><span class="in">#readRenviron("~/.Renviron")</span></span> 6912<span id="cb21-132"><a href="#cb21-132" aria-hidden="true" tabindex="-1"></a></span> 6913<span id="cb21-133"><a href="#cb21-133" aria-hidden="true" tabindex="-1"></a><span class="in"># increase timeout time to download files and cache tigris files</span></span> 6914<span id="cb21-134"><a href="#cb21-134" aria-hidden="true" tabindex="-1"></a><span class="in"># tell terra not to display progress bars</span></span> 6915<span id="cb21-135"><a href="#cb21-135" aria-hidden="true" tabindex="-1"></a><span class="in">options(timeout = 500, tigris_use_cache = TRUE)</span></span> 6916<span id="cb21-136"><a href="#cb21-136" aria-hidden="true" tabindex="-1"></a><span class="in">terraOptions(progress = 0)</span></span> 6917<span id="cb21-137"><a href="#cb21-137" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span> 6918<span id="cb21-138"><a href="#cb21-138" aria-hidden="true" tabindex="-1"></a></span> 6919<span id="cb21-139"><a href="#cb21-139" aria-hidden="true" tabindex="-1"></a><span class="fu">## Map of Study Area</span></span> 6920<span id="cb21-140"><a href="#cb21-140" aria-hidden="true" tabindex="-1"></a></span> 6921<span id="cb21-141"><a href="#cb21-141" aria-hidden="true" tabindex="-1"></a>The map below shows the 14 counties in New York's Southern Tier (Allegany, Broome, Cattaraugus, Chautauqua, Chemung, Chenango, Cortland, Delaware, Otsego, Schoharie, Schuyler, Steuben, Tioga, and Tompkins) and all 1,004 block groups (in 2020-2023).</span> 6922<span id="cb21-142"><a href="#cb21-142" aria-hidden="true" tabindex="-1"></a></span> 6923<span id="cb21-143"><a href="#cb21-143" aria-hidden="true" tabindex="-1"></a><span class="in">```{r study_area_map, message=F, warning=F, cache=T}</span></span> 6924<span id="cb21-144"><a href="#cb21-144" aria-hidden="true" tabindex="-1"></a><span class="in">#| fig-align: center</span></span> 6925<span id="cb21-145"><a href="#cb21-145" aria-hidden="true" tabindex="-1"></a><span class="in">#| fig-margin: 0</span></span> 6926<span id="cb21-146"><a href="#cb21-146" aria-hidden="true" tabindex="-1"></a></span> 6927<span id="cb21-147"><a href="#cb21-147" aria-hidden="true" tabindex="-1"></a><span class="in"># set directories in cwd in which to store data</span></span> 6928<span id="cb21-148"><a href="#cb21-148" aria-hidden="true" tabindex="-1"></a><span class="in">data_download_path <- 'data/'</span></span> 6929<span id="cb21-149"><a href="#cb21-149" aria-hidden="true" tabindex="-1"></a><span class="in">nhgis_data_dir <- paste(data_download_path, 'nhgis/', sep = '')</span></span> 6930<span id="cb21-150"><a href="#cb21-150" aria-hidden="true" tabindex="-1"></a><span class="in">mrlc_data_dir <- paste(data_download_path, 'mrlc/', sep = '')</span></span> 6931<span id="cb21-151"><a href="#cb21-151" aria-hidden="true" tabindex="-1"></a><span class="in">boundaries_data_dir <- paste(data_download_path, 'boundaries/', sep = '')</span></span> 6932<span id="cb21-152"><a href="#cb21-152" aria-hidden="true" tabindex="-1"></a></span> 6933<span id="cb21-153"><a href="#cb21-153" aria-hidden="true" tabindex="-1"></a><span class="in"># set vector of southern tier counties</span></span> 6934<span id="cb21-154"><a href="#cb21-154" aria-hidden="true" tabindex="-1"></a><span class="in">southern_tier_counties = c('Allegany', 'Broome', 'Cattaraugus', 'Chautauqua', </span></span> 6935<span id="cb21-155"><a href="#cb21-155" aria-hidden="true" tabindex="-1"></a><span class="in"> 'Chemung', 'Chenango', 'Cortland', 'Delaware', 'Otsego', </span></span> 6936<span id="cb21-156"><a href="#cb21-156" aria-hidden="true" tabindex="-1"></a><span class="in"> 'Schoharie', 'Schuyler', 'Steuben', 'Tioga', 'Tompkins')</span></span> 6937<span id="cb21-157"><a href="#cb21-157" aria-hidden="true" tabindex="-1"></a></span> 6938<span id="cb21-158"><a href="#cb21-158" aria-hidden="true" tabindex="-1"></a><span class="in"># get study geographies, city and town boundaries, and basemap focu
6938sed on ny state</span></span> 6939<span id="cb21-159"><a href="#cb21-159" aria-hidden="true" tabindex="-1"></a><span class="in">ny_state <- states(cb = TRUE, year = 2020) %>% filter(NAME == 'New York')</span></span> 6940<span id="cb21-160"><a href="#cb21-160" aria-hidden="true" tabindex="-1"></a></span> 6941<span id="cb21-161"><a href="#cb21-161" aria-hidden="true" tabindex="-1"></a><span class="in"># list of county FIPS</span></span> 6942<span id="cb21-162"><a href="#cb21-162" aria-hidden="true" tabindex="-1"></a><span class="in">county_fips <- c(</span></span> 6943<span id="cb21-163"><a href="#cb21-163" aria-hidden="true" tabindex="-1"></a><span class="in"> "003", "007", "009", "013", "015", "017", "023", </span></span> 6944<span id="cb21-164"><a href="#cb21-164" aria-hidden="true" tabindex="-1"></a><span class="in"> "025", "077", "095", "097", "101", "107", "109"</span></span> 6945<span id="cb21-165"><a href="#cb21-165" aria-hidden="true" tabindex="-1"></a><span class="in">)</span></span> 6946<span id="cb21-166"><a href="#cb21-166" aria-hidden="true" tabindex="-1"></a></span> 6947<span id="cb21-167"><a href="#cb21-167" aria-hidden="true" tabindex="-1"></a><span class="in"># get southern tier county boundaries</span></span> 6948<span id="cb21-168"><a href="#cb21-168" aria-hidden="true" tabindex="-1"></a><span class="in">study_counties <- </span></span> 6949<span id="cb21-169"><a href="#cb21-169" aria-hidden="true" tabindex="-1"></a><span class="in"> counties(state = 'NY', cb = TRUE, year = 2020) %>%</span></span> 6950<span id="cb21-170"><a href="#cb21-170" aria-hidden="true" tabindex="-1"></a><span class="in"> filter(NAME %in% southern_tier_counties)</span></span> 6951<span id="cb21-171"><a href="#cb21-171" aria-hidden="true" tabindex="-1"></a></span> 6952<span id="cb21-172"><a href="#cb21-172" aria-hidden="true" tabindex="-1"></a><span class="in"># get block groups in southern tier</span></span> 6953<span id="cb21-173"><a href="#cb21-173" aria-hidden="true" tabindex="-1"></a><span class="in">study_bgs <- </span></span> 6954<span id="cb21-174"><a href="#cb21-174" aria-hidden="true" tabindex="-1"></a><span class="in"> block_groups(state = 'NY', year = 2020) %>%</span></span> 6955<span id="cb21-175"><a href="#cb21-175" aria-hidden="true" tabindex="-1"></a><span class="in"> filter(COUNTYFP %in% county_fips)</span></span> 6956<span id="cb21-176"><a href="#cb21-176" aria-hidden="true" tabindex="-1"></a></span> 6957<span id="cb21-177"><a href="#cb21-177" aria-hidden="true" tabindex="-1"></a><span class="in"># get basemap of NY</span></span> 6958<span id="cb21-178"><a href="#cb21-178" aria-hidden="true" tabindex="-1"></a><span class="in">base_ny <- basemap_raster(ext = ny_state, map_service = 'carto', </span></span> 6959<span id="cb21-179"><a href="#cb21-179" aria-hidden="true" tabindex="-1"></a><span class="in"> map_type = 'light', verbose = FALSE)</span></span> 6960<span id="cb21-180"><a href="#cb21-180" aria-hidden="true" tabindex="-1"></a></span> 6961<span id="cb21-181"><a href="#cb21-181" aria-hidden="true" tabindex="-1"></a><span class="in"># get villages and cities</span></span> 6962<span id="cb21-182"><a href="#cb21-182" aria-hidden="true" tabindex="-1"></a><span class="in">st_civil <- </span></span> 6963<span id="cb21-183"><a href="#cb21-183" aria-hidden="true" tabindex="-1"></a><span class="in"> st_read('data/boundaries/NYS_Civil_Boundaries.geojson', quiet = TRUE) %>%</span></span> 6964<span id="cb21-184"><a href="#cb21-184" aria-hidden="true" tabindex="-1"></a><span class="in"> filter(COUNTY %in% southern_tier_counties) %>%</span></span> 6965<span id="cb21-185"><a href="#cb21-185" aria-hidden="true" tabindex="-1"></a><span class="in"> st_transform(crs = 26918)</span></span> 6966<span id="cb21-186"><a href="#cb21-186" aria-hidden="true" tabindex="-1"></a></span> 6967<span id="cb21-187"><a href="#cb21-187" aria-hidden="true" tabindex="-1"></a><span class="in">st_cities <-</span></span> 6968<span id="cb21-188"><a href="#cb21-188" aria-hidden="true" tabindex="-1"></a><span class="in"> st_read('data/boundaries/NYS_City_Boundaries.geojson', quiet = TRUE) %>%</span></span> 6969<span id="cb21-189"><a href="#cb21-189" aria-hidden="true" tabindex="-1"></a><span class="in"> filter(COUNTY %in% southern_tier_counties) %>%</span></span> 6970<span id="cb21-190"><a href="#cb21-190" aria-hidden="true" tabindex="-1"></a><span class="in"> st_transform(crs = 26918)</span></span> 6971<span id="cb21-191"><a href="#cb21-191" aria-hidden="true" tabindex="-1"></a></span> 6972<span id="cb21-192"><a href="#cb21-192" aria-hidden="true" tabindex="-1"></a><span class="in"># filter out certain villages, transform, and get centroids</span></span> 6973<span id="cb21-193"><a href="#cb21-193" aria-hidden="true" tabindex="-1"></a><span class="in">civil <- </span></span> 6974<span id="cb21-194"><a href="#cb21-194" aria-hidden="true" tabindex="-1"></a><span class="in"> st_civil %>% </span></span> 6975<span id="cb21-195"><a href="#cb21-195" aria-hidden="true" tabindex="-1"></a><span class="in"> st_transform(crs = 26918) %>%</span></span> 6976<span id="cb21-196"><a href="#cb21-196" aria-hidden="true" tabindex="-1"></a><span class="in"> st_centroid() %>%</span></span> 6977<span id="cb21-197"><a href="#cb21-197" aria-hidden="true" tabindex="-1"></a><span class="in"> select(NAME, geometry) %>%</span></span> 6978<span id="cb21-198"><a href="#cb21-198" aria-hidden="true" tabindex="-1"></a><span class="in"> filter(NAME %in% c("Alfred", "Wellsville", "Bath", "Cooperstown", </span></span> 6979<span id="cb21-199"><a href="#cb21-199" aria-hidden="true" tabindex="-1"></a><span class="in"> "Watkins Glen", "Sidney", "Delhi", "Waverly", </span></span> 6980<span id="cb21-200"><a href="#cb21-200" aria-hidden="true" tabindex="-1"></a><span class="in"> "Owego", "Cobleskill", "Hancock"))</span></span> 6981<span id="cb21-201"><a href="#cb21-201" aria-hidden="true" tabindex="-1"></a></span> 6982<span id="cb21-202"><a href="#cb21-202" aria-hidden="true" tabindex="-1"></a><span class="in"># transform cities and get centroids</span></span> 6983<span id="cb21-203"><a href="#cb21-203" aria-hidden="true" tabindex="-1"></a><span class="in">cities <-</span></span> 6984<span id="cb21-204"><a href="#cb21-204" aria-hidden="true" tabindex="-1"></a><span class="in"> st_cities %>%</span></span> 6985<span id="cb21-205"><a href="#cb21-205" aria-hidden="true" tabindex="-1"></a><span class="in"> st_transform(crs = 26918) %>%</span></span> 6986<span id="cb21-206"><a href="#cb21-206" aria-hidden="true" tabindex="-1"></a><span class="in"> st_centroid() %>%</span></span> 6987<span id="cb21-207"><a href="#cb21-207" aria-hidden="true" tabindex="-1"></a><span class="in"> select(NAME, geometry)</span></span> 6988<span id="cb21-208"><a href="#cb21-208" aria-hidden="true" tabindex="-1"></a></span> 6989<span id="cb21-209"><a href="#cb21-209" aria-hidden="true" tabindex="-1"></a><span class="in"># plot block groups, counties, and names of cities and villages</span></span> 6990<span id="cb21-210"><a href="#cb21-210" aria-hidden="true" tabindex="-1"></a><span class="in">st_map <-</span></span> 6991<span id="cb21-211"><a href="#cb21-211" aria-hidden="true" tabindex="-1"></a><span class="in"> ggplot() +</span></span> 6992<span id="cb21-212"><a href="#cb21-212" aria-hidden="true" tabindex="-1"></a><span class="in">
6992 geom_sf(data = study_bgs %>% st_transform(crs = 'EPSG:26918'), </span></span> 6993<span id="cb21-213"><a href="#cb21-213" aria-hidden="true" tabindex="-1"></a><span class="in"> color = 'lightpink', fill = 'white', size = 2.5) +</span></span> 6994<span id="cb21-214"><a href="#cb21-214" aria-hidden="true" tabindex="-1"></a><span class="in"> geom_sf(data = study_counties %>% st_transform(crs = 'EPSG:26918'), </span></span> 6995<span id="cb21-215"><a href="#cb21-215" aria-hidden="true" tabindex="-1"></a><span class="in"> color = 'black', fill = NA, size = 2) +</span></span> 6996<span id="cb21-216"><a href="#cb21-216" aria-hidden="true" tabindex="-1"></a><span class="in"> geom_sf_text(data = cities, aes(label = NAME), </span></span> 6997<span id="cb21-217"><a href="#cb21-217" aria-hidden="true" tabindex="-1"></a><span class="in"> angle = 22.5, size = 4) +</span></span> 6998<span id="cb21-218"><a href="#cb21-218" aria-hidden="true" tabindex="-1"></a><span class="in"> geom_sf_text(data = civil, aes(label = NAME), </span></span> 6999<span id="cb21-219"><a href="#cb21-219" aria-hidden="true" tabindex="-1"></a><span class="in"> angle = -22.5, size = 3.25) +</span></span> 7000<span id="cb21-220"><a href="#cb21-220" aria-hidden="true" tabindex="-1"></a><span class="in"> labs(title = 'NY Southern Tier Counties and Block Groups',</span></span> 7001<span id="cb21-221"><a href="#cb21-221" aria-hidden="true" tabindex="-1"></a><span class="in"> subtitle = 'Block Groups as of 2020') +</span></span> 7002<span id="cb21-222"><a href="#cb21-222" aria-hidden="true" tabindex="-1"></a><span class="in"> theme_map() +</span></span> 7003<span id="cb21-223"><a href="#cb21-223" aria-hidden="true" tabindex="-1"></a><span class="in"> theme(</span></span> 7004<span id="cb21-224"><a href="#cb21-224" aria-hidden="true" tabindex="-1"></a><span class="in"> plot.margin = margin(10, 10, 10, 10),</span></span> 7005<span id="cb21-225"><a href="#cb21-225" aria-hidden="true" tabindex="-1"></a><span class="in"> legend.position = 'none'</span></span> 7006<span id="cb21-226"><a href="#cb21-226" aria-hidden="true" tabindex="-1"></a><span class="in"> ) +</span></span> 7007<span id="cb21-227"><a href="#cb21-227" aria-hidden="true" tabindex="-1"></a><span class="in"> coord_sf(expand = FALSE)</span></span> 7008<span id="cb21-228"><a href="#cb21-228" aria-hidden="true" tabindex="-1"></a></span> 7009<span id="cb21-229"><a href="#cb21-229" aria-hidden="true" tabindex="-1"></a><span class="in">st_map</span></span> 7010<span id="cb21-230"><a href="#cb21-230" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span> 7011<span id="cb21-231"><a href="#cb21-231" aria-hidden="true" tabindex="-1"></a></span> 7012<span id="cb21-232"><a href="#cb21-232" aria-hidden="true" tabindex="-1"></a><span class="in">```{=latex}</span></span> 7013<span id="cb21-233"><a href="#cb21-233" aria-hidden="true" tabindex="-1"></a><span class="in">\newpage</span></span> 7014<span id="cb21-234"><a href="#cb21-234" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span> 7015<span id="cb21-235"><a href="#cb21-235" aria-hidden="true" tabindex="-1"></a></span> 7016<span id="cb21-236"><a href="#cb21-236" aria-hidden="true" tabindex="-1"></a><span class="fu">## Download and Process All Required Data</span></span> 7017<span id="cb21-237"><a href="#cb21-237" aria-hidden="true" tabindex="-1"></a></span> 7018<span id="cb21-238"><a href="#cb21-238" aria-hidden="true" tabindex="-1"></a>Data at the U.S. level is pulled first. This data is used in determining distress status of each block group. Next, block group-level data is pulled and pre-processed.</span> 7019<span id="cb21-239"><a href="#cb21-239" aria-hidden="true" tabindex="-1"></a></span> 7020<span id="cb21-240"><a href="#cb21-240" aria-hidden="true" tabindex="-1"></a><span class="fu">### ACS Data</span></span> 7021<span id="cb21-241"><a href="#cb21-241" aria-hidden="true" tabindex="-1"></a></span> 7022<span id="cb21-242"><a href="#cb21-242" aria-hidden="true" tabindex="-1"></a><span class="fu">#### U.S. Data</span></span> 7023<span id="cb21-243"><a href="#cb21-243" aria-hidden="true" tabindex="-1"></a></span> 7024<span id="cb21-244"><a href="#cb21-244" aria-hidden="true" tabindex="-1"></a>Create collection of distressed indicator-related data at U.S. level for each year between 2009 and 2023.</span> 7025<span id="cb21-245"><a href="#cb21-245" aria-hidden="true" tabindex="-1"></a></span> 7026<span id="cb21-246"><a href="#cb21-246" aria-hidden="true" tabindex="-1"></a><span class="in">```{r us_data, message=F, warning=F}</span></span> 7027<span id="cb21-247"><a href="#cb21-247" aria-hidden="true" tabindex="-1"></a><span class="in">
7027#| cache: false</span></span> 7028<span id="cb21-248"><a href="#cb21-248" aria-hidden="true" tabindex="-1"></a></span> 7029<span id="cb21-249"><a href="#cb21-249" aria-hidden="true" tabindex="-1"></a><span class="in"># set US data for 2009 and 2010</span></span> 7030<span id="cb21-250"><a href="#cb21-250" aria-hidden="true" tabindex="-1"></a><span class="in">us_data.2009 <- data.frame(</span></span> 7031<span id="cb21-251"><a href="#cb21-251" aria-hidden="true" tabindex="-1"></a><span class="in"> Name = 'United States',</span></span> 7032<span id="cb21-252"><a href="#cb21-252" aria-hidden="true" tabindex="-1"></a><span class="in"> year = 2009,</span></span> 7033<span id="cb21-253"><a href="#cb21-253" aria-hidden="true" tabindex="-1"></a><span class="in"> tot_pop = 301461533,</span></span> 7034<span id="cb21-254"><a href="#cb21-254" aria-hidden="true" tabindex="-1"></a><span class="in"> pct_below_poverty = 0.143,</span></span> 7035<span id="cb21-255"><a href="#cb21-255" aria-hidden="true" tabindex="-1"></a><span class="in"> median_income = 64000,</span></span> 7036<span id="cb21-256"><a href="#cb21-256" aria-hidden="true" tabindex="-1"></a><span class="in"> per_capita_income = 39307,</span></span> 7037<span id="cb21-257"><a href="#cb21-257" aria-hidden="true" tabindex="-1"></a><span class="in"> pct_over_16_didnt_work = 0.2133001</span></span> 7038<span id="cb21-258"><a href="#cb21-258" aria-hidden="true" tabindex="-1"></a><span class="in">) %>%</span></span> 7039<span id="cb21-259"><a href="#cb21-259" aria-hidden="true" tabindex="-1"></a><span class="in"> as_tibble()</span></span> 7040<span id="cb21-260"><a href="#cb21-260" aria-hidden="true" tabindex="-1"></a></span> 7041<span id="cb21-261"><a href="#cb21-261" aria-hidden="true" tabindex="-1"></a><span class="in">us_data.2010 <- data.frame(</span></span> 7042<span id="cb21-262"><a href="#cb21-262" aria-hidden="true" tabindex="-1"></a><span class="in"> Name = 'United States',</span></span> 7043<span id="cb21-263"><a href="#cb21-263" aria-hidden="true" tabindex="-1"></a><span class="in"> year = 2010,</span></span> 7044<span id="cb21-264"><a href="#cb21-264" aria-hidden="true" tabindex="-1"></a><span class="in"> tot_pop = 312471327,</span></span> 7045<span id="cb21-265"><a href="#cb21-265" aria-hidden="true" tabindex="-1"></a><span class="in"> pct_below_poverty = 0.153,</span></span> 7046<span id="cb21-266"><a href="#cb21-266" aria-hidden="true" tabindex="-1"></a><span class="in"> median_income = 64400,</span></span> 7047<span id="cb21-267"><a href="#cb21-267" aria-hidden="true" tabindex="-1"></a><span class="in"> per_capita_income = 40557,</span></span> 7048<span id="cb21-268"><a href="#cb21-268" aria-hidden="true" tabindex="-1"></a><span class="in"> pct_over_16_didnt_work = 0.223817</span></span> 7049<span id="cb21-269"><a href="#cb21-269" aria-hidden="true" tabindex="-1"></a><span class="in">) %>%</span></span> 7050<span id="cb21-270"><a href="#cb21-270" aria-hidden="true" tabindex="-1"></a><span class="in"> as_tibble()</span></span> 7051<span id="cb21-271"><a href="#cb21-271" aria-hidden="true" tabindex="-1"></a></span> 7052<span id="cb21-272"><a href="#cb21-272" aria-hidden="true" tabindex="-1"></a><span class="in"># pull US data from ACS for 2011-2023</span></span> 7053<span id="cb21-273"><a href="#cb21-273" aria-hidden="true" tabindex="-1"></a><span class="in">us_data.2011_2023 <- </span></span> 7054<span id="cb21-274"><a href="#cb21-274" aria-hidden="true" tabindex="-1"></a><span class="in"> map2(2011:2023, rep('us', times = 13), get_acs_data) %>% </span></span> 7055<span id="cb21-275"><a href="#cb21-275" aria-hidden="true" tabindex="-1"></a><span class="in"> bind_rows() %>%</span></span> 7056<span id="cb21-276"><a href="#cb21-276" aria-hidden="true" tabindex="-1"></a><span class="in"> rename_with(~str_replace(., 'E$', ''), .cols = ends_with('E')) %>%</span></span> 7057<span id="cb21-277"><a href="#cb21-277" aria-hidden="true" tabindex="-1"></a><span class="in"> select(-ends_with('M')) %>%</span></span> 7058<span id="cb21-278"><a href="#cb21-278" aria-hidden="true" tabindex="-1"></a><span class="in"> mutate(</span></span> 7059<span id="cb21-279"><a href="#cb21-279" aria-hidden="true" tabindex="-1"></a><span class="in"> Name = 'United States',</span></span> 7060<span id="cb21-280"><a href="#cb21-280" aria-hidden="true" tabindex="-1"></a><span class="in"> pct_below_poverty = tot_below_poverty / tot_pop,</span></span> 7061<span id="cb21-281"><a href="#cb21-281" aria-hidden="true" tabindex="-1"></a><span class="in"> pop_over_16_didnt_work = males_didnt_work + females_didnt_work,</span></span> 7062<span id="cb21-282"><a href="#cb21-282" aria-hidden="true" tabindex="-1"></a><span class="in"> pct_over_16_didnt_work = pop_over_16_didnt_work / tot_pop_over_16,</span></span> 7063<span id="cb21-283"><a href="#cb21-283" aria-hidden="true" tabindex="-1"></a><span class="in"> pop_over_15_divorced = males_divorced + females_divorced,</span></span> 7064<span id="cb21-284"><a href="#cb21-284" aria-hidden="true" tabindex="-1"></a><span class="in"> pct_divorced = pop_over_15_divorced / tot_pop_over_15</span></span> 7065<span id="cb21-285"><a href="#cb21-285" aria-hidden="true" tabindex="-1"></a><span class="in"> ) %>%</span></span> 7066<span id="cb21-286"><a href="#cb21-286" aria-hidden="true" tabindex="-1"></a><span class="in"> select(Name, year, tot_pop, per_capita_income, </span></span> 7067<span id="cb21-287"><a href="#cb21-287" aria-hidden="true" tabindex="-1"></a><span class="in"> pct_over_16_didnt_work, median_income, pct_below_poverty, pct_divorced) %>%</span></span> 7068<span id="cb21-288"><a href="#cb21-288" aria-hidden="true" tabindex="-1"></a><span class="in"> as_tibble()</span></span> 7069<span id="cb21-289"><a href="#cb21-289" aria-hidden="true" tabindex="-1"></a></span> 7070<span id="cb21-290"><a href="#cb21-290" aria-hidden="true" tabindex="-1"></a><span class="in"># combine data into single tibble</span></span> 7071<span id="cb21-291"><a href="#cb21-291" aria-hidden="true" tabindex="-1"></a><span class="in">us_data <- bind_rows(us_data.2009, us_data.2010, us_data.2011_2023)</span></span> 7072<span id="cb21-292"><a href="#cb21-292" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span> 7073<span id="cb21-293"><a href="#cb21-293" aria-hidden="true" tabindex="-1"></a></span> 7074<span id="cb21-294"><a href="#cb21-294" aria-hidden="true" tabindex="-1"></a><span class="fu">#### Block Group Data</span></span> 7075<span id="cb21-295"><a href="#cb21-295" aria-hidden="true" tabindex="-1"></a></span> 7076<span id="cb21-296"><a href="#cb21-296" aria-hidden="true" tabindex="-1"></a>Define an extract to submit to NHGIS, then download, load, and process data.</span> 7077<span id="cb21-297"><a href="#cb21-297" aria-hidden="true" tabindex="-1"></a></span> 7078<span id="cb21-298"><a href="#cb21-298" aria-hidden="true" tabindex="-1"></a><span class="fu">##### Download Set-Up</span></span> 7079<span id="cb21-299"><a href="#cb21-299" aria-hidden="true" tabindex="-1"></a></span> 7080<span id="cb21-300"><a href="#cb21-300" aria-hidden="true" tabindex="-1"></a><span class="in">```{r ipums_bg_data_setup, message=F, warning=F}</span></span> 7081<span id="cb21-301"><a href="#cb21-301" aria-hidden="true" tabindex="-1"></a><span class="in">
7081#| cache: false</span></span> 7082<span id="cb21-302"><a href="#cb21-302" aria-hidden="true" tabindex="-1"></a></span> 7083<span id="cb21-303"><a href="#cb21-303" aria-hidden="true" tabindex="-1"></a><span class="in"># set datasets to pull block and block group data from (2009-2023)</span></span> 7084<span id="cb21-304"><a href="#cb21-304" aria-hidden="true" tabindex="-1"></a><span class="in">datasets <- c('2005_2009_ACS5a', '2006_2010_ACS5a', '2007_2011_ACS5a', </span></span> 7085<span id="cb21-305"><a href="#cb21-305" aria-hidden="true" tabindex="-1"></a><span class="in"> '2008_2012_ACS5a', '2009_2013_ACS5a', '2010_2014_ACS5a',</span></span> 7086<span id="cb21-306"><a href="#cb21-306" aria-hidden="true" tabindex="-1"></a><span class="in"> '2011_2015_ACS5a', '2012_2016_ACS5a', '2013_2017_ACS5a',</span></span> 7087<span id="cb21-307"><a href="#cb21-307" aria-hidden="true" tabindex="-1"></a><span class="in"> '2014_2018_ACS5a', '2015_2019_ACS5a', '2016_2020_ACS5a',</span></span> 7088<span id="cb21-308"><a href="#cb21-308" aria-hidden="true" tabindex="-1"></a><span class="in"> '2017_2021_ACS5a', '2018_2022_ACS5a', '2019_2023_ACS5a')</span></span> 7089<span id="cb21-309"><a href="#cb21-309" aria-hidden="true" tabindex="-1"></a></span> 7090<span id="cb21-310"><a href="#cb21-310" aria-hidden="true" tabindex="-1"></a><span class="in"># extract specifications with variables and geographic level (block group) for each dataset</span></span> 7091<span id="cb21-311"><a href="#cb21-311" aria-hidden="true" tabindex="-1"></a><span class="in">bg_dataset_spec <- map(</span></span> 7092<span id="cb21-312"><a href="#cb21-312" aria-hidden="true" tabindex="-1"></a><span class="in"> datasets,</span></span> 7093<span id="cb21-313"><a href="#cb21-313" aria-hidden="true" tabindex="-1"></a><span class="in"> ~ ds_spec(</span></span> 7094<span id="cb21-314"><a href="#cb21-314" aria-hidden="true" tabindex="-1"></a><span class="in"> .x,</span></span> 7095<span id="cb21-315"><a href="#cb21-315" aria-hidden="true" tabindex="-1"></a><span class="in"> #data_tables = c('B01003', 'B11001', 'B17021', 'B19127'),</span></span> 7096<span id="cb21-316"><a href="#cb21-316" aria-hidden="true" tabindex="-1"></a><span class="in"> data_tables = c('B01003', 'B17021', 'B19101', 'B19127', </span></span> 7097<span id="cb21-317"><a href="#cb21-317" aria-hidden="true" tabindex="-1"></a><span class="in"> 'B19301', 'B23022', 'B25001', 'B25004', </span></span> 7098<span id="cb21-318"><a href="#cb21-318" aria-hidden="true" tabindex="-1"></a><span class="in"> 'B25008', 'B25065', 'B11001', 'B25010', 'B12001'),</span></span> 7099<span id="cb21-319"><a href="#cb21-319" aria-hidden="true" tabindex="-1"></a><span class="in"> geog_levels = 'blck_grp'</span></span> 7100<span id="cb21-320"><a href="#cb21-320" aria-hidden="true" tabindex="-1"></a><span class="in"> )</span></span> 7101<span id="cb21-321"><a href="#cb21-321" aria-hidden="true" tabindex="-1"></a><span class="in">)</span></span> 7102<span id="cb21-322"><a href="#cb21-322" aria-hidden="true" tabindex="-1"></a></span> 7103<span id="cb21-323"><a href="#cb21-323" aria-hidden="true" tabindex="-1"></a><span class="in"># set list of shapefiles to include in extract</span></span> 7104<span id="cb21-324"><a href="#cb21-324" aria-hidden="true" tabindex="-1"></a><span class="in">bg_shps <- c('360_blck_grp_2000_tl2009', '360_blck_grp_2010_tl2010',</span></span> 7105<span id="cb21-325"><a href="#cb21-325" aria-hidden="true" tabindex="-1"></a><span class="in"> '360_blck_grp_2011_tl2011', '360_blck_grp_2012_tl2012',</span></span> 7106<span id="cb21-326"><a href="#cb21-326" aria-hidden="true" tabindex="-1"></a><span class="in"> '360_blck_grp_2013_tl2013', '360_blck_grp_2014_tl2014',</span></span> 7107<span id="cb21-327"><a href="#cb21-327" aria-hidden="true" tabindex="-1"></a><span class="in"> '360_blck_grp_2015_tl2015', '360_blck_grp_2016_tl2016',</span></span> 7108<span id="cb21-328"><a href="#cb21-328" aria-hidden="true" tabindex="-1"></a><span class="in"> '360_blck_grp_2017_tl2017', '360_blck_grp_2018_tl2018',</span></span> 7109<span id="cb21-329"><a href="#cb21-329" aria-hidden="true" tabindex="-1"></a><span class="in"> '360_blck_grp_2019_tl2019', '360_blck_grp_2020_tl2020',</span></span> 7110<span id="cb21-330"><a href="#cb21-330" aria-hidden="true" tabindex="-1"></a><span class="in"> '360_blck_grp_2021_tl2021', '360_blck_grp_2022_tl2022',</span></span> 7111<span id="cb21-331"><a href="#cb21-331" aria-hidden="true" tabindex="-1"></a><span class="in"> '360_blck_grp_2023_tl2023')</span></span> 7112<span id="cb21-332"><a href="#cb21-332" aria-hidden="true" tabindex="-1"></a></span> 7113<span id="cb21-333"><a href="#cb21-333" aria-hidden="true" tabindex="-1"></a><span class="in">#########################################################</span></span> 7114<span id="cb21-334"><a href="#cb21-334" aria-hidden="true" tabindex="-1"></a><span class="in"># RUN THIS CODE THE FIRST TIME, COMMENT IT OUT AFTERWARDS</span></span> 7115<span id="cb21-335"><a href="#cb21-335" aria-hidden="true" tabindex="-1"></a><span class="in">#########################################################</span></span> 7116<span id="cb21-336"><a href="#cb21-336" aria-hidden="true" tabindex="-1"></a></span> 7117<span id="cb21-337"><a href="#cb21-337" aria-hidden="true" tabindex="-1"></a><span class="in"># # define extract used to pull from NHGIS</span></span> 7118<span id="cb21-338"><a href="#cb21-338" aria-hidden="true" tabindex="-1"></a><span class="in"># bg_extract <- define_extract_nhgis(</span></span> 7119<span id="cb21-339"><a href="#cb21-339" aria-hidden="true" tabindex="-1"></a><span class="in"># description = 'Block Groups ACS Data and NY Shapefiles (2009-2023)',</span></span> 7120<span id="cb21-340"><a href="#cb21-340" aria-hidden="true" tabindex="-1"></a><span class="in"># datasets = bg_dataset_spec,</span></span> 7121<span id="cb21-341"><a href="#cb21-341" aria-hidden="true" tabindex="-1"></a><span class="in"># geographic_extents = '360',</span></span> 7122<span id="cb21-342"><a href="#cb21-342" aria-hidden="true" tabindex="-1"></a><span class="in"># shapefiles = bg_shps</span></span> 7123<span id="cb21-343"><a href="#cb21-343" aria-hidden="true" tabindex="-1"></a><span class="in"># )</span></span> 7124<span id="cb21-344"><a href="#cb21-344" aria-hidden="true" tabindex="-1"></a><span class="in"># </span></span> 7125<span id="cb21-345"><a href="#cb21-345" aria-hidden="true" tabindex="-1"></a><span class="in"># # create data pull and store it in your account</span></span> 7126<span id="cb21-346"><a href="#cb21-346" aria-hidden="true" tabindex="-1"></a><span class="in"># final_bg_extract <- wait_for_extract(submit_extract(bg_extract))</span></span> 7127<span id="cb21-347"><a href="#cb21-347" aria-hidden="true" tabindex="-1"></a><span class="in"># </span></span> 7128<span id="cb21-348"><a href="#cb21-348" aria-hidden="true" tabindex="-1"></a><span class="in"># # download files to computer</span></span> 7129<span id="cb21-349"><a href="#cb21-349" aria-hidden="true" tabindex="-1"></a><span class="in"># bg_nhgis_files <- download_extract(final_bg_extract, download_dir = nhgis_data_dir)</span></span> 7130<span id="cb21-350"><a href="#cb21-350" aria-hidden="true" tabindex="-1"></a></span> 7131<span id="cb21-351"><a href="#cb21-351" aria-hidden="true" tabindex="-1"></a><span class="in">#########################################################</span></span> 7132<span id="cb21-352"><a href="#cb21-352" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span> 7133<span id="cb21-353"><a href="#cb21-353" aria-hidden="true" tabindex="-1"></a></span> 7134<span id="cb21-354"><a href="#cb21-354" aria-hidden="true" tabindex="-1"></a><span class="fu">##### Load and Process Block Group Data</span></span> 7135<span id="cb21-355"><a href="#cb21-355" aria-hidden="true" tabindex="-1"></a></span> 7136<span id="cb21-356"><a href="#cb21-356" aria-hidden="true" tabindex="-1"></a><span class="in">```{r ipums_bg_data_processing, message=F, warning=F}</span></span> 7137<span id="cb21-357"><a href="#cb21-357" aria-hidden="true" tabindex="-1"></a><span class="in">
7137#| cache: false</span></span> 7138<span id="cb21-358"><a href="#cb21-358" aria-hidden="true" tabindex="-1"></a></span> 7139<span id="cb21-359"><a href="#cb21-359" aria-hidden="true" tabindex="-1"></a><span class="in"># get zip files</span></span> 7140<span id="cb21-360"><a href="#cb21-360" aria-hidden="true" tabindex="-1"></a><span class="in"># CHANGE THIS ACCORDINGLY TO REFLECT YOUR FILE STRUCTURE</span></span> 7141<span id="cb21-361"><a href="#cb21-361" aria-hidden="true" tabindex="-1"></a><span class="in">nhgis_bg_data <- list.files(path = nhgis_data_dir, pattern = 'csv', full.names = TRUE)[[4]]</span></span> 7142<span id="cb21-362"><a href="#cb21-362" aria-hidden="true" tabindex="-1"></a><span class="in">nhgis_bg_shps <- list.files(path = nhgis_data_dir, pattern = 'shape', full.names = TRUE)[[4]]</span></span> 7143<span id="cb21-363"><a href="#cb21-363" aria-hidden="true" tabindex="-1"></a></span> 7144<span id="cb21-364"><a href="#cb21-364" aria-hidden="true" tabindex="-1"></a><span class="in">#########################################</span></span> 7145<span id="cb21-365"><a href="#cb21-365" aria-hidden="true" tabindex="-1"></a><span class="in"># load tabular data for all block groups</span></span> 7146<span id="cb21-366"><a href="#cb21-366" aria-hidden="true" tabindex="-1"></a><span class="in">#########################################</span></span> 7147<span id="cb21-367"><a href="#cb21-367" aria-hidden="true" tabindex="-1"></a></span> 7148<span id="cb21-368"><a href="#cb21-368" aria-hidden="true" tabindex="-1"></a><span class="in"># initiatlize list of block group data for each year</span></span> 7149<span id="cb21-369"><a href="#cb21-369" aria-hidden="true" tabindex="-1"></a><span class="in">bg_data_yrs <- c()</span></span> 7150<span id="cb21-370"><a href="#cb21-370" aria-hidden="true" tabindex="-1"></a></span> 7151<span id="cb21-371"><a href="#cb21-371" aria-hidden="true" tabindex="-1"></a><span class="in"># iterate over each year</span></span> 7152<span id="cb21-372"><a href="#cb21-372" aria-hidden="true" tabindex="-1"></a><span class="in"># filter for relevant study variables, process the data, then merge data</span></span> 7153<span id="cb21-373"><a href="#cb21-373" aria-hidden="true" tabindex="-1"></a><span class="in"># with respective spatial file</span></span> 7154<span id="cb21-374"><a href="#cb21-374" aria-hidden="true" tabindex="-1"></a><span class="in">for (yr in 2009:2023) {</span></span> 7155<span id="cb21-375"><a href="#cb21-375" aria-hidden="true" tabindex="-1"></a><span class="in"> #print(as.character(yr))</span></span> 7156<span id="cb21-376"><a href="#cb21-376" aria-hidden="true" tabindex="-1"></a><span class="in"> </span></span> 7157<span id="cb21-377"><a href="#cb21-377" aria-hidden="true" tabindex="-1"></a><span class="in"> # load and pre-process data</span></span> 7158<span id="cb21-378"><a href="#cb21-378" aria-hidden="true" tabindex="-1"></a><span class="in"> d <- </span></span> 7159<span id="cb21-379"><a href="#cb21-379" aria-hidden="true" tabindex="-1"></a><span class="in"> read_nhgis(nhgis_bg_data, file_select = matches(as.character(yr))) %>%</span></span> 7160<span id="cb21-380"><a href="#cb21-380" aria-hidden="true" tabindex="-1"></a><span class="in"> .[cumall(!('NAME_M' == colnames(.)))] %>%</span></span> 7161<span id="cb21-381"><a href="#cb21-381" aria-hidden="true" tabindex="-1"></a><span class="in"> mutate(YEAR = str_split(YEAR, '-', simplify = TRUE)[,2],</span></span> 7162<span id="cb21-382"><a href="#cb21-382" aria-hidden="true" tabindex="-1"></a><span class="in"> COUNTY = str_split(COUNTY, ' ', simplify = TRUE)[,1]) %>%</span></span> 7163<span id="cb21-383"><a href="#cb21-383" aria-hidden="true" tabindex="-1"></a><span class="in"> filter(COUNTY %in% southern_tier_counties)</span></span> 7164<span id="cb21-384"><a href="#cb21-384" aria-hidden="true" tabindex="-1"></a><span class="in"> </span></span> 7165<span id="cb21-385"><a href="#cb21-385" aria-hidden="true" tabindex="-1"></a><span class="in"> # load shapefile</span></span> 7166<span id="cb21-386"><a href="#cb21-386" aria-hidden="true" tabindex="-1"></a><span class="in"> s <- read_ipums_sf(nhgis_bg_shps, file_select = matches(as.character(yr)))</span></span> 7167<span id="cb21-387"><a href="#cb21-387" aria-hidden="true" tabindex="-1"></a><span class="in"> </span></span> 7168<span id="cb21-388"><a href="#cb21-388" aria-hidden="true" tabindex="-1"></a><span class="in"> # 2009</span></span> 7169<span id="cb21-389"><a href="#cb21-389" aria-hidden="true" tabindex="-1"></a><span class="in"> if (yr == 2009) {</span></span> 7170<span id="cb21-390"><a href="#cb21-390" aria-hidden="true" tabindex="-1"></a><span class="in"> d <- </span></span> 7171<span id="cb21-391"><a href="#cb21-391" aria-hidden="true" tabindex="-1"></a><span class="in"> d %>%</span></span> 7172<span id="cb21-392"><a href="#cb21-392" aria-hidden="true" tabindex="-1"></a><span class="in"> mutate(GEOID = str_split(GEOID, 'US', simplify = TRUE)[,2]) %>%</span></span> 7173<span id="cb21-393"><a href="#cb21-393" aria-hidden="true" tabindex="-1"></a><span class="in"> mutate_at(vars(GEOID), as.numeric) %>%</span></span> 7174<span id="cb21-394"><a href="#cb21-394" aria-hidden="true" tabindex="-1"></a><span class="in"> rename(</span></span> 7175<span id="cb21-395"><a href="#cb21-395" aria-hidden="true" tabindex="-1"></a><span class="in"> year = YEAR, county = COUNTY, name = NAME_E,</span></span> 7176<span id="cb21-396"><a href="#cb21-396" aria-hidden="true" tabindex="-1"></a><span class="in"> tot_pop = 39, tot_hhs = 40, tot_families = 41,</span></span> 7177<span id="cb21-397"><a href="#cb21-397" aria-hidden="true" tabindex="-1"></a><span class="in"> tot_pop_over_15 = 49, males_divorced = 58, females_divorced = 67,</span></span> 7178<span id="cb21-398"><a href="#cb21-398" aria-hidden="true" tabindex="-1"></a><span class="in"> tot_pop_pov_count = 68, pop_in_poverty = 69, agg_fam_income = 120,</span></span> 7179<span id="cb21-399"><a href="#cb21-399" aria-hidden="true" tabindex="-1"></a><span class="in"> per_capita_income = 121, pop_over_16 = 122, males_didnt_work = 146,</span></span> 7180<span id="cb21-400"><a href="#cb21-400" aria-hidden="true" tabindex="-1"></a><span class="in"> females_didnt_work = 170, tot_housing_units = 171, vacant_housing_units = 172,</span></span> 7181<span id="cb21-401"><a href="#cb21-401" aria-hidden="true" tabindex="-1"></a><span class="in"> renter_occ_housing_units = 182, agg_gross_rent = 186</span></span> 7182<span id="cb21-402"><a href="#cb21-402" aria-hidden="true" tabindex="-1"></a><span class="in"> ) %>%</span></span> 7183<span id="cb21-403"><a href="#cb21-403" aria-hidden="true" tabindex="-1"></a><span class="in"> .[,c(2, 8, 37:41, 49, 58, 67:69, 120:122, 146, 170:172, 182, 186)] %>%</span></span> 7184<span id="cb21-404"><a href="#cb21-404" aria-hidden="true" tabindex="-1"></a><span class="in"> relocate(GEOID, .before = year) %>%</span></span> 7185<span id="cb21-405"><a href="#cb21-405" aria-hidden="true" tabindex="-1"></a><span class="in"> relocate(c(county, year), .after = name)</span></span> 7186<span id="cb21-406"><a href="#cb21-406" aria-hidden="true" tabindex="-1"></a><span class="in"> </span></span> 7187<span id="cb21-407"><a href="#cb21-407" aria-hidden="true" tabindex="-1"></a><span class="in"> s <-</span></span> 7188<span id="cb21-408"><a href="#cb21-408" aria-hidden="true" tabindex="-1"></a><span class="in"> s %>%</span></span> 7189<span id="cb21-409"><a href="#cb21-409" aria-hidden="true" tabindex="-1"></a><span class="in"> mutate(GEOID = as.numeric(paste(STATEFP00, COUNTYFP00, TRACTCE00, BLKGRPCE00, sep=''))) %>%</span></span> 7190<span id="cb21-410"><a href="#cb21-410" aria-hidden="true" tabindex="-1"></a><span class="in"> filter(GEOID %in% (d$GEOID)) %>%</span></span> 7191<span id="cb21-411"><a href="#cb21-411" aria-hidden="true" tabindex="-1"></a><span class="in"> select(GEOID, geometry) %>%</span></span> 7192<span id="cb21-412"><a href="#cb21-412" aria-hidden="true" tabindex="-1"></a><span class="in"> st_transform(crs = st_crs(study_bgs))</span></span> 7193<span id="cb21-413"><a href="#cb21-413" aria-hidden="true" tabindex="-1"></a><span class="in"> } else if (yr == 2010) { # 2010</span></span> 7194<span id="cb21-414"><a href="#cb21-414" aria-hidden="true" tabindex="-1"></a><span class="in"> d <-</span></span> 7195<span id="cb21-415"><a href="#cb21-415" aria-hidden="true" tabindex="-1"></a><span class="in"> d %>%</span></span> 7196<span id="cb21-416"><a href="#cb21-416" aria-hidden="true" tabindex="-1"></a><span class="in"> mutate(GEOID = str_split(GEOID, 'US', simplify = TRUE)[,2]) %>%</span></span> 7197<span id="cb21-417"><a href="#cb21-417" aria-hidden="true" tabindex="-1"></a><span class="in"> mutate_at(vars(GEOID), as.numeric) %>%</span></span> 7198<span id="cb21-418"><a href="#cb21-418" aria-hidden="true" tabindex="-1"></a><span class="in"> rename(</span></span> 7199<span id="cb21-419"><a href="#cb21-419" aria-hidden="true" tabindex="-1"></a><span class="in"> year = YEAR, county = COUNTY, name = NAME_E,</span></span> 7200<span id="cb21-420"><a href="#cb21-420" aria-hidden="true" tabindex="-1"></a><span class="in"> tot_pop = 41, tot_hhs = 42, tot_families = 43,</span></span> 7201<span id="cb21-421"><a href="#cb21-421" aria-hidden="true" tabindex="-1"></a><span class="in"> tot_pop_over_15 = 51, males_divorced = 60, females_divorced = 69,</span></span> 7202<span id="cb21-422"><a href="#cb21-422" aria-hidden="true" tabindex="-1"></a><span class="in"> tot_pop_pov_count = 70, pop_in_poverty = 71, agg_fam_income = 122,</span></span> 7203<span id="cb21-423"><a href="#cb21-423" aria-hidden="true" tabindex="-1"></a><span class="in"> per_capita_income = 123, pop_over_16 = 124, males_didnt_work = 148,</span></span> 7204<span id="cb21-424"><a href="#cb21-424" aria-hidden="true" tabindex="-1"></a><span class="in"> females_didnt_work = 172, tot_housing_units = 173, vacant_housing_units = 174,</span></span> 7205<span id="cb21-425"><a href="#cb21-425" aria-hidden="true" tabindex="-1"></a><span class="in"> renter_occ_housing_units = 184, agg_gross_rent = 188</span></span> 7206<span id="cb21-426"><a href="#cb21-426" aria-hidden="true" tabindex="-1"></a><span class="in"> ) %>%</span></span> 7207<span id="cb21-427"><a href="#cb21-427" aria-hidden="true" tabindex="-1"></a><span class="in"> .[,c(2, 8, 37, 40:43, 51, 60, 69:71, 122:124, 148, 172:174, 184, 188)] %>%</span></span> 7208<span id="cb21-428"><a href="#cb21-428" aria-hidden="true" tabindex="-1"></a><span class="in"> relocate(GEOID, .before = year) %>%</span></span> 7209<span id="cb21-429"><a href="#cb21-429" aria-hidden="true" tabindex="-1"></a><span class="in"> relocate(c(county, year), .after = name)</span></span> 7210<span id="cb21-430"><a href="#cb21-430" aria-hidden="true" tabindex="-1"></a><span class="in"> </span></span> 7211<span id="cb21-431"><a href="#cb21-431" aria-hidden="true" tabindex="-1"></a><span class="in"> s <- s %>%</span></span> 7212<span id="cb21-432"><a href="#cb21-432" aria-hidden="true" tabindex="-1"></a><span class="in"> rename(GEOID = `GEOID10`) %>%</span></span> 7213<span id="cb21-433"><a href="#cb21-433" aria-hidden="true" tabindex="-1"></a><span class="in"> mutate(across(GEOID, as.numeric)) %>%</span></span> 7214<span id="cb21-434"><a href="#cb21-434" aria-hidden="true" tabindex="-1"></a><span class="in"> filter(GEOID %in% (d$GEOID)) %>%</span></span> 7215<span id="cb21-435"><a href="#cb21-435" aria-hidden="true" tabindex="-1"></a><span class="in"> select(GEOID, geometry) %>%</span></span> 7216<span id="cb21-436"><a href="#cb21-436" aria-hidden="true" tabindex="-1"></a><span class="in"> st_transform(crs = st_crs(study_bgs))</span></span> 7217<span id="cb21-437"><a href="#cb21-437" aria-hidden="true" tabindex="-1"></a><span class="in"> } else if (yr >= 2011 & yr <= 2020) { # 2011 to 2020</span></span> 7218<span id="cb21-438"><a href="#cb21-438" aria-hidden="true" tabindex="-1"></a><span class="in"> d <-</span></span> 7219<span id="cb21-439"><a href="#cb21-439" aria-hidden="true" tabindex="-1"></a><span class="in"> d %>%</span></span> 7220<span id="cb21-440"><a href="#cb21-440" aria-hidden="true" tabindex="-1"></a><span class="in"> mutate(GEOID = str_split(GEOID, 'US', simplify = TRUE)[,2]) %>%</span></span> 7221<span id="cb21-441"><a href="#cb21-441" aria-hidden="true" tabindex="-1"></a><span class="in"> mutate(across(GEOID, as.numeric)) %>%</span></span> 7222<span id="cb21-442"><a href="#cb21-442" aria-hidden="true" tabindex="-1"></a><span class="in"> rename(</span></span> 7223<span id="cb21-443"><a href="#cb21-443" aria-hidden="true" tabindex="-1"></a><span class="in"> year = YEAR, county = COUNTY, name = NAME_E,</span></span> 7224<span id="cb21-444"><a href="#cb21-444" aria-hidden="true" tabindex="-1"></a><span class="in"> tot_pop = 42, tot_hhs = 43, tot_families = 44,</span></span> 7225<span id="cb21-445"><a href="#cb21-445" aria-hidden="true" tabindex="-1"></a><span class="in"> tot_pop_over_15 = 52, males_divorced = 61, females_divorced = 70,</span></span> 7226<span id="cb21-446"><a href="#cb21-446" aria-hidden="true" tabindex="-1"></a><span class="in"> tot_pop_pov_count = 71, pop_in_poverty = 72, agg_fam_income = 123,</span></span> 7227<span id="cb21-447"><a href="#cb21-447" aria-hidden="true" tabindex="-1"></a><span class="in"> per_capita_income = 124, pop_over_16 = 125, males_didnt_work = 149,</span></span> 7228<span id="cb21-448"><a href="#cb21-448" aria-hidden="true" tabindex="-1"></a><span class="in"> females_didnt_work = 173, tot_housing_units = 174, vacant_housing_units = 175,</span></span> 7229<span id="cb21-449"><a href="#cb21-449" aria-hidden="true" tabindex="-1"></a><span class="in"> renter_occ_housing_units = 185, agg_gross_rent = 189</span></span> 7230<span id="cb21-450"><a href="#cb21-450" aria-hidden="true" tabindex="-1"></a><span class="in"> ) %>%</span></span> 7231<span id="cb21-451"><a href="#cb21-451" aria-hidden="true" tabindex="-1"></a><span class="in"> .[,c(2, 8, 38, 41:44, 52, 61, 70:72, 123:125, 149, 173:175, 185, 189)] %>%</span></span> 7232<span id="cb21-452"><a href="#cb21-452" aria-hidden="true" tabindex="-1"></a><span class="in"> relocate(GEOID, .before = year) %>%</span></span> 7233<span id="cb21-453"><a href="#cb21-453" aria-hidden="true" tabindex="-1"></a><span class="in"> relocate(c(county, year), .after = name)</span></span> 7234<span id="cb21-454"><a href="#cb21-454" aria-hidden="true" tabindex="-1"></a><span class="in"> </span></span> 7235<span id="cb21-455"><a href="#cb21-455" aria-hidden="true" tabindex="-1"></a><span class="in"> s <- s %>%</span></span> 7236<span id="cb21-456"><a href="#cb21-456" aria-hidden="true" tabindex="-1"></a><span class="in"> filter(GEOID %in% (d$GEOID)) %>%</span></span> 7237<span id="cb21-457"><a href="#cb21-457" aria-hidden="true" tabindex="-1"></a><span class="in"> mutate(across(GEOID, as.numeric)) %>%</span></span> 7238<span id="cb21-458"><a href="#cb21-458" aria-hidden="true" tabindex="-1"></a><span class="in"> select(GEOID, geometry) %>%</span></span> 7239<span id="cb21-459"><a href="#cb21-459" aria-hidden="true" tabindex="-1"></a><span class="in"> st_transform(crs = st_crs(study_bgs))</span></span> 7240<span id="cb21-460"><a href="#cb21-460" aria-hidden="true" tabindex="-1"></a><span class="in"> } else if (yr >= 2021 & yr <= 2022) { # 2021 to 2022</span></span> 7241<span id="cb21-461"><a href="#cb21-461" aria-hidden="true" tabindex="-1"></a><span class="in"> d <-</span></span> 7242<span id="cb21-462"><a href="#cb21-462" aria-hidden="true" tabindex="-1"></a><span class="in"> d %>%</span></span> 7243<span id="cb21-463"><a href="#cb21-463" aria-hidden="true" tabindex="-1"></a><span class="in"> mutate(GEO_ID = str_split(GEO_ID, 'US', simplify = TRUE)[,2]) %>%</span></span> 7244<span id="cb21-464"><a href="#cb21-464" aria-hidden="true" tabindex="-1"></a><span class="in"> mutate_at(vars(GEO_ID), as.numeric) %>%</span></span> 7245<span id="cb21-465"><a href="#cb21-465" aria-hidden="true" tabindex="-1"></a><span class="in"> rename(</span></span> 7246<span id="cb21-466"><a href="#cb21
7246-466" aria-hidden="true" tabindex="-1"></a><span class="in"> year = YEAR, county = COUNTY, name = NAME_E,</span></span> 7247<span id="cb21-467"><a href="#cb21-467" aria-hidden="true" tabindex="-1"></a><span class="in"> tot_pop = 43, tot_hhs = 44, tot_families = 45,</span></span> 7248<span id="cb21-468"><a href="#cb21-468" aria-hidden="true" tabindex="-1"></a><span class="in"> tot_pop_over_15 = 53, males_divorced = 62, females_divorced = 71,</span></span> 7249<span id="cb21-469"><a href="#cb21-469" aria-hidden="true" tabindex="-1"></a><span class="in"> tot_pop_pov_count = 72, pop_in_poverty = 73, agg_fam_income = 124,</span></span> 7250<span id="cb21-470"><a href="#cb21-470" aria-hidden="true" tabindex="-1"></a><span class="in"> per_capita_income = 125, pop_over_16 = 126, males_didnt_work = 150,</span></span> 7251<span id="cb21-471"><a href="#cb21-471" aria-hidden="true" tabindex="-1"></a><span class="in"> females_didnt_work = 174, tot_housing_units = 175, vacant_housing_units = 176,</span></span> 7252<span id="cb21-472"><a href="#cb21-472" aria-hidden="true" tabindex="-1"></a><span class="in"> renter_occ_housing_units = 186, agg_gross_rent = 190</span></span> 7253<span id="cb21-473"><a href="#cb21-473" aria-hidden="true" tabindex="-1"></a><span class="in"> ) %>%</span></span> 7254<span id="cb21-474"><a href="#cb21-474" aria-hidden="true" tabindex="-1"></a><span class="in"> .[,c(2, 8, 38, 42:45, 53, 62, 71:73, 124:126, 150, 174:176, 186, 190)] %>%</span></span> 7255<span id="cb21-475"><a href="#cb21-475" aria-hidden="true" tabindex="-1"></a><span class="in"> rename(GEOID = GEO_ID) %>%</span></span> 7256<span id="cb21-476"><a href="#cb21-476" aria-hidden="true" tabindex="-1"></a><span class="in"> relocate(GEOID, .before = year) %>%</span></span> 7257<span id="cb21-477"><a href="#cb21-477" aria-hidden="true" tabindex="-1"></a><span class="in"> relocate(c(county, year), .after = name)</span></span> 7258<span id="cb21-478"><a href="#cb21-478" aria-hidden="true" tabindex="-1"></a><span class="in"> </span></span> 7259<span id="cb21-479"><a href="#cb21-479" aria-hidden="true" tabindex="-1"></a><span class="in"> s <- s %>%</span></span> 7260<span id="cb21-480"><a href="#cb21-480" aria-hidden="true" tabindex="-1"></a><span class="in"> filter(GEOID %in% (d$GEOID)) %>%</span></span> 7261<span id="cb21-481"><a href="#cb21-481" aria-hidden="true" tabindex="-1"></a><span class="in"> mutate(across(GEOID, as.numeric)) %>%</span></span> 7262<span id="cb21-482"><a href="#cb21-482" aria-hidden="true" tabindex="-1"></a><span class="in"> select(GEOID, geometry) %>%</span></span> 7263<span id="cb21-483"><a href="#cb21-483" aria-hidden="true" tabindex="-1"></a><span class="in"> st_transform(crs = st_crs(study_bgs))</span></span> 7264<span id="cb21-484"><a href="#cb21-484" aria-hidden="true" tabindex="-1"></a><span class="in"> } else { # 2023</span></span> 7265<span id="cb21-485"><a href="#cb21-485" aria-hidden="true" tabindex="-1"></a><span class="in"> d <-</span></span> 7266<span id="cb21-486"><a href="#cb21-486" aria-hidden="true" tabindex="-1"></a><span class="in"> d %>%</span></span> 7267<span id="cb21-487"><a href="#cb21-487" aria-hidden="true" tabindex="-1"></a><span class="in"> mutate(GEO_ID = str_split(GEO_ID, 'US', simplify = TRUE)[,2]) %>%</span></span> 7268<span id="cb21-488"><a href="#cb21-488" aria-hidden="true" tabindex="-1"></a><span class="in"> mutate_at(vars(GEO_ID), as.numeric) %>%</span></span> 7269<span id="cb21-489"><a href="#cb21-489" aria-hidden="true" tabindex="-1"></a><span class="in"> rename(</span></span> 7270<span id="cb21-490"><a href="#cb21-490" aria-hidden="true" tabindex="-1"></a><span class="in"> year = YEAR, county = COUNTY, name = NAME_E,</span></span> 7271<span id="cb21-491"><a href="#cb21-491" aria-hidden="true" tabindex="-1"></a><span class="in"> tot_pop = 42, tot_hhs = 43, tot_families = 44,</span></span> 7272<span id="cb21-492"><a href="#cb21-492" aria-hidden="true" tabindex="-1"></a><span class="in"> tot_pop_over_15 = 52, males_divorced = 61, females_divorced = 70,</span></span> 7273<span id="cb21-493"><a href="#cb21-493" aria-hidden="true" tabindex="-1"></a><span class="in"> tot_pop_pov_count = 71, pop_in_poverty = 72, agg_fam_income = 123,</span></span> 7274<span id="cb21-494"><a href="#cb21-494" aria-hidden="true" tabindex="-1"></a><span class="in"> per_capita_income = 124, pop_over_16 = 125, males_didnt_work = 149,</span></span> 7275<span id="cb21-495"><a href="#cb21-495" aria-hidden="true" tabindex="-1"></a><span class="in"> females_didnt_work = 173, tot_housing_units = 174, vacant_housing_units = 175,</span></span> 7276<span id="cb21-496"><a href="#cb21-496" aria-hidden="true" tabindex="-1"></a><span class="in"> renter_occ_housing_units = 185, agg_gross_rent = 189</span></span> 7277<span id="cb21-497"><a href="#cb21-497" aria-hidden="true" tabindex="-1"></a><span class="in"> ) %>%</span></span> 7278<span id="cb21-498"><a href="#cb21-498" aria-hidden="true" tabindex="-1"></a><span class="in"> .[,c(2, 8, 37, 41:44, 52, 61, 70:72, 123:125, 149, 173:175, 185, 189)] %>%</span></span> 7279<span id="cb21-499"><a href="#cb21-499" aria-hidden="true" tabindex="-1"></a><span class="in"> rename(GEOID = GEO_ID) %>%</span></span> 7280<span id="cb21-500"><a href="#cb21-500" aria-hidden="true" tabindex="-1"></a><span class="in"> relocate(GEOID, .before = year) %>%</span></span> 7281<span id="cb21-501"><a href="#cb21-501" aria-hidden="true" tabindex="-1"></a><span class="in"> relocate(c(county, year), .after = name)</span></span> 7282<span id="cb21-502"><a href="#cb21-502" aria-hidden="true" tabindex="-1"></a><span class="in"> </span></span> 7283<span id="cb21-503"><a href="#cb21-503" aria-hidden="true" tabindex="-1"></a><span class="in"> s <- </span></span> 7284<span id="cb21-504"><a href="#cb21-504" aria-hidden="true" tabindex="-1"></a><span class="in"> s %>%</span></span> 7285<span id="cb21-505"><a href="#cb21-505" aria-hidden="true" tabindex="-1"></a><span class="in"> filter(GEOID %in% (d$GEOID)) %>%</span></span> 7286<span id="cb21-506"><a href="#cb21-506" aria-hidden="true" tabindex="-1"></a><span class="in"> mutate(across(GEOID, as.numeric)) %>%</span></span> 7287<span id="cb21-507"><a href="#cb21-507" aria-hidden="true" tabindex="-1"></a><span class="in"> select(GEOID, geometry) %>%</span></span> 7288<span id="cb21-508"><a href="#cb21-508" aria-hidden="true" tabindex="-1"></a><span class="in"> st_transform(crs = st_crs(study_bgs))</span></span> 7289<span id="cb21-509"><a href="#cb21-509" aria-hidden="true" tabindex="-1"></a><span class="in"> }</span></span> 7290<span id="cb21-510"><a href="#cb21-510" aria-hidden="true" tabindex="-1"></a><span class="in"> </span></span> 7291<span id="cb21-511"><a href="#cb21-511" aria-hidden="true" tabindex="-1"></a><span class="in"> # convert year and data columns to numeric, then change negative and NA values to 0</span></span> 7292<span id="cb21-512"><a href="#cb21-512" aria-hidden="true" tabindex="-1"></a><span class="in"> data <- </span></span> 7293<span id="cb21-513"><a href="#cb21-513" aria-hidden="true" tabindex="-1"></a><span class="in"> d %>%</span></span> 7294<span id="cb21-514"><a href="#cb21-514" aria-hidden="true" tabindex="-1"></a><span class="in"> mutate(across(c(tot_pop:agg_gross_rent), as.numeric),</span></span> 7295<span id="cb21-515"><a href="#cb21-515" aria-hidden="true" tabindex="-1"></a><span class="in"> across(c(tot_pop:agg_gross_rent), ~ ifelse(is.na(.) | . < 0 | . == '.', '0', .)))</span></span> 7296<span id="cb21-516"><a href="#cb21-516" aria-hidden="true" tabindex="-1"></a><span class="in"> </span></span> 7297<span id="cb21-517"><a href="#cb21-517" aria-hidden="true" tabindex="-1"></a><span class="in"> data[is.na(data)] <- 0</span></span> 7298<span id="cb21-518"><a href="#cb21-518" aria-hidden="true" tabindex="-1"></a><span class="in"> </span></span> 7299<span id="cb21-519"><a href="#cb21-519" aria-hidden="true" tabindex="-1"></a><span class="in"> # join spatial features with data then transform to NAD83</span></span> 7300<span id="cb21-520"><a href="#cb21-520" aria-hidden="true" tabindex="-1"></a><span class="in"> full_data <- </span></span> 7301<span id="cb21-521"><a href="#cb21-521" aria-hidden="true" tabindex="-1"></a><span class="in"> inner_join(s, data, by = 'GEOID') %>% </span></span> 7302<span id="cb21-522"><a href="#cb21-522" aria-hidden="true" tabindex="-1"></a><span class="in"> relocate(geometry, .after = last_col()) %>%</span></span> 7303<span id="cb21-523"><a href="#cb21-523" aria-hidden="true" tabindex="-1"></a><span class="in"> st_transform(crs = st_crs(study_bgs))</span></span> 7304<span id="cb21-524"><a href="#cb21-524" aria-hidden="true" tabindex="-1"></a><span class="in"> </span></span> 7305<span id="cb21-525"><a href="#cb21-525" aria-hidden="true" tabindex="-1"></a><span class="in"> # add to bg_data_yrs and move onto the next year after doing this</span></span> 7306<span id="cb21-526"><a href="#cb21-526" aria-hidden="true" tabindex="-1"></a><span class="in"> bg_data_yrs[[as.character(yr)]] <- full_data</span></span> 7307<span id="cb21-527"><a href="#cb21-527" aria-hidden="true" tabindex="-1"></a><span class="in">}</span></span> 7308<span id="cb21-528"><a href="#cb21-528" aria-hidden="true" tabindex="-1"></a></span> 7309<span id="cb21-529"><a href="#cb21-529" aria-hidden="true" tabindex="-1"></a><span class="in"># combine block group data into single dataframe</span></span> 7310<span id="cb21-530"><a href="#cb21-530" aria-hidden="true" tabindex="-1"></a><span class="in">st_bgs <- </span></span> 7311<span id="cb21-531"><a href="#cb21-531" aria-hidden="true" tabindex="-1"></a><span class="in"> bind_rows(bg_data_yrs) %>%</span></span> 7312<span id="cb21-532"><a href="#cb21-532" aria-hidden="true" tabindex="-1"></a><span class="in"> mutate(</span></span> 7313<span id="cb21-533"><a href="#cb21-533" aria-hidden="true" tabindex="-1"></a><span class="in"> across(c(GEOID, year, agg_fam_income, per_capita_income, agg_gross_rent), as.numeric),</span></span> 7314<span id="cb21-534"><a href="#cb21-534" aria-hidden="true" tabindex="-1"></a><span class="in"> pop_over_16_didnt_work = males_didnt_work + females_didnt_work</span></span> 7315<span id="cb21-535"><a href="#cb21-535" aria-hidden="true" tabindex="-1"></a><span class="in"> ) %>%</span></span> 7316<span id="cb21-536"><a href="#cb21-536" aria-hidden="true" tabindex="-1"></a><span class="in"> relocate(geometry, .after = last_col())</span></span> 7317<span id="cb21-537"><a href="#cb21-537" aria-hidden="true" tabindex="-1"></a><span class="in">st_bgs[is.na(st_bgs)] <- 0</span></span> 7318<span id="cb21-538"><a href="#cb21-538" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span> 7319<span id="cb21-539"><a href="#cb21-539" aria-hidden="true" tabindex="-1"></a></span> 7320<span id="cb21-540"><a href="#cb21-540" aria-hidden="true" tabindex="-1"></a><span class="in">```{=latex}</span></span> 7321<span id="cb21-541"><a href="#cb21-541" aria-hidden="true" tabindex="-1"></a><span class="in">\newpage</span></span> 7322<span id="cb21-542"><a href="#cb21-542" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span> 7323<span id="cb21-543"><a href="#cb21-543" aria-hidden="true" tabindex="-1"></a></span> 7324<span id="cb21-544"><a href="#cb21-544" aria-hidden="true" tabindex="-1"></a><span class="fu">### Population-Weighted Areal Interpolation and Distressed Status Determination</span></span> 7325<span id="cb21-545"><a href="#cb21-545" aria-hidden="true" tabindex="-1"></a></span> 7326<span id="cb21-546"><a href="#cb21-546" aria-hidden="true" tabindex="-1"></a>Apply population-weighted areal interpolation using centroid assignment to the 2009 - 2019 block group boundaries and ensure that they follow the 2020 boundaries. Estimate all data by applying a scaling factor to each variable after interpolation. Merge all the data together, recombine with 2020-2023 data, then calculate remaining variables.</span> 7327<span id="cb21-547"><a href="#cb21-547" aria-hidden="true" tabindex="-1"></a></span> 7328<span id="cb21-548"><a href="#cb21-548" aria-hidden="true" tabindex="-1"></a><span class="in">```{r pw_areal_interpolation, message=F, warning=F}</span></span> 7329<span id="cb21-549"><a href="#cb21-549" aria-hidden="true" tabindex="-1"></a><span class="in">
7329#| cache: false</span></span> 7330<span id="cb21-550"><a href="#cb21-550" aria-hidden="true" tabindex="-1"></a></span> 7331<span id="cb21-551"><a href="#cb21-551" aria-hidden="true" tabindex="-1"></a><span class="in"># isolate post-2020 block group data</span></span> 7332<span id="cb21-552"><a href="#cb21-552" aria-hidden="true" tabindex="-1"></a><span class="in">st_bgs.post_2020 <- </span></span> 7333<span id="cb21-553"><a href="#cb21-553" aria-hidden="true" tabindex="-1"></a><span class="in"> st_bgs %>%</span></span> 7334<span id="cb21-554"><a href="#cb21-554" aria-hidden="true" tabindex="-1"></a><span class="in"> filter(year >= 2020) %>%</span></span> 7335<span id="cb21-555"><a href="#cb21-555" aria-hidden="true" tabindex="-1"></a><span class="in"> select(-name) %>%</span></span> 7336<span id="cb21-556"><a href="#cb21-556" aria-hidden="true" tabindex="-1"></a><span class="in"> st_make_valid()</span></span> 7337<span id="cb21-557"><a href="#cb21-557" aria-hidden="true" tabindex="-1"></a></span> 7338<span id="cb21-558"><a href="#cb21-558" aria-hidden="true" tabindex="-1"></a><span class="in"># get only 2020 block groups</span></span> 7339<span id="cb21-559"><a href="#cb21-559" aria-hidden="true" tabindex="-1"></a><span class="in">st_bgs.2020 <-</span></span> 7340<span id="cb21-560"><a href="#cb21-560" aria-hidden="true" tabindex="-1"></a><span class="in"> st_bgs %>%</span></span> 7341<span id="cb21-561"><a href="#cb21-561" aria-hidden="true" tabindex="-1"></a><span class="in"> filter(year == 2020) %>%</span></span> 7342<span id="cb21-562"><a href="#cb21-562" aria-hidden="true" tabindex="-1"></a><span class="in"> st_make_valid()</span></span> 7343<span id="cb21-563"><a href="#cb21-563" aria-hidden="true" tabindex="-1"></a></span> 7344<span id="cb21-564"><a href="#cb21-564" aria-hidden="true" tabindex="-1"></a><span class="in"># get 2020 blocks to use as weights in interpolation</span></span> 7345<span id="cb21-565"><a href="#cb21-565" aria-hidden="true" tabindex="-1"></a><span class="in">st_blocks.2020 <- </span></span> 7346<span id="cb21-566"><a href="#cb21-566" aria-hidden="true" tabindex="-1"></a><span class="in"> tigris::blocks(state = 'NY', year = 2020) %>%</span></span> 7347<span id="cb21-567"><a href="#cb21-567" aria-hidden="true" tabindex="-1"></a><span class="in"> filter(COUNTYFP20 %in% county_fips) %>%</span></span> 7348<span id="cb21-568"><a href="#cb21-568" aria-hidden="true" tabindex="-1"></a><span class="in"> st_make_valid()</span></span> 7349<span id="cb21-569"><a href="#cb21-569" aria-hidden="true" tabindex="-1"></a></span> 7350<span id="cb21-570"><a href="#cb21-570" aria-hidden="true" tabindex="-1"></a><span class="in"># get list of variables to scale</span></span> 7351<span id="cb21-571"><a href="#cb21-571" aria-hidden="true" tabindex="-1"></a><span class="in">vars_to_scale <- c(</span></span> 7352<span id="cb21-572"><a href="#cb21-572" aria-hidden="true" tabindex="-1"></a><span class="in"> "tot_pop", "tot_hhs", "tot_families", "tot_pop_over_15", </span></span> 7353<span id="cb21-573"><a href="#cb21-573" aria-hidden="true" tabindex="-1"></a><span class="in"> "males_divorced", "females_divorced", "tot_pop_pov_count", </span></span> 7354<span id="cb21-574"><a href="#cb21-574" aria-hidden="true" tabindex="-1"></a><span class="in"> "pop_in_poverty", "agg_fam_income", "agg_income", </span></span> 7355<span id="cb21-575"><a href="#cb21-575" aria-hidden="true" tabindex="-1"></a><span class="in"> "pop_over_16", "males_didnt_work", "females_didnt_work", </span></span> 7356<span id="cb21-576"><a href="#cb21-576" aria-hidden="true" tabindex="-1"></a><span class="in"> "tot_housing_units", "vacant_housing_units", </span></span> 7357<span id="cb21-577"><a href="#cb21-577" aria-hidden="true" tabindex="-1"></a><span class="in"> "renter_occ_housing_units", "agg_gross_rent", "pop_over_16_didnt_work"</span></span> 7358<span id="cb21-578"><a href="#cb21-578" aria-hidden="true" tabindex="-1"></a><span class="in">)</span></span> 7359<span id="cb21-579"><a href="#cb21-579" aria-hidden="true" tabindex="-1"></a></span> 7360<span id="cb21-580"><a href="#cb21-580" aria-hidden="true" tabindex="-1"></a><span class="in">
7360# interpolate block groups boundaries and new boundary data for each year,</span></span> 7361<span id="cb21-581"><a href="#cb21-581" aria-hidden="true" tabindex="-1"></a><span class="in"># then store in interpolated data list</span></span> 7362<span id="cb21-582"><a href="#cb21-582" aria-hidden="true" tabindex="-1"></a><span class="in">interpolated_data <- lapply(2009:2019, interpolate_data)</span></span> 7363<span id="cb21-583"><a href="#cb21-583" aria-hidden="true" tabindex="-1"></a></span> 7364<span id="cb21-584"><a href="#cb21-584" aria-hidden="true" tabindex="-1"></a><span class="in"># bind interpolated data together</span></span> 7365<span id="cb21-585"><a href="#cb21-585" aria-hidden="true" tabindex="-1"></a><span class="in">st_interpolated.pre_2020 <- </span></span> 7366<span id="cb21-586"><a href="#cb21-586" aria-hidden="true" tabindex="-1"></a><span class="in"> bind_rows(interpolated_data) %>%</span></span> 7367<span id="cb21-587"><a href="#cb21-587" aria-hidden="true" tabindex="-1"></a><span class="in"> relocate(geometry, .after = last_col())</span></span> 7368<span id="cb21-588"><a href="#cb21-588" aria-hidden="true" tabindex="-1"></a></span> 7369<span id="cb21-589"><a href="#cb21-589" aria-hidden="true" tabindex="-1"></a><span class="in"># bind yearly interpolated data into single dataframe</span></span> 7370<span id="cb21-590"><a href="#cb21-590" aria-hidden="true" tabindex="-1"></a><span class="in"># calculate total divorced population and determine distressed status</span></span> 7371<span id="cb21-591"><a href="#cb21-591" aria-hidden="true" tabindex="-1"></a><span class="in">st_bgs_final <- </span></span> 7372<span id="cb21-592"><a href="#cb21-592" aria-hidden="true" tabindex="-1"></a><span class="in"> bind_rows(st_interpolated.pre_2020,</span></span> 7373<span id="cb21-593"><a href="#cb21-593" aria-hidden="true" tabindex="-1"></a><span class="in"> st_bgs.post_2020 %>% mutate(year = as.numeric(year))) %>%</span></span> 7374<span id="cb21-594"><a href="#cb21-594" aria-hidden="true" tabindex="-1"></a><span class="in"> left_join(us_data %>% select(year, median_income, pct_below_poverty),</span></span> 7375<span id="cb21-595"><a href="#cb21-595" aria-hidden="true" tabindex="-1"></a><span class="in"> by = 'year',</span></span> 7376<span id="cb21-596"><a href="#cb21-596" aria-hidden="true" tabindex="-1"></a><span class="in"> suffix = c('', '')) %>%</span></span> 7377<span id="cb21-597"><a href="#cb21-597" aria-hidden="true" tabindex="-1"></a><span class="in"> rename(median_income_us = median_income,</span></span> 7378<span id="cb21-598"><a href="#cb21-598" aria-hidden="true" tabindex="-1"></a><span class="in"> pct_below_poverty_us = pct_below_poverty) %>%</span></span> 7379<span id="cb21-599"><a href="#cb21-599" aria-hidden="true" tabindex="-1"></a><span class="in"> mutate(</span></span> 7380<span id="cb21-600"><a href="#cb21-600" aria-hidden="true" tabindex="-1"></a><span class="in"> county = study_counties[data.frame(</span></span> 7381<span id="cb21-601"><a href="#cb21-601" aria-hidden="true" tabindex="-1"></a><span class="in"> st_intersects(st_centroid(.), study_counties %>% select(NAME))</span></span> 7382<span id="cb21-602"><a href="#cb21-602" aria-hidden="true" tabindex="-1"></a><span class="in"> )$col.id,]$NAME,</span></span> 7383<span id="cb21-603"><a href="#cb21-603" aria-hidden="true" tabindex="-1"></a><span class="in"> area_sqmi = as.numeric(st_area(.) * 3.861E-7),</span></span> 7384<span id="cb21-604"><a href="#cb21-604" aria-hidden="true" tabindex="-1"></a><span class="in"> per_capita_income = agg_income / tot_pop,</span></span> 7385<span id="cb21-605"><a href="#cb21-605" aria-hidden="true" tabindex="-1"></a><span class="in"> tot_pop_density = tot_pop / area_sqmi,</span></span> 7386<span id="cb21-606"><a href="#cb21-606" aria-hidden="true" tabindex="-1"></a><span class="in"> pct_below_poverty = pop_in_poverty / tot_pop_pov_count,</span></span> 7387<span id="cb21-607"><a href="#cb21-607" aria-hidden="true" tabindex="-1"></a><span class="in"> pop_over_15_divorced = males_divorced + females_divorced,</span></span> 7388<span id="cb21-608"><a href="#cb21-608" aria-hidden="true" tabindex="-1"></a><span class="in"> pct_divorced = pop_over_15_divorced / tot_pop_over_15,</span></span> 7389<span id="cb21-609"><a href="#cb21-609" aria-hidden="true" tabindex="-1"></a><span class="in"> pct_didnt_work_past_yr = pop_over_16_didnt_work / pop_over_16,</span></span> 7390<span id="cb21-610"><a href="#cb21-610" aria-hidden="true" tabindex="-1"></a><span class="in"> pct_family_hhs = tot_families / tot_hhs,</span></span> 7391<span id="cb21-611"><a href="#cb21-611" aria-hidden="true" tabindex="-1"></a><span class="in"> hu_vacancy_rate = vacant_housing_units / tot_housing_units,</span></span> 7392<span id="cb21-612"><a href="#cb21-612" aria-hidden="true" tabindex="-1"></a><span class="in"> avg_fam_income = agg_fam_income / tot_families,</span></span> 7393<span id="cb21-613"><a href="#cb21-613" aria-hidden="true" tabindex="-1"></a><span class="in"> avg_rent = agg_gross_rent / renter_occ_housing_units,</span></span> 7394<span id="cb21-614"><a href="#cb21-614" aria-hidden="true" tabindex="-1"></a><span class="in"> across(-geometry, ~ ifelse((is.na(.)), 0, .)),</span></span> 7395<span id="cb21-615"><a href="#cb21-615" aria-hidden="true" tabindex="-1"></a><span class="in"> is_distressed = ifelse(</span></span> 7396<span id="cb21-616"><a href="#cb21-616" aria-hidden="true" tabindex="-1"></a><span class="in"> (((avg_fam_income / median_income_us) <= 0.67) & ((pct_below_poverty / pct_below_poverty_us) >= 1.50)),</span></span> 7397<span id="cb21-617"><a href="#cb21-617" aria-hidden="true" tabindex="-1"></a><span class="in"> 1, 0</span></span> 7398<span id="cb21-618"><a href="#cb21-618" aria-hidden="true" tabindex="-1"></a><span class="in"> )</span></span> 7399<span id="cb21-619"><a href="#cb21-619" aria-hidden="true" tabindex="-1"></a><span class="in"> ) %>%</span></span> 7400<span id="cb21-620"><a href="#cb21-620" aria-hidden="true" tabindex="-1"></a><span class="in"> select(-ends_with('_us')) %>%</span></span> 7401<span id="cb21-621"><a href="#cb21-621" aria-hidden="true" tabindex="-1"></a><span class="in"> relocate(pop_over_15_divorced, .after = females_divorced) %>%</span></span> 7402<span id="cb21-622"><a href="#cb21-622" aria-hidden="true" tabindex="-1"></a><span class="in"> relocate(pop_over_16_didnt_work, .after = females_didnt_work) %>%</span></span> 7403<span id="cb21-623"><a href="#cb21-623" aria-hidden="true" tabindex="-1"></a><span class="in"> relocate(c(county, year), .after = GEOID) %>%</span></span> 7404<span id="cb21-624"><a href="#cb21-624" aria-hidden="true" tabindex="-1"></a><span class="in"> relocate(geometry, .after = last_col())</span></span> 7405<span id="cb21-625"><a href="#cb21-625" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span> 7406<span id="cb21-626"><a href="#cb21-626" aria-hidden="true" tabindex="-1"></a></span> 7407<span id="cb21-627"><a href="#cb21-627" aria-hidden="true" tabindex="-1"></a><span class="in">```{=latex}</span></span> 7408<span id="cb21-628"><a href="#cb21-628" aria-hidden="true" tabindex="-1"></a><span class="in">\newpage</span></span> 7409<span id="cb21-629"><a href="#cb21-629" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span> 7410<span id="cb21-630"><a href="#cb21-630" aria-hidden="true" tabindex="-1"></a></span> 7411<span id="cb21-631"><a href="#cb21-631" aria-hidden="true" tabindex="-1"></a><span class="fu">### Land Cover Data</span></span> 7412<span id="cb21-632"><a href="#cb21-632" aria-hidden="true" tabindex="-1"></a></span> 7413<span id="cb21-633"><a href="#cb21-633" aria-hidden="true" tabindex="-1"></a>This section pertains to downloading and processing national land cover rasters over the entire 14-county study area. The interpretation of the land cover images' pixel values and corresponding land cover classes is from the <span class="co">[</span><span class="ot">Multi-Resolution Land Characteristics Consortium</span><span class="co">](https://www.mrlc.gov/data/type/land-cover)</span>.</span> 7414<span id="cb21-634"><a href="#cb21-634" aria-hidden="true" tabindex="-1"></a></span> 7415<span id="cb21-635"><a href="#cb21-635" aria-hidden="true" tabindex="-1"></a><span class="fu">#### Land Cover Processing</span></span> 7416<span id="cb21-636"><a href="#cb21-636" aria-hidden="true" tabindex="-1"></a></span> 7417<span id="cb21-637"><a href="#cb21-637" aria-hidden="true" tabindex="-1"></a><span class="in">```{r land_cover_setup, message=F, warning=F, cache=T}</span></span> 7418<span id="cb21-638"><a href="#cb21-638" aria-hidden="true" tabindex="-1"></a><span class="in"># dissolve counties into single study area polygon</span></span> 7419<span id="cb21-639"><a href="#cb21-639" aria-hidden="true" tabindex="-1"></a><span class="in">st_full_study_area <- st_bgs_final %>% filter(year == 2023) %>% st_union()</span></span> 7420<span id="cb21-640"><a href="#cb21-640" aria-hidden="true" tabindex="-1"></a></span> 7421<span id="cb21-641"><a href="#cb21-641" aria-hidden="true" tabindex="-1"></a><span class="in"># set years of land cover data</span></span> 7422<span id="cb21-642"><a href="#cb21-642" aria-hidden="true" tabindex="-1"></a><span class="in">lc_yrs <- 2008:2023</span></span> 7423<span id="cb21-643"><a href="#cb21-643" aria-hidden="true" tabindex="-1"></a></span> 7424<span id="cb21-644"><a href="#cb21-644" aria-hidden="true" tabindex="-1"></a><span class="in"># create list of tiff urls for each year</span></span> 7425<span id="cb21-645"><a href="#cb21-645" aria-hidden="true" tabindex="-1"></a><span class="in">nlcd_urls <- paste0('https://www.mrlc.gov/downloads/sciweb1/shared/mrlc/data-bundles/Annual_NLCD_LndCov_',</span></span> 7426<span id="cb21-646"><a href="#cb21-646" aria-hidden="true" tabindex="-1"></a><span class="in"> lc_yrs,</span></span> 7427<span id="cb21-647"><a href="#cb21-647" aria-hidden="true" tabindex="-1"></a><span class="in"> '_CU_C1V0.tif')</span></span> 7428<span id="cb21-648"><a href="#cb21-648" aria-hidden="true" tabindex="-1"></a></span> 7429<span id="cb21-649"><a href="#cb21-649" aria-hidden="true" tabindex="-1"></a><span class="in">names(nlcd_urls) <- lc_yrs</span></span> 7430<span id="cb21-650"><a href="#cb21-650" aria-hidden="true" tabindex="-1"></a></span> 7431<span id="cb21-651"><a href="#cb21-651" aria-hidden="true" tabindex="-1"></a><span class="in"># # download all land cover rasters</span></span> 7432<span id="cb21-652"><a href="#cb21-652" aria-hidden="true" tabindex="-1"></a><span class="in"># # RUN THE FIRST TIME, THEN RE-COMMENT OUT AFTER</span></span> 7433<span id="cb21-653"><a href="#cb21-653" aria-hidden="true" tabindex="-1"></a><span class="in"># # THIS STEP CAN TAKE AROUND AN HOUR</span></span> 7434<span id="cb21-654"><a href="#cb21-654" aria-hidden="true" tabindex="-1"></a><span class="in"># for (year in names(nlcd_urls)) {</span></span> 7435<span id="cb21-655"><a href="#cb21-655" aria-hidden="true" tabindex="-1"></a><span class="in"># cat('Downloading NLCD raster for year', paste(year, '...', sep = ''))</span></span> 7436<span id="cb21-656"><a href="#cb21-656" aria-hidden="true" tabindex="-1"></a><span class="in"># </span></span> 7437<span id="cb21-657"><a href="#cb21-657" aria-hidden="true" tabindex="-1"></a><span class="in"># # set path of downloaded raster</span></span> 7438<span id="cb21-658"><a href="#cb21-658" aria-hidden="true" tabindex="-1"></a><span class="in"># download_path = paste(mrlc_data_dir, paste('nlcd_', year, '.tif', sep=''), sep = '')</span></span> 7439<span id="cb21-659"><a href="#cb21-659" aria-hidden="true" tabindex="-1"></a><span class="in"># </span></span> 7440<span id="cb21-660"><a href="#cb21-660" aria-hidden="true" tabindex="-1"></a><span class="in"># # download raster</span></span> 7441<span id="cb21-661"><a href="#cb21-661" aria-hidden="true" tabindex="-1"></a><span class="in"># download(nlcd_urls[[year]], download_path, mode = 'wb')</span></span> 7442<span id="cb21-662"><a href="#cb21-662" aria-hidden="true" tabindex="-1"></a><span class="in"># }</span></span> 7443<span id="cb21-663"><a href="#cb21-663" aria-hidden="true" tabindex="-1"></a></span> 7444<span id="cb21-664"><a href="#cb21-664" aria-hidden="true" tabindex="-1"></a><span class="in"># process rasters and wrap them so they can be used in main session</span></span> 7445<span id="cb21-665"><a href="#cb21-665" aria-hidden="true" tabindex="-1"></a><span class="in">nlcd_wrapped <- lapply(lc_yrs, function(yr) {</span></span> 7446<span id="cb21-666"><a href="#cb21-666" aria-hidden="true" tabindex="-1"></a><span class="in"> # crop and mask each raster, then project it to NAD83</span></span> 7447<span id="cb21-667"><a href="#cb21-667" aria-hidden="true" tabindex="-1"></a><span class="in"> </span></span> 7448<span id="cb21-668"><a href="#cb21-668" aria-hidden="true" tabindex="-1"></a><span class="in"> #print(paste('Processing NLCD for', as.character(yr)))</span></span> 7449<span id="cb21-669"><a href="#cb21-669" aria-hidden="true" tabindex="-1"></a><span class="in"> </span></span> 7450<span id="cb21-670"><a href="#cb21-670" aria-hidden="true" tabindex="-1"></a><span class="in"> # get path to file</span></span> 7451<span id="cb21-671"><a href="#cb21-671" aria-hidden="true" tabindex="-1"></a><span class="in">
7451 # YOU MAY HAVE TO ADD .tif AS THE EXTENSION OF THE RASTER FILE</span></span> 7452<span id="cb21-672"><a href="#cb21-672" aria-hidden="true" tabindex="-1"></a><span class="in"> lc_rast <- paste(mrlc_data_dir, paste('nlcd_', as.character(yr), sep = ''), sep = '')</span></span> 7453<span id="cb21-673"><a href="#cb21-673" aria-hidden="true" tabindex="-1"></a><span class="in"> </span></span> 7454<span id="cb21-674"><a href="#cb21-674" aria-hidden="true" tabindex="-1"></a><span class="in"> # catch errors in processing the raster</span></span> 7455<span id="cb21-675"><a href="#cb21-675" aria-hidden="true" tabindex="-1"></a><span class="in"> tryCatch({</span></span> 7456<span id="cb21-676"><a href="#cb21-676" aria-hidden="true" tabindex="-1"></a><span class="in"> # load raster, then crop and mask to southern tier study area</span></span> 7457<span id="cb21-677"><a href="#cb21-677" aria-hidden="true" tabindex="-1"></a><span class="in"> r <- rast(lc_rast)</span></span> 7458<span id="cb21-678"><a href="#cb21-678" aria-hidden="true" tabindex="-1"></a><span class="in"> r_crop <- </span></span> 7459<span id="cb21-679"><a href="#cb21-679" aria-hidden="true" tabindex="-1"></a><span class="in"> terra::crop(</span></span> 7460<span id="cb21-680"><a href="#cb21-680" aria-hidden="true" tabindex="-1"></a><span class="in"> r, </span></span> 7461<span id="cb21-681"><a href="#cb21-681" aria-hidden="true" tabindex="-1"></a><span class="in"> vect(st_transform(st_full_study_area, crs = st_crs(r))),</span></span> 7462<span id="cb21-682"><a href="#cb21-682" aria-hidden="true" tabindex="-1"></a><span class="in"> progress = 0</span></span> 7463<span id="cb21-683"><a href="#cb21-683" aria-hidden="true" tabindex="-1"></a><span class="in"> )</span></span> 7464<span id="cb21-684"><a href="#cb21-684" aria-hidden="true" tabindex="-1"></a><span class="in"> r_mask <- terra::mask(</span></span> 7465<span id="cb21-685"><a href="#cb21-685" aria-hidden="true" tabindex="-1"></a><span class="in"> r_crop, </span></span> 7466<span id="cb21-686"><a href="#cb21-686" aria-hidden="true" tabindex="-1"></a><span class="in"> vect(st_transform(st_full_study_area, crs = st_crs(r))),</span></span> 7467<span id="cb21-687"><a href="#cb21-687" aria-hidden="true" tabindex="-1"></a><span class="in"> progress = 0</span></span> 7468<span id="cb21-688"><a href="#cb21-688" aria-hidden="true" tabindex="-1"></a><span class="in"> )</span></span> 7469<span id="cb21-689"><a href="#cb21-689" aria-hidden="true" tabindex="-1"></a><span class="in"> </span></span> 7470<span id="cb21-690"><a href="#cb21-690" aria-hidden="true" tabindex="-1"></a><span class="in"> # add year as name of masked raster</span></span> 7471<span id="cb21-691"><a href="#cb21-691" aria-hidden="true" tabindex="-1"></a><span class="in"> names(r_mask) <- paste('NLCD_', as.character(yr), sep = '')</span></span> 7472<span id="cb21-692"><a href="#cb21-692" aria-hidden="true" tabindex="-1"></a><span class="in"> </span></span> 7473<span id="cb21-693"><a href="#cb21-693" aria-hidden="true" tabindex="-1"></a><span class="in"> # return wrapped, masked raster</span></span> 7474<span id="cb21-694"><a href="#cb21-694" aria-hidden="true" tabindex="-1"></a><span class="in"> return(</span></span> 7475<span id="cb21-695"><a href="#cb21-695" aria-hidden="true" tabindex="-1"></a><span class="in"> wrap(</span></span> 7476<span id="cb21-696"><a href="#cb21-696" aria-hidden="true" tabindex="-1"></a><span class="in"> project(r_mask, crs(as_spatvector(st_bgs_final)), progress = 0)</span></span> 7477<span id="cb21-697"><a href="#cb21-697" aria-hidden="true" tabindex="-1"></a><span class="in"> )</span></span> 7478<span id="cb21-698"><a href="#cb21-698" aria-hidden="true" tabindex="-1"></a><span class="in"> )</span></span> 7479<span id="cb21-699"><a href="#cb21-699" aria-hidden="true" tabindex="-1"></a><span class="in"> }, error = function(e) {</span></span> 7480<span id="cb21-700"><a href="#cb21-700" aria-hidden="true" tabindex="-1"></a><span class="in"> message('Failed for year ', as.character(yr), ': ', e$message)</span></span> 7481<span id="cb21-701"><a href="#cb21-701" aria-hidden="true" tabindex="-1"></a><span class="in"> return(NULL)</span></span> 7482<span id="cb21-702"><a href="#cb21-702" aria-hidden="true" tabindex="-1"></a><span class="in"> })</span></span> 7483<span id="cb21-703"><a href="#cb21-703" aria-hidden="true" tabindex="-1"></a><span class="in">})</span></span> 7484<span id="cb21-704"><a href="#cb21-704" aria-hidden="true" tabindex="-1"></a></span> 7485<span id="cb21-705"><a href="#cb21-705" aria-hidden="true" tabindex="-1"></a><span class="in"># unwrap rasters so they can be used in the main session</span></span> 7486<span id="cb21-706"><a href="#cb21-706" aria-hidden="true" tabindex="-1"></a><span class="in">nlcd <- lapply(nlcd_wrapped, function(r) {</span></span> 7487<span id="cb21-707"><a href="#cb21-707" aria-hidden="true" tabindex="-1"></a><span class="in"> if (!is.null(r)) unwrap(r) else NULL</span></span> 7488<span id="cb21-708"><a href="#cb21-708" aria-hidden="true" tabindex="-1"></a><span class="in">})</span></span> 7489<span id="cb21-709"><a href="#cb21-709" aria-hidden="true" tabindex="-1"></a></span> 7490<span id="cb21-710"><a href="#cb21-710" aria-hidden="true" tabindex="-1"></a><span class="in">names(nlcd) <- lapply(lc_yrs, as.character)</span></span> 7491<span id="cb21-711"><a href="#cb21-711" aria-hidden="true" tabindex="-1"></a></span> 7492<span id="cb21-712"><a href="#cb21-712" aria-hidden="true" tabindex="-1"></a><span class="in"># stack rasters</span></span> 7493<span id="cb21-713"><a href="#cb21-713" aria-hidden="true" tabindex="-1"></a><span class="in">nlcd_stack <- round(rast(compact(nlcd)))</span></span> 7494<span id="cb21-714"><a href="#cb21-714" aria-hidden="true" tabindex="-1"></a></span> 7495<span id="cb21-715"><a href="#cb21-715" aria-hidden="true" tabindex="-1"></a><span class="in"># get unique pixel values</span></span> 7496<span id="cb21-716"><a href="#cb21-716" aria-hidden="true" tabindex="-1"></a><span class="in">all_classes <- sort(unique(values(nlcd_stack$`2023`)))</span></span> 7497<span id="cb21-717"><a href="#cb21-717" aria-hidden="true" tabindex="-1"></a></span> 7498<span id="cb21-718"><a href="#cb21-718" aria-hidden="true" tabindex="-1"></a><span class="in"># create reclassification matrix from data frame</span></span> 7499<span id="cb21-719"><a href="#cb21-719" aria-hidden="true" tabindex="-1"></a><span class="in">lc_type_reclass <- data.frame(</span></span> 7500<span id="cb21-720"><a href="#cb21-720" aria-hidden="true" tabindex="-1"></a><span class="in"> code = all_classes</span></span> 7501<span id="cb21-721"><a href="#cb21-721" aria-hidden="true" tabindex="-1"></a><span class="in">) %>%</span></span> 7502<span id="cb21-722"><a href="#cb21-722" aria-hidden="true" tabindex="-1"></a><span class="in"> mutate(</span></span> 7503<span id="cb21-723"><a href="#cb21-723" aria-hidden="true" tabindex="-1"></a><span class="in"> reclass = case_when(</span></span> 7504<span id="cb21-724"><a href="#cb21-724" aria-hidden="true" tabindex="-1"></a><span class="in"> code %in% 15:30 ~ 1, # Developed (Open Space, Low Intensity, Median Intensity, High Intensity) => 1</span></span> 7505<span id="cb21-725"><a href="#cb21-725" aria-hidden="true" tabindex="-1"></a><span class="in"> code %in% 41:43 ~ 2, # Forests (Deciduous, Evergreen, and Mixed) =>
7505 2</span></span> 7506<span id="cb21-726"><a href="#cb21-726" aria-hidden="true" tabindex="-1"></a><span class="in"> code %in% 44:59 ~ 3, # Shrubs and Scrubs => 3</span></span> 7507<span id="cb21-727"><a href="#cb21-727" aria-hidden="true" tabindex="-1"></a><span class="in"> code %in% 60:74 ~ 4, # Grassland => 4</span></span> 7508<span id="cb21-728"><a href="#cb21-728" aria-hidden="true" tabindex="-1"></a><span class="in"> code %in% 75:87 ~ 5, # Agriculture (Pasture and Cultivated Crops) => 5</span></span> 7509<span id="cb21-729"><a href="#cb21-729" aria-hidden="true" tabindex="-1"></a><span class="in"> code %in% 88:95 ~ 6, # Wetlands (Woody and Emergent Herbaceous) => 6</span></span> 7510<span id="cb21-730"><a href="#cb21-730" aria-hidden="true" tabindex="-1"></a><span class="in"> code == 12 ~ 7, # Perennial Ice and Snow => 7</span></span> 7511<span id="cb21-731"><a href="#cb21-731" aria-hidden="true" tabindex="-1"></a><span class="in"> code %in% 11:20 ~ 8, # Open Water => 8</span></span> 7512<span id="cb21-732"><a href="#cb21-732" aria-hidden="true" tabindex="-1"></a><span class="in"> code %in% 31:40 ~ 9, # Barren Land or Mining => 9</span></span> 7513<span id="cb21-733"><a href="#cb21-733" aria-hidden="true" tabindex="-1"></a><span class="in"> is.na(code) ~ 0 # Unknown</span></span> 7514<span id="cb21-734"><a href="#cb21-734" aria-hidden="true" tabindex="-1"></a><span class="in"> )</span></span> 7515<span id="cb21-735"><a href="#cb21-735" aria-hidden="true" tabindex="-1"></a><span class="in"> )</span></span> 7516<span id="cb21-736"><a href="#cb21-736" aria-hidden="true" tabindex="-1"></a></span> 7517<span id="cb21-737"><a href="#cb21-737" aria-hidden="true" tabindex="-1"></a><span class="in">lc_type_reclass <- as.matrix(lc_type_reclass)</span></span> 7518<span id="cb21-738"><a href="#cb21-738" aria-hidden="true" tabindex="-1"></a></span> 7519<span id="cb21-739"><a href="#cb21-739" aria-hidden="true" tabindex="-1"></a><span class="in"># create labels for reclassified categories</span></span> 7520<span id="cb21-740"><a href="#cb21-740" aria-hidden="true" tabindex="-1"></a><span class="in">reclassed_labels <- c(</span></span> 7521<span id="cb21-741"><a href="#cb21-741" aria-hidden="true" tabindex="-1"></a><span class="in"> '0' = 'No Change or Unknown',</span></span> 7522<span id="cb21-742"><a href="#cb21-742" aria-hidden="true" tabindex="-1"></a><span class="in"> '1' = 'Developed',</span></span> 7523<span id="cb21-743"><a href="#cb21-743" aria-hidden="true" tabindex="-1"></a><span class="in"> '2' = 'Forest',</span></span> 7524<span id="cb21-744"><a href="#cb21-744" aria-hidden="true" tabindex="-1"></a><span class="in"> '3' = 'Shrubland',</span></span> 7525<span id="cb21-745"><a href="#cb21-745" aria-hidden="true" tabindex="-1"></a><span class="in"> '4' = 'Grassland',</span></span> 7526<span id="cb21-746"><a href="#cb21-746" aria-hidden="true" tabindex="-1"></a><span class="in"> '5' = 'Agriculture',</span></span> 7527<span id="cb21-747"><a href="#cb21-747" aria-hidden="true" tabindex="-1"></a><span class="in"> '6' = 'Wetlands',</span></span> 7528<span id="cb21-748"><a href="#cb21-748" aria-hidden="true" tabindex="-1"></a><span class="in"> '7' = 'Ice & Snow',</span></span> 7529<span id="cb21-749"><a href="#cb21-749" aria-hidden="true" tabindex="-1"></a><span class="in"> '8' = 'Water',</span></span> 7530<span id="cb21-750"><a href="#cb21-750" aria-hidden="true" tabindex="-1"></a><span class="in"> '9' = 'Barren Land'</span></span> 7531<span id="cb21-751"><a href="#cb21-751" aria-hidden="true" tabindex="-1"></a><span class="in">)</span></span> 7532<span id="cb21-752"><a href="#cb21-752" aria-hidden="true" tabindex="-1"></a></span> 7533<span id="cb21-753"><a href="#cb21-753" aria-hidden="true" tabindex="-1"></a><span class="in"># apply reclassification across the whole stack</span></span> 7534<span id="cb21-754"><a href="#cb21-754" aria-hidden="true" tabindex="-1"></a><span class="in">nlcd_stack.reclass <- classify(nlcd_stack, rcl = lc_type_reclass, </span></span> 7535<span id="cb21-755"><a href="#cb21-755" aria-hidden="true" tabindex="-1"></a><span class="in"> others = NA, progress = 0)</span></span> 7536<span id="cb21-756"><a href="#cb21-756" aria-hidden="true" tabindex="-1"></a></span> 7537<span id="cb21-757"><a href="#cb21-757" aria-hidden="true" tabindex="-1"></a><span class="in">################################################</span></span> 7538<span id="cb21-758"><a href="#cb21-758" aria-hidden="true" tabindex="-1"></a><span class="in"># RUN THE FIRST TIME, THEN RE-COMMENT OUT AFTER</span></span> 7539<span id="cb21-759"><a href="#cb21-759" aria-hidden="true" tabindex="-1"></a><span class="in"># THIS STEP CAN TAKE AROUND AN HOUR</span></span> 7540<span id="cb21-760"><a href="#cb21-760" aria-hidden="true" tabindex="-1"></a><span class="in">###############################################</span></span> 7541<span id="cb21-761"><a href="#cb21-761" aria-hidden="true" tabindex="-1"></a></span> 7542<span id="cb21-762"><a href="#cb21-762" aria-hidden="true" tabindex="-1"></a><span class="in"># download reclassified rasters to speed up process</span></span> 7543<span id="cb21-763"><a href="#cb21-763" aria-hidden="true" tabindex="-1"></a><span class="in"># for (yr in names(nlcd_stack.reclass)) {</span></span> 7544<span id="cb21-764"><a href="#cb21-764" aria-hidden="true" tabindex="-1"></a><span class="in"># cat('Downloading reclassified raster for year', paste(yr, '...', sep = ''))</span></span> 7545<span id="cb21-765"><a href="#cb21-765" aria-hidden="true" tabindex="-1"></a><span class="in"># </span></span> 7546<span id="cb21-766"><a href="#cb21-766" aria-hidden="true" tabindex="-1"></a><span class="in"># # set path of downloaded raster</span></span> 7547<span id="cb21-767"><a href="#cb21-767" aria-hidden="true" tabindex="-1"></a><span class="in"># download_path = paste(mrlc_data_dir, paste('nlcd_', yr, '_reclass', '.tif', sep=''), sep = '')</span></span> 7548<span id="cb21-768"><a href="#cb21-768" aria-hidden="true" tabindex="-1"></a><span class="in"># </span></span> 7549<span id="cb21-769"><a href="#cb21-769" aria-hidden="true" tabindex="-1"></a><span class="in"># # download raster</span></span> 7550<span id="cb21-770"><a href="#cb21-770" aria-hidden="true" tabindex="-1"></a><span class="in">
7550# writeRaster(nlcd_stack.reclass[[yr]], download_path, overwrite = TRUE)</span></span> 7551<span id="cb21-771"><a href="#cb21-771" aria-hidden="true" tabindex="-1"></a><span class="in"># }</span></span> 7552<span id="cb21-772"><a href="#cb21-772" aria-hidden="true" tabindex="-1"></a></span> 7553<span id="cb21-773"><a href="#cb21-773" aria-hidden="true" tabindex="-1"></a><span class="in"># convert sq meters to sq miles</span></span> 7554<span id="cb21-774"><a href="#cb21-774" aria-hidden="true" tabindex="-1"></a><span class="in">pixel_area_sqmi <- (29.49398 * 29.49398) * 3.861e-7</span></span> 7555<span id="cb21-775"><a href="#cb21-775" aria-hidden="true" tabindex="-1"></a></span> 7556<span id="cb21-776"><a href="#cb21-776" aria-hidden="true" tabindex="-1"></a><span class="in"># prepare 3 background R sessions to process in parallel</span></span> 7557<span id="cb21-777"><a href="#cb21-777" aria-hidden="true" tabindex="-1"></a><span class="in">plan(multisession, workers = 2)</span></span> 7558<span id="cb21-778"><a href="#cb21-778" aria-hidden="true" tabindex="-1"></a></span> 7559<span id="cb21-779"><a href="#cb21-779" aria-hidden="true" tabindex="-1"></a><span class="in"># calculate total area of each land cover category </span></span> 7560<span id="cb21-780"><a href="#cb21-780" aria-hidden="true" tabindex="-1"></a><span class="in"># in square miles over the entire study area for each year (2008-2023)</span></span> 7561<span id="cb21-781"><a href="#cb21-781" aria-hidden="true" tabindex="-1"></a><span class="in"># have to also include 2008 due to </span></span> 7562<span id="cb21-782"><a href="#cb21-782" aria-hidden="true" tabindex="-1"></a><span class="in">zonal_stats <- future_lapply(lc_yrs, function(yr) {</span></span> 7563<span id="cb21-783"><a href="#cb21-783" aria-hidden="true" tabindex="-1"></a><span class="in"> #print(paste('Calculating zonal statistics for', as.character(yr)))</span></span> 7564<span id="cb21-784"><a href="#cb21-784" aria-hidden="true" tabindex="-1"></a><span class="in"> </span></span> 7565<span id="cb21-785"><a href="#cb21-785" aria-hidden="true" tabindex="-1"></a><span class="in"> # get tract boundaries, then project</span></span> 7566<span id="cb21-786"><a href="#cb21-786" aria-hidden="true" tabindex="-1"></a><span class="in"> bgs_yr <- if (yr == 2008) {</span></span> 7567<span id="cb21-787"><a href="#cb21-787" aria-hidden="true" tabindex="-1"></a><span class="in"> st_bgs_final %>% filter(year == 2009)</span></span> 7568<span id="cb21-788"><a href="#cb21-788" aria-hidden="true" tabindex="-1"></a><span class="in"> } else {</span></span> 7569<span id="cb21-789"><a href="#cb21-789" aria-hidden="true" tabindex="-1"></a><span class="in"> st_bgs_final %>% filter(year == yr)</span></span> 7570<span id="cb21-790"><a href="#cb21-790" aria-hidden="true" tabindex="-1"></a><span class="in"> }</span></span> 7571<span id="cb21-791"><a href="#cb21-791" aria-hidden="true" tabindex="-1"></a><span class="in"> </span></span> 7572<span id="cb21-792"><a href="#cb21-792" aria-hidden="true" tabindex="-1"></a><span class="in"> bgs_yr <- st_transform(bgs_yr, 5070)</span></span> 7573<span id="cb21-793"><a href="#cb21-793" aria-hidden="true" tabindex="-1"></a><span class="in"> </span></span> 7574<span id="cb21-794"><a href="#cb21-794" aria-hidden="true" tabindex="-1"></a><span class="in"> # load reclassified nlcd raster for year and project</span></span> 7575<span id="cb21-795"><a href="#cb21-795" aria-hidden="true" tabindex="-1"></a><span class="in"> lc <- project(</span></span> 7576<span id="cb21-796"><a href="#cb21-796" aria-hidden="true" tabindex="-1"></a><span class="in"> rast(paste(mrlc_data_dir, '/nlcd_', as.character(yr), '_reclass.tif', sep = '')),</span></span> 7577<span id="cb21-797"><a href="#cb21-797" aria-hidden="true" tabindex="-1"></a><span class="in"> 'EPSG:5070',</span></span> 7578<span id="cb21-798"><a href="#cb21-798" aria-hidden="true" tabindex="-1"></a><span class="in"> progress = 0</span></span> 7579<span id="cb21-799"><a href="#cb21-799" aria-hidden="true" tabindex="-1"></a><span class="in"> )</span></span> 7580<span id="cb21-800"><a href="#cb21-800" aria-hidden="true" tabindex="-1"></a><span class="in"> </span></span> 7581<span id="cb21-801"><a href="#cb21-801" aria-hidden="true" tabindex="-1"></a><span class="in"> # extract counts of each class per tract</span></span> 7582<span id="cb21-802"><a href="#cb21-802" aria-hidden="true" tabindex="-1"></a><span class="in"> z <- terra::extract(</span></span> 7583<span id="cb21-803"><a href="#cb21-803" aria-hidden="true" tabindex="-1"></a><span class="in"> lc, vect(bgs_yr),</span></span> 7584<span id="cb21-804"><a href="#cb21-804" aria-hidden="true" tabindex="-1"></a><span class="in"> fun = function(x, ...) table(factor(x, levels = 1:9)),</span></span> 7585<span id="cb21-805"><a href="#cb21-805" aria-hidden="true" tabindex="-1"></a><span class="in"> progress = 0</span></span> 7586<span id="cb21-806"><a href="#cb21-806" aria-hidden="true" tabindex="-1"></a><span class="in"> ) %>% </span></span> 7587<span id="cb21-807"><a href="#cb21-807" aria-hidden="true" tabindex="-1"></a><span class="in"> st_drop_geometry() %>%</span></span> 7588<span id="cb21-808"><a href="#cb21-808" aria-hidden="true" tabindex="-1"></a><span class="in"> as.data.frame()</span></span> 7589<span id="cb21-809"><a href="#cb21-809" aria-hidden="true" tabindex="-1"></a><span class="in"> </span></span> 7590<span id="cb21-810"><a href="#cb21-810" aria-hidden="true" tabindex="-1"></a><span class="in"> # replace index IDs with actual GEOIDs</span></span> 7591<span id="cb21-811"><a href="#cb21-811" aria-hidden="true" tabindex="-1"></a><span class="in"> z$GEOID <- as.character(bgs_yr$GEOID)</span></span> 7592<span id="cb21-812"><a href="#cb21-812" aria-hidden="true" tabindex="-1"></a><span class="in"> </span></span> 7593<span id="cb21-813"><a href="#cb21-813" aria-hidden="true" tabindex="-1"></a><span class="in"> # remove the index column</span></span> 7594<span id="cb21-814"><a href="#cb21-814" aria-hidden="true" tabindex="-1"></a><span class="in"> z <- z %>% select(-ID) %>% relocate(1:9, .after = GEOID)</span></span> 7595<span id="cb21-815"><a href="#cb21-815" aria-hidden="true" tabindex="-1"></a><span class="in"> </span></span> 7596<span id="cb21-816"><a href="#cb21-816" aria-hidden="true" tabindex="-1"></a><span class="in"> # rename columns</span></span> 7597<span id="cb21-817"><a href="#cb21-817" aria-hidden="true" tabindex="-1"></a><span class="in"> colnames(z) <- c('GEOID', as.character(1:9))</span></span> 7598<span id="cb21-818"><a href="#cb21-818" aria-hidden="true" tabindex="-1"></a><span class="in"> </span></span> 7599<span id="cb21-819"><a href="#cb21-819" aria-hidden="true" tabindex="-1"></a><span class="in"> # elongate data, then return it</span></span> 7600<span id="cb21-820"><a href="#cb21-820" aria-hidden="true" tabindex="-1"></a><span class="in"> z_long <- </span></span> 7601<span id="cb21-821"><a href="#cb21-821" aria-hidden="true" tabindex="-1"></a><span class="in"> z %>%</span></span> 7602<span id="cb21-822"><a href="#cb21-822" aria-hidden="true" tabindex="-1"></a><span class="in"> mutate(across(all_of(as.character(1:9)), as.numeric)) %>%</span></span> 7603<span id="cb21-823"><a href="#cb21-823" aria-hidden="true" tabindex="-1"></a><span class="in"> pivot_longer(</span></span> 7604<span id="cb21-824"><a href="#cb21-824" aria-hidden="true" tabindex="-1"></a><span class="in"> cols = all_of(as.character(1:9)),</span></span> 7605<span id="cb21-825"><a href="#cb21-825" aria-hidden="true" tabindex="-1"></a><span class="in"> names_to = 'land_cover_class',</span></span> 7606<span id="cb21-826"><a href="#cb21-826" aria-hidden="true" tabindex="-1"></a><span class="in"> values_to = 'pixel_count'</span></span> 7607<span id="cb21-827"><a href="#cb21-827" aria-hidden="true" tabindex="-1"></a><span class="in"> ) %>%</span></span> 7608<span id="cb21-828"><a href="#cb21-828" aria-hidden="true" tabindex="-1"></a><span class="in"> mutate(</span></span> 7609<span id="cb21-829"><a href="#cb21-829" aria-hidden="true" tabindex="-1"></a><span class="in">
7609 year = yr,</span></span> 7610<span id="cb21-830"><a href="#cb21-830" aria-hidden="true" tabindex="-1"></a><span class="in"> land_cover_class = reclassed_labels[land_cover_class],</span></span> 7611<span id="cb21-831"><a href="#cb21-831" aria-hidden="true" tabindex="-1"></a><span class="in"> area_sqmi = pixel_count * pixel_area_sqmi</span></span> 7612<span id="cb21-832"><a href="#cb21-832" aria-hidden="true" tabindex="-1"></a><span class="in"> ) %>%</span></span> 7613<span id="cb21-833"><a href="#cb21-833" aria-hidden="true" tabindex="-1"></a><span class="in"> select(GEOID, year, land_cover_class, area_sqmi)</span></span> 7614<span id="cb21-834"><a href="#cb21-834" aria-hidden="true" tabindex="-1"></a><span class="in"> </span></span> 7615<span id="cb21-835"><a href="#cb21-835" aria-hidden="true" tabindex="-1"></a><span class="in"> return(z_long)</span></span> 7616<span id="cb21-836"><a href="#cb21-836" aria-hidden="true" tabindex="-1"></a><span class="in">}, future.seed = TRUE)</span></span> 7617<span id="cb21-837"><a href="#cb21-837" aria-hidden="true" tabindex="-1"></a></span> 7618<span id="cb21-838"><a href="#cb21-838" aria-hidden="true" tabindex="-1"></a><span class="in"># switch back to sequential processing</span></span> 7619<span id="cb21-839"><a href="#cb21-839" aria-hidden="true" tabindex="-1"></a><span class="in">plan(sequential)</span></span> 7620<span id="cb21-840"><a href="#cb21-840" aria-hidden="true" tabindex="-1"></a></span> 7621<span id="cb21-841"><a href="#cb21-841" aria-hidden="true" tabindex="-1"></a><span class="in"># combine zonal stats</span></span> 7622<span id="cb21-842"><a href="#cb21-842" aria-hidden="true" tabindex="-1"></a><span class="in">zonal_stats_full <- bind_rows(zonal_stats)</span></span> 7623<span id="cb21-843"><a href="#cb21-843" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span> 7624<span id="cb21-844"><a href="#cb21-844" aria-hidden="true" tabindex="-1"></a></span> 7625<span id="cb21-845"><a href="#cb21-845" aria-hidden="true" tabindex="-1"></a><span class="in">```{=latex}</span></span> 7626<span id="cb21-846"><a href="#cb21-846" aria-hidden="true" tabindex="-1"></a><span class="in">\newpage</span></span> 7627<span id="cb21-847"><a href="#cb21-847" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span> 7628<span id="cb21-848"><a href="#cb21-848" aria-hidden="true" tabindex="-1"></a></span> 7629<span id="cb21-849"><a href="#cb21-849" aria-hidden="true" tabindex="-1"></a><span class="fu">#### Land Cover Calculations at Block Group Level</span></span> 7630<span id="cb21-850"><a href="#cb21-850" aria-hidden="true" tabindex="-1"></a></span> 7631<span id="cb21-851"><a href="#cb21-851" aria-hidden="true" tabindex="-1"></a>Calculate the total coverage of each land cover class within each block group in each of the 15 years.</span> 7632<span id="cb21-852"><a href="#cb21-852" aria-hidden="true" tabindex="-1"></a></span> 7633<span id="cb21-853"><a href="#cb21-853" aria-hidden="true" tabindex="-1"></a><span class="in">```{r land_cover_calculations, message=F, warning=F, cache=T}</span></span> 7634<span id="cb21-854"><a href="#cb21-854" aria-hidden="true" tabindex="-1"></a><span class="in"># calculate total areas of each class for each GEOID in each year,</span></span> 7635<span id="cb21-855"><a href="#cb21-855" aria-hidden="true" tabindex="-1"></a><span class="in"># as well as the percent change from the previous year</span></span> 7636<span id="cb21-856"><a href="#cb21-856" aria-hidden="true" tabindex="-1"></a><span class="in"># fill all null values</span></span> 7637<span id="cb21-857"><a href="#cb21-857" aria-hidden="true" tabindex="-1"></a><span class="in"># pivot to wide format</span></span> 7638<span id="cb21-858"><a href="#cb21-858" aria-hidden="true" tabindex="-1"></a><span class="in">zonal_changes <-</span></span> 7639<span id="cb21-859"><a href="#cb21-859" aria-hidden="true" tabindex="-1"></a><span class="in"> zonal_stats_full %>%</span></span> 7640<span id="cb21-860"><a href="#cb21-860" aria-hidden="true" tabindex="-1"></a><span class="in"> arrange(GEOID, land_cover_class, year) %>%</span></span> 7641<span id="cb21-861"><a href="#cb21-861" aria-hidden="true" tabindex="-1"></a><span class="in"> group_by(GEOID, land_cover_class) %>%</span></span> 7642<span id="cb21-862"><a href="#cb21-862" aria-hidden="true" tabindex="-1"></a><span class="in"> mutate(</span></span> 7643<span id="cb21-863"><a href="#cb21-863" aria-hidden="true" tabindex="-1"></a><span class="in"> prev_area_sqmi = lag(area_sqmi),</span></span> 7644<span id="cb21-864"><a href="#cb21-864" aria-hidden="true" tabindex="-1"></a><span class="in"> pct_change = ifelse(</span></span> 7645<span id="cb21-865"><a href="#cb21-865" aria-hidden="true" tabindex="-1"></a><span class="in"> !is.na(prev_area_sqmi) & prev_area_sqmi > 0,</span></span> 7646<span id="cb21-866"><a href="#cb21-866" aria-hidden="true" tabindex="-1"></a><span class="in"> ((area_sqmi - prev_area_sqmi) / prev_area_sqmi),</span></span> 7647<span id="cb21-867"><a href="#cb21-867" aria-hidden="true" tabindex="-1"></a><span class="in"> 0</span></span> 7648<span id="cb21-868"><a href="#cb21-868" aria-hidden="true" tabindex="-1"></a><span class="in"> )</span></span> 7649<span id="cb21-869"><a href="#cb21-869" aria-hidden="true" tabindex="-1"></a><span class="in"> ) %>%</span></span> 7650<span id="cb21-870"><a href="#cb21-870" aria-hidden="true" tabindex="-1"></a><span class="in"> ungroup() %>%</span></span> 7651<span id="cb21-871"><a href="#cb21-871" aria-hidden="true" tabindex="-1"></a><span class="in"> pivot_wider(id_cols = c(GEOID, year),</span></span> 7652<span id="cb21-872"><a href="#cb21-872" aria-hidden="true" tabindex="-1"></a><span class="in"> names_from=land_cover_class,</span></span> 7653<span id="cb21-873"><a href="#cb21-873" aria-hidden="true" tabindex="-1"></a><span class="in"> values_from = c(area_sqmi, pct_change),</span></span> 7654<span id="cb21-874"><a href="#cb21-874" aria-hidden="true" tabindex="-1"></a><span class="in"> names_glue = '{land_cover_class}_{.value}') %>%</span></span> 7655<span id="cb21-875"><a href="#cb21-875" aria-hidden="true" tabindex="-1"></a><span class="in"> mutate(across(everything(), ~ ifelse(is.na(.), 0, .)),</span></span> 7656<span id="cb21-876"><a href="#cb21-876" aria-hidden="true" tabindex="-1"></a><span class="in"> GEOID = as.numeric(GEOID))</span></span> 7657<span id="cb21-877"><a href="#cb21-877" aria-hidden="true" tabindex="-1"></a></span> 7658<span id="cb21-878"><a href="#cb21-878" aria-hidden="true" tabindex="-1"></a><span class="in"># join zonal changes to final st_bgs_final dataframe</span></span> 7659<span id="cb21-879"><a href="#cb21-879" aria-hidden="true" tabindex="-1"></a><span class="in">st_bgs_all <-</span></span> 7660<span id="cb21-880"><a href="#cb21-880" aria-hidden="true" tabindex="-1"></a><span class="in"> st_bgs_final %>%</span></span> 7661<span id="cb21-881"><a href="#cb21-881" aria-hidden="true" tabindex="-1"></a><span class="in"> st_transform(crs= 'EPSG:26918') %>% </span></span> 7662<span id="cb21-882"><a href="#cb21-882" aria-hidden="true" tabindex="-1"></a><span class="in"> left_join(zonal_changes %>% filter(year != 2008), </span></span> 7663<span id="cb21-883"><a href="#cb21-883" aria-hidden="true" tabindex="-1"></a><span class="in"> by = c('GEOID', 'year')) %>%</span></span> 7664<span id="cb21-884"><a href="#cb21-884" aria-hidden="true" tabindex="-1"></a><span class="in"> mutate(</span></span> 7665<span id="cb21-885"><a href="#cb21-885" aria-hidden="true" tabindex="-1"></a><span class="in"> pct_agriculture = Agriculture_area_sqmi / area_sqmi,</span></span> 7666<span id="cb21-886"><a href="#cb21-886" aria-hidden="true" tabindex="-1"></a><span class="in"> pct_barren = `Barren Land_area_sqmi` / area_sqmi,</span></span> 7667<span id="cb21-887"><a href="#cb21-887" aria-hidden="true" tabindex="-1"></a><span class="in"> pct_developed = Developed_area_sqmi / area_sqmi,</span></span> 7668<span id="cb21-888"><a href="#cb21-888" aria-hidden="true" tabindex="-1"></a><span class="in"> pct_forest = Forest_area_sqmi / area_sqmi,</span></span> 7669<span id="cb21-889"><a href="#cb21-889" aria-hidden="true" tabindex="-1"></a><span class="in"> pct_grassland = Grassland_area_sqmi,</span></span> 7670<span id="cb21-890"><a href="#cb21-890" aria-hidden="true" tabindex="-1"></a><span class="in"> pct_ice_snow = `Ice & Snow_area_sqmi` / area_sqmi,</span></span> 7671<span id="cb21-891"><a href="#cb21-891" aria-hidden="true" tabindex="-1"></a><span class="in"> pct_shrubland = Shrubland_area_sqmi / area_sqmi,</span></span> 7672<span id="cb21-892"><a href="#cb21-892" aria-hidden="true" tabindex="-1"></a><span class="in"> pct_water = Water_area_sqmi / area_sqmi,</span></span> 7673<span id="cb21-893"><a href="#cb21-893" aria-hidden="true" tabindex="-1"></a><span class="in"> pct_wetlands = Wetlands_area_sqmi / area_sqmi</span></span> 7674<span id="cb21-894"><a href="#cb21-894" aria-hidden="true" tabindex="-1"></a><span class="in"> ) %>%</span></span> 7675<span id="cb21-895"><a href="#cb21-895" aria-hidden="true" tabindex="-1"></a><span class="in"> relocate(geometry, .after = last_col())</span></span> 7676<span id="cb21-896"><a href="#cb21-896" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span> 7677<span id="cb21-897"><a href="#cb21-897" aria-hidden="true" tabindex="-1"></a></span> 7678<span id="cb21-898"><a href="#cb21-898" aria-hidden="true" tabindex="-1"></a><span class="fu"># Results</span></span> 7679<span id="cb21-899"><a href="#cb21-899" aria-hidden="true" tabindex="-1"></a></span> 7680<span id="cb21-900"><a href="#cb21-900" aria-hidden="true" tabindex="-1"></a><span class="fu">## Logit Mixed-Effects Model Using glmmTMB</span></span> 7681<span id="cb21-901"><a href="#cb21-901" aria-hidden="true" tabindex="-1"></a></span> 7682<span id="cb21-902"><a href="#cb21-902" aria-hidden="true" tabindex="-1"></a>There are 15,060 observations in the dataset, which represent the 1,004 uniform block groups across a 15-year period. The model predicts the distressed status of a block group given the scaled predictors (population density, unemployment rate, spatially lagged unemployment rate, average rent, divorced rate, and percent developed), while incorporating a categorical time period variable (year_group) and taking into account the fact that some block groups may be inherently more or less at risk than average through the random intercepts for each individual block group (1 | GEOID). The model was trained using a random sample of 80% of the unique 1,004 block group GEOIDs in the study, which amounts to 803 block groups and 12,045 training observations.</span> 7683<span id="cb21-903"><a href="#cb21-903" aria-hidden="true" tabindex="-1"></a></span> 7684<span id="cb21-904"><a href="#cb21-904" aria-hidden="true" tabindex="-1"></a><span class="in">```{r glmmtmb_regression, message=F, warning=F}</span></span> 7685<span id="cb21-905"><a href="#cb21-905" aria-hidden="true" tabindex="-1"></a><span class="in">
7685#| cache: false</span></span> 7686<span id="cb21-906"><a href="#cb21-906" aria-hidden="true" tabindex="-1"></a></span> 7687<span id="cb21-907"><a href="#cb21-907" aria-hidden="true" tabindex="-1"></a><span class="in">set.seed(1234)</span></span> 7688<span id="cb21-908"><a href="#cb21-908" aria-hidden="true" tabindex="-1"></a></span> 7689<span id="cb21-909"><a href="#cb21-909" aria-hidden="true" tabindex="-1"></a><span class="in"># scale quantitative independent variables, then place each observation in a year group,</span></span> 7690<span id="cb21-910"><a href="#cb21-910" aria-hidden="true" tabindex="-1"></a><span class="in"># create if_distressed value</span></span> 7691<span id="cb21-911"><a href="#cb21-911" aria-hidden="true" tabindex="-1"></a><span class="in">st_bgs.scaled <-</span></span> 7692<span id="cb21-912"><a href="#cb21-912" aria-hidden="true" tabindex="-1"></a><span class="in"> st_bgs_all %>%</span></span> 7693<span id="cb21-913"><a href="#cb21-913" aria-hidden="true" tabindex="-1"></a><span class="in"> rename(</span></span> 7694<span id="cb21-914"><a href="#cb21-914" aria-hidden="true" tabindex="-1"></a><span class="in"> Barren_Land_area_sqmi = `Barren Land_area_sqmi`,</span></span> 7695<span id="cb21-915"><a href="#cb21-915" aria-hidden="true" tabindex="-1"></a><span class="in"> Barren_Land_pct_change = `Barren Land_pct_change`</span></span> 7696<span id="cb21-916"><a href="#cb21-916" aria-hidden="true" tabindex="-1"></a><span class="in"> ) %>%</span></span> 7697<span id="cb21-917"><a href="#cb21-917" aria-hidden="true" tabindex="-1"></a><span class="in"> mutate(</span></span> 7698<span id="cb21-918"><a href="#cb21-918" aria-hidden="true" tabindex="-1"></a><span class="in"> tot_pop_density = scale(tot_pop_density)[,1],</span></span> 7699<span id="cb21-919"><a href="#cb21-919" aria-hidden="true" tabindex="-1"></a><span class="in"> per_capita_income = scale(per_capita_income)[,1],</span></span> 7700<span id="cb21-920"><a href="#cb21-920" aria-hidden="true" tabindex="-1"></a><span class="in"> pct_below_poverty = scale(pct_below_poverty)[,1],</span></span> 7701<span id="cb21-921"><a href="#cb21-921" aria-hidden="true" tabindex="-1"></a><span class="in"> pct_didnt_work_past_yr = scale(pct_didnt_work_past_yr)[,1],</span></span> 7702<span id="cb21-922"><a href="#cb21-922" aria-hidden="true" tabindex="-1"></a><span class="in"> pct_family_hhs = scale(pct_family_hhs)[,1],</span></span> 7703<span id="cb21-923"><a href="#cb21-923" aria-hidden="true" tabindex="-1"></a><span class="in"> pct_divorced = scale(pct_divorced)[,1],</span></span> 7704<span id="cb21-924"><a href="#cb21-924" aria-hidden="true" tabindex="-1"></a><span class="in"> avg_fam_income = scale(avg_fam_income)[,1],</span></span> 7705<span id="cb21-925"><a href="#cb21-925" aria-hidden="true" tabindex="-1"></a><span class="in"> pct_developed = scale(pct_developed)[,1],</span></span> 7706<span id="cb21-926"><a href="#cb21-926" aria-hidden="true" tabindex="-1"></a><span class="in"> year_group = case_when(</span></span> 7707<span id="cb21-927"><a href="#cb21-927" aria-hidden="true" tabindex="-1"></a><span class="in"> year <= 2013 ~ '2009-2013',</span></span> 7708<span id="cb21-928"><a href="#cb21-928" aria-hidden="true" tabindex="-1"></a><span class="in"> year <= 2018 ~ '2014-2018',</span></span> 7709<span id="cb21-929"><a href="#cb21-929" aria-hidden="true" tabindex="-1"></a><span class="in"> TRUE ~ '2019-2023'</span></span> 7710<span id="cb21-930"><a href="#cb21-930" aria-hidden="true" tabindex="-1"></a><span class="in"> ),</span></span> 7711<span id="cb21-931"><a href="#cb21-931" aria-hidden="true" tabindex="-1"></a><span class="in"> ) %>%</span></span> 7712<span id="cb21-932"><a href="#cb21-932" aria-hidden="true" tabindex="-1"></a><span class="in"> relocate(geometry, .after = last_col()) %>%</span></span> 7713<span id="cb21-933"><a href="#cb21-933" aria-hidden="true" tabindex="-1"></a><span class="in"> st_transform(crs = 5070)</span></span> 7714<span id="cb21-934"><a href="#cb21-934" aria-hidden="true" tabindex="-1"></a></span> 7715<span id="cb21-935"><a href="#cb21-935" aria-hidden="true" tabindex="-1"></a><span class="in">
7715###</span></span> 7716<span id="cb21-936"><a href="#cb21-936" aria-hidden="true" tabindex="-1"></a><span class="in"># define neighbors and weights to add spatial lag variables</span></span> 7717<span id="cb21-937"><a href="#cb21-937" aria-hidden="true" tabindex="-1"></a><span class="in">###</span></span> 7718<span id="cb21-938"><a href="#cb21-938" aria-hidden="true" tabindex="-1"></a></span> 7719<span id="cb21-939"><a href="#cb21-939" aria-hidden="true" tabindex="-1"></a><span class="in"># get centroids of unique block groups</span></span> 7720<span id="cb21-940"><a href="#cb21-940" aria-hidden="true" tabindex="-1"></a><span class="in">unique_bgs <- st_bgs.scaled %>% distinct(GEOID, .keep_all = TRUE)</span></span> 7721<span id="cb21-941"><a href="#cb21-941" aria-hidden="true" tabindex="-1"></a><span class="in">centroids <- st_centroid(unique_bgs)</span></span> 7722<span id="cb21-942"><a href="#cb21-942" aria-hidden="true" tabindex="-1"></a><span class="in">coords <- st_coordinates(centroids)[, 1:2]</span></span> 7723<span id="cb21-943"><a href="#cb21-943" aria-hidden="true" tabindex="-1"></a></span> 7724<span id="cb21-944"><a href="#cb21-944" aria-hidden="true" tabindex="-1"></a><span class="in"># define k-nearest neighbors</span></span> 7725<span id="cb21-945"><a href="#cb21-945" aria-hidden="true" tabindex="-1"></a><span class="in">k <- 6</span></span> 7726<span id="cb21-946"><a href="#cb21-946" aria-hidden="true" tabindex="-1"></a><span class="in">knn_neighbors <- knearneigh(coords, k = k)</span></span> 7727<span id="cb21-947"><a href="#cb21-947" aria-hidden="true" tabindex="-1"></a><span class="in">nb <- knn2nb(knn_neighbors)</span></span> 7728<span id="cb21-948"><a href="#cb21-948" aria-hidden="true" tabindex="-1"></a></span> 7729<span id="cb21-949"><a href="#cb21-949" aria-hidden="true" tabindex="-1"></a><span class="in"># create spatial weights matrix</span></span> 7730<span id="cb21-950"><a href="#cb21-950" aria-hidden="true" tabindex="-1"></a><span class="in">lw <- nb2listw(nb, style = 'W')</span></span> 7731<span id="cb21-951"><a href="#cb21-951" aria-hidden="true" tabindex="-1"></a></span> 7732<span id="cb21-952"><a href="#cb21-952" aria-hidden="true" tabindex="-1"></a><span class="in"># pct_didnt_work_past_yr as spatial</span></span> 7733<span id="cb21-953"><a href="#cb21-953" aria-hidden="true" tabindex="-1"></a><span class="in">lag_didnt_work <- lag.listw(</span></span> 7734<span id="cb21-954"><a href="#cb21-954" aria-hidden="true" tabindex="-1"></a><span class="in"> lw, st_bgs.scaled$pct_didnt_work_past_yr[!duplicated(st_bgs.scaled$GEOID)]</span></span> 7735<span id="cb21-955"><a href="#cb21-955" aria-hidden="true" tabindex="-1"></a><span class="in">)</span></span> 7736<span id="cb21-956"><a href="#cb21-956" aria-hidden="true" tabindex="-1"></a></span> 7737<span id="cb21-957"><a href="#cb21-957" aria-hidden="true" tabindex="-1"></a><span class="in"># append lags to each unique GEOID</span></span> 7738<span id="cb21-958"><a href="#cb21-958" aria-hidden="true" tabindex="-1"></a><span class="in">st_bgs.lagged <- </span></span> 7739<span id="cb21-959"><a href="#cb21-959" aria-hidden="true" tabindex="-1"></a><span class="in"> st_bgs.scaled %>%</span></span> 7740<span id="cb21-960"><a href="#cb21-960" aria-hidden="true" tabindex="-1"></a><span class="in"> distinct(GEOID, .keep_all = TRUE) %>%</span></span> 7741<span id="cb21-961"><a href="#cb21-961" aria-hidden="true" tabindex="-1"></a><span class="in"> mutate(lag_pct_didnt_work_past_yr = lag_didnt_work)</span></span> 7742<span id="cb21-962"><a href="#cb21-962" aria-hidden="true" tabindex="-1"></a></span> 7743<span id="cb21-963"><a href="#cb21-963" aria-hidden="true" tabindex="-1"></a><span class="in"># join back to full dataset</span></span> 7744<span id="cb21-964"><a href="#cb21-964" aria-hidden="true" tabindex="-1"></a><span class="in">st_bgs.scaled_lagged <-</span></span> 7745<span id="cb21-965"><a href="#cb21-965" aria-hidden="true" tabindex="-1"></a><span class="in"> st_bgs.scaled %>%</span></span> 7746<span id="cb21-966"><a href="#cb21-966" aria-hidden="true" tabindex="-1"></a><span class="in"> left_join(</span></span> 7747<span id="cb21-967"><a href="#cb21-967" aria-hidden="true" tabindex="-1"></a><span class="in"> st_bgs.lagged %>% </span></span> 7748<span id="cb21-968"><a href="#cb21-968" aria-hidden="true" tabindex="-1"></a><span class="in"> select(GEOID, lag_pct_didnt_work_past_yr) %>% </span></span> 7749<span id="cb21-969"><a href="#cb21-969" aria-hidden="true" tabindex="-1"></a><span class="in"> st_drop_geometry,</span></span> 7750<span id="cb21-970"><a href="#cb21-970" aria-hidden="true" tabindex="-1"></a><span class="in"> by = 'GEOID'</span></span> 7751<span id="cb21-971"><a href="#cb21-971" aria-hidden="true" tabindex="-1"></a><span class="in"> )</span></span> 7752<span id="cb21-972"><a href="#cb21-972" aria-hidden="true" tabindex="-1"></a></span> 7753<span id="cb21-973"><a href="#cb21-973" aria-hidden="true" tabindex="-1"></a><span class="in"># get unique GEOIDs</span></span> 7754<span id="cb21-974"><a href="#cb21-974" aria-hidden="true" tabindex="-1"></a><span class="in">unique_geoids <- unique(st_bgs.scaled_lagged$GEOID)</span></span> 7755<span id="cb21-975"><a href="#cb21-975" aria-hidden="true" tabindex="-1"></a></span> 7756<span id="cb21-976"><a href="#cb21-976" aria-hidden="true" tabindex="-1"></a><span class="in"># randomly split GEOIDs into train (80%) and test (20%) sets</span></span> 7757<span id="cb21-977"><a href="#cb21-977" aria-hidden="true" tabindex="-1"></a><span class="in">train_geoids <- sample(unique_geoids, size = 0.8 * length(unique(unique_geoids)))</span></span> 7758<span id="cb21-978"><a href="#cb21-978" aria-hidden="true" tabindex="-1"></a><span class="in">test_geoids <- setdiff(unique_geoids, train_geoids)</span></span> 7759<span id="cb21-979"><a href="#cb21-979" aria-hidden="true" tabindex="-1"></a></span> 7760<span id="cb21-980"><a href="#cb21-980" aria-hidden="true" tabindex="-1"></a><span class="in"># split data into train and test datasets</span></span> 7761<span id="cb21-981"><a href="#cb21-981" aria-hidden="true" tabindex="-1"></a><span class="in">train_data <- st_bgs.scaled_lagged %>% filter(GEOID %in% train_geoids)</span></span> 7762<span id="cb21-982"><a href="#cb21-982" aria-hidden="true" tabindex="-1"></a><span class="in">test_data <- st_bgs.scaled_lagged %>% filter(GEOID %in% test_geoids)</span></span> 7763<span id="cb21-983"><a href="#cb21-983" aria-hidden="true" tabindex="-1"></a></span> 7764<span id="cb21-984"><a href="#cb21-984" aria-hidden="true" tabindex="-1"></a><span class="in"># # check</span></span> 7765<span id="cb21-985"><a href="#cb21-985" aria-hidden="true" tabindex="-1"></a><span class="in"># n_distinct(train_data$GEOID)</span></span> 7766<span id="cb21-986"><a href="#cb21-986" aria-hidden="true" tabindex="-1"></a><span class="in"># n_distinct(test_data$GEOID)</span></span> 7767<span id="cb21-987"><a href="#cb21-987" aria-hidden="true" tabindex="-1"></a></span> 7768<span id="cb21-988"><a href="#cb21-988" aria-hidden="true" tabindex="-1"></a><span class="in">######################################</span></span> 7769<span id="cb21-989"><a href="#cb21-989" aria-hidden="true" tabindex="-1"></a><span class="in"># glmm with template model builder</span></span> 7770<span id="cb21-990"><a href="#cb21-990" aria-hidden="true" tabindex="-1"></a><span class="in"># a spatial lag variable (pct_didnt_work_past_yr), </span></span> 7771<span id="cb21-991"><a href="#cb21-991" aria-hidden="true" tabindex="-1"></a><span class="in"># a fixed year_group categorical variable, </span></span> 7772<span id="cb21-992"><a href="#cb21-992" aria-hidden="true" tabindex="-1"></a><span class="in"># and GEOID as random effects</span></span> 7773<span id="cb21-993"><a href="#cb21-993" aria-hidden="true" tabindex="-1"></a><span class="in">######################################</span></span> 7774<span id="cb21-994"><a href="#cb21-994" aria-hidden="true" tabindex="-1"></a><span class="in">model_glmm <- glmmTMB(</span></span> 7775<span id="cb21-995"><a href="#cb21-995" aria-hidden="true" tabindex="-1"></a><span class="in"> is_distressed ~ tot_pop_density + pct_didnt_work_past_yr + lag_pct_didnt_work_past_yr +</span></span> 7776<span id="cb21-996"><a href="#cb21-996" aria-hidden="true" tabindex="-1"></a><span class="in"> avg_rent + pct_divorced + pct_developed + factor(year_group) + (1 | GEOID),</span></span> 7777<span id="cb21-997"><a href="#cb21-997" aria-hidden="true" tabindex="-1"></a><span class="in"> data = train_data,</span></span> 7778<span id="cb21-998"><a href="#cb21-998" aria-hidden="true" tabindex="-1"></a><span class="in"> family = binomial(link = 'logit')</span></span> 7779<span id="cb21-999"><a href="#cb21-999" aria-hidden="true" tabindex="-1"></a><span class="in">)</span></span> 7780<span id="cb21-1000"><a href="#cb21-1000" aria-hidden="true" tabindex="-1"></a></span> 7781<span id="cb21-1001"><a href="#cb21-1001" aria-hidden="true" tabindex="-1"></a><span class="in">#summary(model_glmm)</span></span> 7782<span id="cb21-1002"><a href="#cb21-1002" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span> 7783<span id="cb21-1003"><a href="#cb21-1003" aria-hidden="true" tabindex="-1"></a></span> 7784<span id="cb21-1004"><a href="#cb21-1004" aria-hidden="true" tabindex="-1"></a>The model can be expressed in mathematical terms as:</span> 7785<span id="cb21-1005"><a href="#cb21-1005" aria-hidden="true" tabindex="-1"></a></span> 7786<span id="cb21-1006"><a href="#cb21-1006" aria-hidden="true" tabindex="-1"></a><span class="al"></span>{fig-alt="Distress presence predictive logit mixed-effects model."}</span> 7787<span id="cb21-1007"><a href="#cb21-1007" aria-hidden="true" tabindex="-1"></a></span> 7788<span id="cb21-1008"><a href="#cb21-1008" aria-hidden="true" tabindex="-1"></a>Where:</span> 7789<span id="cb21-1009"><a href="#cb21-1009" aria-hidden="true" tabindex="-1"></a></span> 7790<span id="cb21-1010"><a href="#cb21-1010" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span>β~0~ -> fixed intercept</span> 7791<span id="cb21-1011"><a href="#cb21-1011" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span>β~1~,â¦,β~6~ -> fixed-effect coefficients</span> 7792<span id="cb21-1012"><a href="#cb21-1012" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span>γ~k~γ~k~ -> fixed effects for year group (excluding the reference group)</span> 7793<span id="cb21-1013"><a href="#cb21-1013" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span>u~i~ -> random intercept for block group i (GEOID)</span> 7794<span id="cb21-1014"><a href="#cb21-1014" aria-hidden="true" tabindex="-1"></a></span> 7795<span id="cb21-1015"><a href="#cb21-1015" aria-hidden="true" tabindex="-1"></a>Before interpretation and result analysis can be conducted, various diagnostic tests must be completed to ensure model stability and fit.</span> 7796<span id="cb21-1016"><a href="#cb21-1016" aria-hidden="true" tabindex="-1"></a></span> 7797<span id="cb21-1017"><a href="#cb21-1017" aria-hidden="true" tabindex="-1"></a><span class="fu">### Diagnostic Testing</span></span> 7798<span id="cb21-1018"><a href="#cb21-1018" aria-hidden="true" tabindex="-1"></a></span> 7799<span id="cb21-1019"><a href="#cb21-1019" aria-hidden="true" tabindex="-1"></a><span class="in">```{r diagnostic_testing, message = FALSE, warning = FALSE}</span></span> 7800<span id="cb21-1020"><a href="#cb21-1020" aria-hidden="true" tabindex="-1"></a><span class="in">
7800#| cache: false</span></span> 7801<span id="cb21-1021"><a href="#cb21-1021" aria-hidden="true" tabindex="-1"></a></span> 7802<span id="cb21-1022"><a href="#cb21-1022" aria-hidden="true" tabindex="-1"></a><span class="in"># residual diagnostics by simulating residuals to check for</span></span> 7803<span id="cb21-1023"><a href="#cb21-1023" aria-hidden="true" tabindex="-1"></a><span class="in"># uniformity, outliers, non-linearity, and heterscedasticity</span></span> 7804<span id="cb21-1024"><a href="#cb21-1024" aria-hidden="true" tabindex="-1"></a></span> 7805<span id="cb21-1025"><a href="#cb21-1025" aria-hidden="true" tabindex="-1"></a><span class="in"># simulate residuals</span></span> 7806<span id="cb21-1026"><a href="#cb21-1026" aria-hidden="true" tabindex="-1"></a><span class="in">sim_res <- simulateResiduals(model_glmm)</span></span> 7807<span id="cb21-1027"><a href="#cb21-1027" aria-hidden="true" tabindex="-1"></a></span> 7808<span id="cb21-1028"><a href="#cb21-1028" aria-hidden="true" tabindex="-1"></a><span class="in"># plot simulated residuals</span></span> 7809<span id="cb21-1029"><a href="#cb21-1029" aria-hidden="true" tabindex="-1"></a><span class="in">plot(sim_res)</span></span> 7810<span id="cb21-1030"><a href="#cb21-1030" aria-hidden="true" tabindex="-1"></a></span> 7811<span id="cb21-1031"><a href="#cb21-1031" aria-hidden="true" tabindex="-1"></a><span class="in"># check for zero inflation</span></span> 7812<span id="cb21-1032"><a href="#cb21-1032" aria-hidden="true" tabindex="-1"></a><span class="in">testZeroInflation(sim_res)</span></span> 7813<span id="cb21-1033"><a href="#cb21-1033" aria-hidden="true" tabindex="-1"></a></span> 7814<span id="cb21-1034"><a href="#cb21-1034" aria-hidden="true" tabindex="-1"></a><span class="in"># check for normality of random effects,</span></span> 7815<span id="cb21-1035"><a href="#cb21-1035" aria-hidden="true" tabindex="-1"></a><span class="in"># then plot a histogram of them</span></span> 7816<span id="cb21-1036"><a href="#cb21-1036" aria-hidden="true" tabindex="-1"></a><span class="in">ranef_vals <- ranef(model_glmm)$cond$GEOID</span></span> 7817<span id="cb21-1037"><a href="#cb21-1037" aria-hidden="true" tabindex="-1"></a></span> 7818<span id="cb21-1038"><a href="#cb21-1038" aria-hidden="true" tabindex="-1"></a><span class="in">ggplot(data.frame(ranef = ranef_vals[,1]), aes(x = ranef)) +</span></span> 7819<span id="cb21-1039"><a href="#cb21-1039" aria-hidden="true" tabindex="-1"></a><span class="in"> geom_histogram(bins = 30) +</span></span> 7820<span id="cb21-1040"><a href="#cb21-1040" aria-hidden="true" tabindex="-1"></a><span class="in"> labs(title = 'Distribution of Random Intercepts (GEOID)',</span></span> 7821<span id="cb21-1041"><a href="#cb21-1041" aria-hidden="true" tabindex="-1"></a><span class="in"> x = 'Random Effects', y = 'Count')</span></span> 7822<span id="cb21-1042"><a href="#cb21-1042" aria-hidden="true" tabindex="-1"></a></span> 7823<span id="cb21-1043"><a href="#cb21-1043" aria-hidden="true" tabindex="-1"></a><span class="in"># approximate multicollinearity</span></span> 7824<span id="cb21-1044"><a href="#cb21-1044" aria-hidden="true" tabindex="-1"></a><span class="in">vif_check <- lm(</span></span> 7825<span id="cb21-1045"><a href="#cb21-1045" aria-hidden="true" tabindex="-1"></a><span class="in"> is_distressed ~ tot_pop_density + pct_didnt_work_past_yr + lag_pct_didnt_work_past_yr +</span></span> 7826<span id="cb21-1046"><a href="#cb21-1046" aria-hidden="true" tabindex="-1"></a><span class="in"> avg_rent + pct_divorced + pct_developed + factor(year_group),</span></span> 7827<span id="cb21-1047"><a href="#cb21-1047" aria-hidden="true" tabindex="-1"></a><span class="in"> data = train_data</span></span> 7828<span id="cb21-1048"><a href="#cb21-1048" aria-hidden="true" tabindex="-1"></a><span class="in">)</span></span> 7829<span id="cb21-1049"><a href="#cb21-1049" aria-hidden="true" tabindex="-1"></a></span> 7830<span id="cb21-1050"><a href="#cb21-1050" aria-hidden="true" tabindex="-1"></a><span class="in">vif(vif_check)</span></span> 7831<span id="cb21-1051"><a href="#cb21-1051" aria-hidden="true" tabindex="-1"></a></span> 7832<span id="cb21-1052"><a href="#cb21-1052" aria-hidden="true" tabindex="-1"></a><span class="in">
7832###</span></span> 7833<span id="cb21-1053"><a href="#cb21-1053" aria-hidden="true" tabindex="-1"></a><span class="in"># Moran's I (spatial autocorrelation)</span></span> 7834<span id="cb21-1054"><a href="#cb21-1054" aria-hidden="true" tabindex="-1"></a><span class="in">###</span></span> 7835<span id="cb21-1055"><a href="#cb21-1055" aria-hidden="true" tabindex="-1"></a></span> 7836<span id="cb21-1056"><a href="#cb21-1056" aria-hidden="true" tabindex="-1"></a><span class="in"># </span></span> 7837<span id="cb21-1057"><a href="#cb21-1057" aria-hidden="true" tabindex="-1"></a><span class="in">train_bgs <- train_data %>% distinct(GEOID, .keep_all = TRUE)</span></span> 7838<span id="cb21-1058"><a href="#cb21-1058" aria-hidden="true" tabindex="-1"></a><span class="in">unique_train_geoids <- unique(train_bgs$GEOID)</span></span> 7839<span id="cb21-1059"><a href="#cb21-1059" aria-hidden="true" tabindex="-1"></a></span> 7840<span id="cb21-1060"><a href="#cb21-1060" aria-hidden="true" tabindex="-1"></a><span class="in"># extract residuals and attach GEOID, calculate avg residuals per block group,</span></span> 7841<span id="cb21-1061"><a href="#cb21-1061" aria-hidden="true" tabindex="-1"></a><span class="in"># then ensure order matches spatial weights matrix</span></span> 7842<span id="cb21-1062"><a href="#cb21-1062" aria-hidden="true" tabindex="-1"></a><span class="in">avg_res <- data.frame(</span></span> 7843<span id="cb21-1063"><a href="#cb21-1063" aria-hidden="true" tabindex="-1"></a><span class="in"> GEOID = train_data$GEOID,</span></span> 7844<span id="cb21-1064"><a href="#cb21-1064" aria-hidden="true" tabindex="-1"></a><span class="in"> residuals = residuals(model_glmm)</span></span> 7845<span id="cb21-1065"><a href="#cb21-1065" aria-hidden="true" tabindex="-1"></a><span class="in">) %>%</span></span> 7846<span id="cb21-1066"><a href="#cb21-1066" aria-hidden="true" tabindex="-1"></a><span class="in"> group_by(GEOID) %>%</span></span> 7847<span id="cb21-1067"><a href="#cb21-1067" aria-hidden="true" tabindex="-1"></a><span class="in"> summarize(mean_residual = mean(residuals, na.rm = TRUE)) %>%</span></span> 7848<span id="cb21-1068"><a href="#cb21-1068" aria-hidden="true" tabindex="-1"></a><span class="in"> arrange(match(GEOID, unique_train_geoids))</span></span> 7849<span id="cb21-1069"><a href="#cb21-1069" aria-hidden="true" tabindex="-1"></a></span> 7850<span id="cb21-1070"><a href="#cb21-1070" aria-hidden="true" tabindex="-1"></a><span class="in"># get centroids of unique block groups</span></span> 7851<span id="cb21-1071"><a href="#cb21-1071" aria-hidden="true" tabindex="-1"></a><span class="in">centroids_train <- st_centroid(train_bgs)</span></span> 7852<span id="cb21-1072"><a href="#cb21-1072" aria-hidden="true" tabindex="-1"></a><span class="in">coords_train <- st_coordinates(centroids_train)[, 1:2]</span></span> 7853<span id="cb21-1073"><a href="#cb21-1073" aria-hidden="true" tabindex="-1"></a></span> 7854<span id="cb21-1074"><a href="#cb21-1074" aria-hidden="true" tabindex="-1"></a><span class="in"># define k-nearest neighbors</span></span> 7855<span id="cb21-1075"><a href="#cb21-1075" aria-hidden="true" tabindex="-1"></a><span class="in">knn_neighbors_train <- knearneigh(coords_train, k = k)</span></span> 7856<span id="cb21-1076"><a href="#cb21-1076" aria-hidden="true" tabindex="-1"></a><span class="in">nb_train <- knn2nb(knn_neighbors_train)</span></span> 7857<span id="cb21-1077"><a href="#cb21-1077" aria-hidden="true" tabindex="-1"></a></span> 7858<span id="cb21-1078"><a href="#cb21-1078" aria-hidden="true" tabindex="-1"></a><span class="in"># create spatial weights matrix</span></span> 7859<span id="cb21-1079"><a href="#cb21-1079" aria-hidden="true" tabindex="-1"></a><span class="in">lw_train <- nb2listw(nb_train, style = 'W')</span></span> 7860<span id="cb21-1080"><a href="#cb21-1080" aria-hidden="true" tabindex="-1"></a></span> 7861<span id="cb21-1081"><a href="#cb21-1081" aria-hidden="true" tabindex="-1"></a><span class="in"># Moran's I test</span></span> 7862<span id="cb21-1082"><a href="#cb21-1082" aria-hidden="true" tabindex="-1"></a><span class="in">moran_test <- moran.test(avg_res$mean_residual, lw_train)</span></span> 7863<span id="cb21-1083"><a href="#cb21-1083" aria-hidden="true" tabindex="-1"></a></span> 7864<span id="cb21-1084"><a href="#cb21-1084" aria-hidden="true" tabindex="-1"></a><span class="in">moran <- data.frame(</span></span> 7865<span id="cb21-1085"><a href="#cb21-1085" aria-hidden="true" tabindex="-1"></a><span class="in"> Moran_I = moran_test$estimate[[1]],</span></span> 7866<span id="cb21-1086"><a href="#cb21-1086" aria-hidden="true" tabindex="-1"></a><span class="in"> Moran_I_Std_Dev = moran_test$statistic,</span></span> 7867<span id="cb21-1087"><a href="#cb21-1087" aria-hidden="true" tabindex="-1"></a><span class="in"> p_value = moran_test$p.value</span></span> 7868<span id="cb21-1088"><a href="#cb21-1088" aria-hidden="true" tabindex="-1"></a><span class="in">)</span></span> 7869<span id="cb21-1089"><a href="#cb21-1089" aria-hidden="true" tabindex="-1"></a></span> 7870<span id="cb21-1090"><a href="#cb21-1090" aria-hidden="true" tabindex="-1"></a><span class="in">moran %>%</span></span> 7871<span id="cb21-1091"><a href="#cb21-1091" aria-hidden="true" tabindex="-1"></a><span class="in"> gt() %>%</span></span> 7872<span id="cb21-1092"><a href="#cb21-1092" aria-hidden="true" tabindex="-1"></a><span class="in"> tab_header(</span></span> 7873<span id="cb21-1093"><a href="#cb21-1093" aria-hidden="true" tabindex="-1"></a><span class="in"> title = 'Moran\'s I Test Results'</span></span> 7874<span id="cb21-1094"><a href="#cb21-1094" aria-hidden="true" tabindex="-1"></a><span class="in"> ) %>%</span></span> 7875<span id="cb21-1095"><a href="#cb21-1095" aria-hidden="true" tabindex="-1"></a><span class="in"> #fmt_markdown(columns = c(CI)) %>%</span></span> 7876<span id="cb21-1096"><a href="#cb21-1096" aria-hidden="true" tabindex="-1"></a><span class="in"> cols_label(</span></span> 7877<span id="cb21-1097"><a href="#cb21-1097" aria-hidden="true" tabindex="-1"></a><span class="in"> Moran_I = 'Moran\'s I',</span></span> 7878<span id="cb21-1098"><a href="#cb21-1098" aria-hidden="true" tabindex="-1"></a><span class="in"> Moran_I_Std_Dev = 'Std. Dev.',</span></span> 7879<span id="cb21-1099"><a href="#cb21-1099" aria-hidden="true" tabindex="-1"></a><span class="in"> p_value = 'p-value'</span></span> 7880<span id="cb21-1100"><a href="#cb21-1100" aria-hidden="true" tabindex="-1"></a><span class="in"> ) %>%</span></span> 7881<span id="cb21-1101"><a href="#cb21-1101" aria-hidden="true" tabindex="-1"></a><span class="in"> tab_options(</span></span> 7882<span id="cb21-1102"><a href="#cb21-1102" aria-hidden="true" tabindex="-1"></a><span class="in"> table.font.size = 'large'</span></span> 7883<span id="cb21-1103"><a href="#cb21-1103" aria-hidden="true" tabindex="-1"></a><span class="in"> )</span></span> 7884<span id="cb21-1104"><a href="#cb21-1104" aria-hidden="true" tabindex="-1"></a><span class="in"> </span></span> 7885<span id="cb21-1105"><a href="#cb21-1105" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span> 7886<span id="cb21-1106"><a href="#cb21-1106" aria-hidden="true" tabindex="-1"></a></span> 7887<span id="cb21-1107"><a href="#cb21-1107" aria-hidden="true" tabindex="-1"></a>The KS test result for uniformity showed no significant deviation from uniformity (p = 0.547), which suggests expected well-behaved residuals. The dispersion test showed no significant over- or under-dispersion (p = 0.08), signalling appropriate levels of variance. The outlier test showed no significant outliers (p = 0.645). The zero inflation test showed no evidence of zero inflation (p = 0.512). The QQ plot indicates appropriate residual distribution, and the Residuals vs. Predicted plot shows a slight curve at higher predictions, but is generally fairly flat otherwise. All VIF values used to measure multicollinearity are less than 5, meaning there is no concerning multicollinearity between the predictors.</span> 7888<span id="cb21-1108"><a href="#cb21-1108" aria-hidden="true" tabindex="-1"></a></span> 7889<span id="cb21-1109"><a href="#cb21-1109" aria-hidden="true" tabindex="-1"></a>While significant spatial autocorrelation is present in the residuals (Moran's I = 0.088, p < 0.001), its magnitude is modest and expected given the spatial clustering of socioeconomic distress. In precursor models, Moran's I reached upwards of 0.15. The inclusion of a lagged variable and block group-level random effects substantially reduced spatial dependence compared to these initial models.</span> 7890<span id="cb21-1110"><a href="#cb21-1110" aria-hidden="true" tabindex="-1"></a></span> 7891<span id="cb21-1111"><a href="#cb21-1111" aria-hidden="true" tabindex="-1"></a>Overall, the model fits well, the random effects have a reasonable distribution, and multicollinearity is not a concern. While spatial autocorrelation is present, it is moderate and characteristic of socioeconomic spatial data. There were also attempts at limiting spatial autocorrelation in the form of introducing a spatially lagged covariate, which alleviated some of the autocorrelation. Remaining autocorrelation likely reflects unmeasured spatial processes beyond the scope of this analysis.</span> 7892<span id="cb21-1112"><a href="#cb21-1112" aria-hidden="true" tabindex="-1"></a></span> 7893<span id="cb21-1113"><a href="#cb21-1113" aria-hidden="true" tabindex="-1"></a><span class="fu">### Fixed Effects</span></span> 7894<span id="cb21-1114"><a href="#cb21-1114" aria-hidden="true" tabindex="-1"></a></span> 7895<span id="cb21-1115"><a href="#cb21-1115" aria-hidden="true" tabindex="-1"></a><span class="in">```{r fixed_effects, message=F, warning=F}</span></span> 7896<span id="cb21-1116"><a href="#cb21-1116" aria-hidden="true" tabindex="-1"></a><span class="in">
7896#| cache: false</span></span> 7897<span id="cb21-1117"><a href="#cb21-1117" aria-hidden="true" tabindex="-1"></a></span> 7898<span id="cb21-1118"><a href="#cb21-1118" aria-hidden="true" tabindex="-1"></a><span class="in"># extract fixed effects</span></span> 7899<span id="cb21-1119"><a href="#cb21-1119" aria-hidden="true" tabindex="-1"></a><span class="in">fixef_vals <- fixef(model_glmm)$cond</span></span> 7900<span id="cb21-1120"><a href="#cb21-1120" aria-hidden="true" tabindex="-1"></a><span class="in">odds_ratios <- exp(fixef_vals)</span></span> 7901<span id="cb21-1121"><a href="#cb21-1121" aria-hidden="true" tabindex="-1"></a></span> 7902<span id="cb21-1122"><a href="#cb21-1122" aria-hidden="true" tabindex="-1"></a><span class="in"># get standard errors</span></span> 7903<span id="cb21-1123"><a href="#cb21-1123" aria-hidden="true" tabindex="-1"></a><span class="in">se_vals <- summary(model_glmm)$coefficients$cond[, 'Std. Error']</span></span> 7904<span id="cb21-1124"><a href="#cb21-1124" aria-hidden="true" tabindex="-1"></a></span> 7905<span id="cb21-1125"><a href="#cb21-1125" aria-hidden="true" tabindex="-1"></a><span class="in"># compute 95% confidence interval</span></span> 7906<span id="cb21-1126"><a href="#cb21-1126" aria-hidden="true" tabindex="-1"></a><span class="in">lower_ci <- exp(fixef_vals - 1.96 * se_vals)</span></span> 7907<span id="cb21-1127"><a href="#cb21-1127" aria-hidden="true" tabindex="-1"></a><span class="in">upper_ci <- exp(fixef_vals + 1.96 * se_vals)</span></span> 7908<span id="cb21-1128"><a href="#cb21-1128" aria-hidden="true" tabindex="-1"></a></span> 7909<span id="cb21-1129"><a href="#cb21-1129" aria-hidden="true" tabindex="-1"></a><span class="in"># get fixed effects data</span></span> 7910<span id="cb21-1130"><a href="#cb21-1130" aria-hidden="true" tabindex="-1"></a><span class="in">fixed_effects <-</span></span> 7911<span id="cb21-1131"><a href="#cb21-1131" aria-hidden="true" tabindex="-1"></a><span class="in"> data.frame(</span></span> 7912<span id="cb21-1132"><a href="#cb21-1132" aria-hidden="true" tabindex="-1"></a><span class="in"> Estimate = fixef_vals,</span></span> 7913<span id="cb21-1133"><a href="#cb21-1133" aria-hidden="true" tabindex="-1"></a><span class="in"> Odds_Ratio = odds_ratios,</span></span> 7914<span id="cb21-1134"><a href="#cb21-1134" aria-hidden="true" tabindex="-1"></a><span class="in"> CI_Lower = lower_ci,</span></span> 7915<span id="cb21-1135"><a href="#cb21-1135" aria-hidden="true" tabindex="-1"></a><span class="in"> CI_Upper = upper_ci,</span></span> 7916<span id="cb21-1136"><a href="#cb21-1136" aria-hidden="true" tabindex="-1"></a><span class="in"> p_value = summary(model_glmm)$coefficients$cond[, 'Pr(>|z|)']</span></span> 7917<span id="cb21-1137"><a href="#cb21-1137" aria-hidden="true" tabindex="-1"></a><span class="in"> )</span></span> 7918<span id="cb21-1138"><a href="#cb21-1138" aria-hidden="true" tabindex="-1"></a></span> 7919<span id="cb21-1139"><a href="#cb21-1139" aria-hidden="true" tabindex="-1"></a><span class="in"># odds ratio table</span></span> 7920<span id="cb21-1140"><a href="#cb21-1140" aria-hidden="true" tabindex="-1"></a><span class="in">odds_table <-</span></span> 7921<span id="cb21-1141"><a href="#cb21-1141" aria-hidden="true" tabindex="-1"></a><span class="in"> fixed_effects %>%</span></span> 7922<span id="cb21-1142"><a href="#cb21-1142" aria-hidden="true" tabindex="-1"></a><span class="in"> mutate(</span></span> 7923<span id="cb21-1143"><a href="#cb21-1143" aria-hidden="true" tabindex="-1"></a><span class="in"> Predictor = rownames(.),</span></span> 7924<span id="cb21-1144"><a href="#cb21-1144" aria-hidden="true" tabindex="-1"></a><span class="in"> Estimate = round(Estimate, 3),</span></span> 7925<span id="cb21-1145"><a href="#cb21-1145" aria-hidden="true" tabindex="-1"></a><span class="in"> Odds_Ratio = round(Odds_Ratio, 2),</span></span> 7926<span id="cb21-1146"><a href="#cb21-1146" aria-hidden="true" tabindex="-1"></a><span class="in"> CI = paste0('[', round(CI_Lower, 2), ', ', round(CI_Upper, 2), ']'),</span></span> 7927<span id="cb21-1147"><a href="#cb21-1147" aria-hidden="true" tabindex="-1"></a><span class="in"> p_value = formatC(p_value, format = 'e', digits = 2)</span></span> 7928<span id="cb21-1148"><a href="#cb21-1148" aria-hidden="true" tabindex="-1"></a><span class="in"> ) %>%</span></span> 7929<span id="cb21-1149"><a href="#cb21-1149" aria-hidden="true" tabindex="-1"></a><span class="in"> select(Predictor, Estimate, Odds_Ratio, CI, p_value) %>%</span></span> 7930<span id="cb21-1150"><a href="#cb21-1150" aria-hidden="true" tabindex="-1"></a><span class="in"> gt() %>%</span></span> 7931<span id="cb21-1151"><a href="#cb21-1151" aria-hidden="true" tabindex="-1"></a><span class="in"> tab_header(</span></span> 7932<span id="cb21-1152"><a href="#cb21-1152" aria-hidden="true" tabindex="-1"></a><span class="in"> title = 'Odds Ratios from Logit Mixed-Effects Model',</span></span> 7933<span id="cb21-1153"><a href="#cb21-1153" aria-hidden="true" tabindex="-1"></a><span class="in"> subtitle = '95% Confidence Intervals and p-values'</span></span> 7934<span id="cb21-1154"><a href="#cb21-1154" aria-hidden="true" tabindex="-1"></a><span class="in"> ) %>%</span></span> 7935<span id="cb21-1155"><a href="#cb21-1155" aria-hidden="true" tabindex="-1"></a><span class="in"> fmt_markdown(columns = c(CI)) %>%</span></span> 7936<span id="cb21-1156"><a href="#cb21-1156" aria-hidden="true" tabindex="-1"></a><span class="in"> cols_label(</span></span> 7937<span id="cb21-1157"><a href="#cb21-1157" aria-hidden="true" tabindex="-1"></a><span class="in"> Predictor = 'Predictor',</span></span> 7938<span id="cb21-1158"><a href="#cb21-1158" aria-hidden="true" tabindex="-1"></a><span class="in"> Odds_Ratio = 'Odds Ratio',</span></span> 7939<span id="cb21-1159"><a href="#cb21-1159" aria-hidden="true" tabindex="-1"></a><span class="in"> CI = '95% CI',</span></span> 7940<span id="cb21-1160"><a href="#cb21-1160" aria-hidden="true" tabindex="-1"></a><span class="in"> p_value = 'p_value'</span></span> 7941<span id="cb21-1161"><a href="#cb21-1161" aria-hidden="true" tabindex="-1"></a><span class="in"> ) %>%</span></span> 7942<span id="cb21-1162"><a href="#cb21-1162" aria-hidden="true" tabindex="-1"></a><span class="in"> tab_options(</span></span> 7943<span id="cb21-1163"><a href="#cb21-1163" aria-hidden="true" tabindex="-1"></a><span class="in"> table.font.size = 'small',</span></span> 7944<span id="cb21-1164"><a href="#cb21-1164" aria-hidden="true" tabindex="-1"></a><span class="in"> heading.align = 'left'</span></span> 7945<span id="cb21-1165"><a href="#cb21-1165" aria-hidden="true" tabindex="-1"></a><span class="in"> )</span></span> 7946<span id="cb21-1166"><a href="#cb21-1166" aria-hidden="true" tabindex="-1"></a></span> 7947<span id="cb21-1167"><a href="#cb21-1167" aria-hidden="true" tabindex="-1"></a><span class="in">odds_table</span></span> 7948<span id="cb21-1168"><a href="#cb21-1168" aria-hidden="true" tabindex="-1"></a></span> 7949<span id="cb21-1169"><a href="#cb21-1169" aria-hidden="true" tabindex="-1"></a><span class="in"># add significance to highlight predictors that are significant</span></span> 7950<span id="cb21-1170"><a href="#cb21-1170" aria-hidden="true" tabindex="-1"></a><span class="in">fixed_effects.plt_df <-</span></span> 7951<span id="cb21-1171"><a href="#cb21-1171" aria-hidden="true" tabindex="-1"></a><span class="in"> fixed_effects %>%</span></span> 7952<span id="cb21-1172"><a href="#cb21-1172" aria-hidden="true" tabindex="-1"></a><span class="in"> mutate(</span></span> 7953<span id="cb21-1173"><a href="#cb21-1173" aria-hidden="true" tabindex="-1"></a><span class="in"> Predictor = rownames(.),</span></span> 7954<span id="cb21-1174"><a href="#cb21-1174" aria-hidden="true" tabindex="-1"></a><span class="in"> Significance = ifelse(p_value < 0.05, 'Significant', 'Non Significant')</span></span> 7955<span id="cb21-1175"><a href="#cb21-1175" aria-hidden="true" tabindex="-1"></a><span class="in"> )</span></span> 7956<span id="cb21-1176"><a href="#cb21-1176" aria-hidden="true" tabindex="-1"></a></span> 7957<span id="cb21-1177"><a href="#cb21-1177" aria-hidden="true" tabindex="-1"></a><span class="in"># plot fixed effects with confidence intervals and significance</span></span> 7958<span id="cb21-1178"><a href="#cb21-1178" aria-hidden="true" tabindex="-1"></a><span class="in">fixed_effects_plt <-</span></span> 7959<span id="cb21-1179"><a href="#cb21-1179" aria-hidden="true" tabindex="-1"></a><span class="in"> ggplot(fixed_effects.plt_df, aes(x = reorder(Predictor, Odds_Ratio), y = Odds_Ratio)) +</span></span> 7960<span id="cb21-1180"><a href="#cb21-1180" aria-hidden="true" tabindex="-1"></a><span class="in"> geom_point(aes(color = Significance), size = 3) +</span></span> 7961<span id="cb21-1181"><a href="#cb21-1181" aria-hidden="true" tabindex="-1"></a><span class="in"> geom_errorbar(aes(ymin = CI_Lower, ymax = CI_Upper, color = Significance), </span></span> 7962<span id="cb21-1182"><a href="#cb21-1182" aria-hidden="true" tabindex="-1"></a><span class="in"> width = 0.2) +</span></span> 7963<span id="cb21-1183"><a href="#cb21-1183" aria-hidden="true" tabindex="-1"></a><span class="in"> geom_hline(yintercept = 1, linetype = 'dashed', color = 'red') +</span></span> 7964<span id="cb21-1184"><a href="#cb21-1184" aria-hidden="true" tabindex="-1"></a><span class="in"> scale_y_log10() + # log scale for more condense plot</span></span> 7965<span id="cb21-1185"><a href="#cb21-1185" aria-hidden="true" tabindex="-1"></a><span class="in"> coord_flip() +</span></span> 7966<span id="cb21-1186"><a href="#cb21-1186" aria-hidden="true" tabindex="-1"></a><span class="in"> labs(</span></span> 7967<span id="cb21-1187"><a href="#cb21-1187" aria-hidden="true" tabindex="-1"></a><span class="in"> title = 'Odds Ratios with 95% Confidence Intervals',</span></span> 7968<span id="cb21-1188"><a href="#cb21-1188" aria-hidden="true" tabindex="-1"></a><span class="in"> subtitle = 'Logit Mixed-Effects Model (glmmTMB) with Spatial Lag',</span></span> 7969<span id="cb21-1189"><a href="#cb21-1189" aria-hidden="true" tabindex="-1"></a><span class="in"> x = 'Predictor',</span></span> 7970<span id="cb21-1190"><a href="#cb21-1190" aria-hidden="true" tabindex="-1"></a><span class="in"> y = 'Odds Ratio (log scale)',</span></span> 7971<span id="cb21-1191"><a href="#cb21-1191" aria-hidden="true" tabindex="-1"></a><span class="in"> color = 'Significance'</span></span> 7972<span id="cb21-1192"><a href="#cb21-1192" aria-hidden="true" tabindex="-1"></a><span class="in"> )</span></span> 7973<span id="cb21-1193"><a href="#cb21-1193" aria-hidden="true" tabindex="-1"></a></span> 7974<span id="cb21-1194"><a href="#cb21-1194" aria-hidden="true" tabindex="-1"></a><span class="in">fixed_effects_plt</span></span> 7975<span id="cb21-1195"><a href="#cb21-1195" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span> 7976<span id="cb21-1196"><a href="#cb21-1196" aria-hidden="true" tabindex="-1"></a></span> 7977<span id="cb21-1197"><a href="#cb21-1197" aria-hidden="true" tabindex="-1"></a>The fit statistics (AIC = 3595.7, BIC = 3669.7, Log-Likelihood = -1787.8) suggests that the model is well-fitting given the number of predictors and amount of data. Since all predictors were scaled (i.e., standardized), interpretations are based on 1 standard deviation changes. Population density, unemployment rate, divorced rate, and percentage developed land cover are all strong, statistically significant predictors of distress and are all associated with a large increased odds that a block group is distressed. The highly increased odds of distress associated with higher population density and percentage developed land cover suggests that denser development and urbanization play a significant role in distress. At the same time, average rent plays the role of a minor yet statistically significant protective factor and would suggest that a block group with higher rent may be more stable. </span> 7978<span id="cb21-1198"><a href="#cb21-1198" aria-hidden="true" tabindex="-1"></a></span> 7979<span id="cb21-1199"><a href="#cb21-1199" aria-hidden="true" tabindex="-1"></a>The 2019-2023 year group is statistically significant, which shows that a trend of higher distressed odds started especially after 2018. This would suggest that various socioeconomic factors have gotten worse in the Southern Tier at least since 2018, leading to increased distress across the region over time and may have been exacerbated (or even caused) by the COVID pandemic.<
7979/span> 7980<span id="cb21-1200"><a href="#cb21-1200" aria-hidden="true" tabindex="-1"></a></span> 7981<span id="cb21-1201"><a href="#cb21-1201" aria-hidden="true" tabindex="-1"></a>Distress risk is not just temporal. The spatially lagged covariate of unemployment rate is also a strong, significant predictor of distress. It suggests that higher rates of unemployment in neighboring block groups are strongly associated with increased odds of distress in a given block group. More specifically, a 1 standard deviation increase in the spatially lagged unemployment rate is associated with a threefold increase in the odds that a block group is classified as distressed, independent of its own unemployment rate. Even if a block group's own unemployment is more moderate, being surrounded by high-unemployment areas still substantially increases distress risk.</span> 7982<span id="cb21-1202"><a href="#cb21-1202" aria-hidden="true" tabindex="-1"></a></span> 7983<span id="cb21-1203"><a href="#cb21-1203" aria-hidden="true" tabindex="-1"></a><span class="fu">### Model Prediction Accuracy</span></span> 7984<span id="cb21-1204"><a href="#cb21-1204" aria-hidden="true" tabindex="-1"></a></span> 7985<span id="cb21-1205"><a href="#cb21-1205" aria-hidden="true" tabindex="-1"></a>A confusion matrix is created from predicted probabilities that are converted to binary predictions, and then the model's overall accuracy, sensitivity, and specificity are calculated by using the remaining 20% of unique block group GEOIDs as test data.</span> 7986<span id="cb21-1206"><a href="#cb21-1206" aria-hidden="true" tabindex="-1"></a></span> 7987<span id="cb21-1207"><a href="#cb21-1207" aria-hidden="true" tabindex="-1"></a><span class="in">```{r model_accuracy, message=F, warning=F}</span></span> 7988<span id="cb21-1208"><a href="#cb21-1208" aria-hidden="true" tabindex="-1"></a><span class="in">#| cache: false</span></span> 7989<span id="cb21-1209"><a href="#cb21-1209" aria-hidden="true" tabindex="-1"></a></span> 7990<span id="cb21-1210"><a href="#cb21-1210" aria-hidden="true" tabindex="-1"></a><span class="in"># get predicted probabilities</span></span> 7991<span id="cb21-1211"><a href="#cb21-1211" aria-hidden="true" tabindex="-1"></a><span class="in">pred_probs <- predict(model_glmm, newdata = test_data,</span></span> 7992<span id="cb21-1212"><a href="#cb21-1212" aria-hidden="true" tabindex="-1"></a><span class="in"> type = 'response', allow.new.levels = TRUE)</span></span> 7993<span id="cb21-1213"><a href="#cb21-1213" aria-hidden="true" tabindex="-1"></a></span> 7994<span id="cb21-1214"><a href="#cb21-1214" aria-hidden="true" tabindex="-1"></a><span class="in"># convert probabilities to binary predictions</span></span> 7995<span id="cb21-1215"><a href="#cb21-1215" aria-hidden="true" tabindex="-1"></a><span class="in">pred_class <- ifelse(pred_probs > 0.5, 1, 0)</span></span> 7996<span id="cb21-1216"><a href="#cb21-1216" aria-hidden="true" tabindex="-1"></a></span> 7997<span id="cb21-1217"><a href="#cb21-1217" aria-hidden="true" tabindex="-1"></a><span class="in"># create confusion matrix, then extract values</span></span> 7998<span id="cb21-1218"><a href="#cb21-1218" aria-hidden="true" tabindex="-1"></a><span class="in">conf_matrix <- table(Predicted = pred_class, Actual = test_data$is_distressed)</span></span> 7999<span id="cb21-1219"><a href="#cb21-1219" aria-hidden="true" tabindex="-1"></a></span> 8000<span id="cb21-1220"><a href="#cb21-1220" aria-hidden="true" tabindex="-1"></a><span class="in">TN <- conf_matrix[1,1]</span></span> 8001<span id="cb21-1221"><a href="#cb21-1221" aria-hidden="true" tabindex="-1"></a><span class="in">FP <- conf_matrix[2,1]</span></span> 8002<span id="cb21-1222"><a href="#cb21-1222" aria-hidden="true" tabindex="-1"></a><span class="in">FN <- conf_matrix[1,2]</span></span> 8003<span id="cb21-1223"><a href="#cb21-1223" aria-hidden="true" tabindex="-1"></a><span class="in">TP <- conf_matrix[2,2]</span></span> 8004<span id="cb21-1224"><a href="#cb21-1224" aria-hidden="true" tabindex="-1"></a></span> 8005<span id="cb21-1225"><a href="#cb21-1225" aria-hidden="true" tabindex="-1"></a><span class="in"># create confusion matrix dataframe</span></span> 8006<span id="cb21-1226"><a href="#cb21-1226" aria-hidden="true" tabindex="-1"></a><span class="in">confusion_df <- tibble(</span></span> 8007<span id="cb21-1227"><a href="#cb21-1227" aria-hidden="true" tabindex="-1"></a><span class="in"> Outcome = c('True Negatives', 'False Positives',</span></span> 8008<span id="cb21-1228"><a href="#cb21-1228" aria-hidden="true" tabindex="-1"></a><span class="in"> 'False Negatives', 'True Positives'),</span></span> 8009<span id="cb21-1229"><a href="#cb21-1229" aria-hidden="true" tabindex="-1"></a><span class="in"> Count = c(TN, FP, FN, TP)</span></span> 8010<span id="cb21-1230"><a href="#cb21-1230" aria-hidden="true" tabindex="-1"></a><span class="in">)</span></span> 8011<span id="cb21-1231"><a href="#cb21-1231" aria-hidden="true" tabindex="-1"></a></span> 8012<span id="cb21-1232"><a href="#cb21-1232" aria-hidden="true" tabindex="-1"></a><span class="in"># create gt table for confusion matrix</span></span> 8013<span id="cb21-1233"><a href="#cb21-1233" aria-hidden="true" tabindex="-1"></a><span class="in">confusion_gt <-</span></span> 8014<span id="cb21-1234"><a href="#cb21-1234" aria-hidden="true" tabindex="-1"></a><span class="in"> confusion_df %>%</span></span> 8015<span id="cb21-1235"><a href="#cb21-1235" aria-hidden="true" tabindex="-1"></a><span class="in"> gt() %>%</span></span> 8016<span id="cb21-1236"><a href="#cb21-1236" aria-hidden="true" tabindex="-1"></a><span class="in"> tab_header(title = 'Confusion Matrix: Distressed Status Prediction Model') %>%</span></span> 8017<span id="cb21-1237"><a href="#cb21-1237" aria-hidden="true" tabindex="-1"></a><span class="in"> fmt_number(columns = Count, decimals = 0)</span></span> 8018<span id="cb21-1238"><a href="#cb21-1238" aria-hidden="true" tabindex="-1"></a></span> 8019<span id="cb21-1239"><a href="#cb21-1239" aria-hidden="true" tabindex="-1"></a><span class="in"># calculate metrics</span></span> 8020<span id="cb21-1240"><a href="#cb21-1240" aria-hidden="true" tabindex="-1"></a><span class="in">accuracy <- (TP + TN) / sum(conf_matrix)</span></span> 8021<span id="cb21-1241"><a href="#cb21-1241" aria-hidden="true" tabindex="-1"></a><span class="in">sensitivity <- TP / (TP + FN)</span></span> 8022<span id="cb21-1242"><a href="#cb21-1242" aria-hidden="true" tabindex="-1"></a><span class="in">specificity <- TN / (TN + FP)</span></span> 8023<span id="cb21-1243"><a href="#cb21-1243" aria-hidden="true" tabindex="-1"></a></span> 8024<span id="cb21-1244"><a href="#cb21-1244" aria-hidden="true" tabindex="-1"></a><span class="in"># create metrix dataframe</span></span> 8025<span id="cb21-1245"><a href="#cb21-1245" aria-hidden="true" tabindex="-1"></a><span class="in">metrics_df <- tibble(</span></span> 8026<span id="cb21-1246"><a href="#cb21-1246" aria-hidden="true" tabindex="-1"></a><span class="in"> Metric = c('Accuracy', 'Sensitivity (Recall)', 'Specificity'),</span></span> 8027<span id="cb21-1247"><a href="#cb21-1247" aria-hidden="true" tabindex="-1"></a><span class="in">
8027 Value = c(accuracy, sensitivity, specificity)</span></span> 8028<span id="cb21-1248"><a href="#cb21-1248" aria-hidden="true" tabindex="-1"></a><span class="in">)</span></span> 8029<span id="cb21-1249"><a href="#cb21-1249" aria-hidden="true" tabindex="-1"></a></span> 8030<span id="cb21-1250"><a href="#cb21-1250" aria-hidden="true" tabindex="-1"></a><span class="in"># create gt table for metrics</span></span> 8031<span id="cb21-1251"><a href="#cb21-1251" aria-hidden="true" tabindex="-1"></a><span class="in">metrics_gt <-</span></span> 8032<span id="cb21-1252"><a href="#cb21-1252" aria-hidden="true" tabindex="-1"></a><span class="in"> metrics_df %>%</span></span> 8033<span id="cb21-1253"><a href="#cb21-1253" aria-hidden="true" tabindex="-1"></a><span class="in"> mutate(Value = round(Value, 4)) %>%</span></span> 8034<span id="cb21-1254"><a href="#cb21-1254" aria-hidden="true" tabindex="-1"></a><span class="in"> gt() %>%</span></span> 8035<span id="cb21-1255"><a href="#cb21-1255" aria-hidden="true" tabindex="-1"></a><span class="in"> tab_header(title = 'Distressed Status Prediction Model Performance Metrics') %>%</span></span> 8036<span id="cb21-1256"><a href="#cb21-1256" aria-hidden="true" tabindex="-1"></a><span class="in"> fmt_percent(columns = Value, decimals = 2)</span></span> 8037<span id="cb21-1257"><a href="#cb21-1257" aria-hidden="true" tabindex="-1"></a></span> 8038<span id="cb21-1258"><a href="#cb21-1258" aria-hidden="true" tabindex="-1"></a><span class="in"># show gt tables of confusion matrix and metrics</span></span> 8039<span id="cb21-1259"><a href="#cb21-1259" aria-hidden="true" tabindex="-1"></a><span class="in">confusion_gt</span></span> 8040<span id="cb21-1260"><a href="#cb21-1260" aria-hidden="true" tabindex="-1"></a><span class="in">metrics_gt</span></span> 8041<span id="cb21-1261"><a href="#cb21-1261" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span> 8042<span id="cb21-1262"><a href="#cb21-1262" aria-hidden="true" tabindex="-1"></a></span> 8043<span id="cb21-1263"><a href="#cb21-1263" aria-hidden="true" tabindex="-1"></a>Overall, the model has a prediction accuracy rate of about 92%. However, this is mostly due to the model's high level of ability at ruling out distress evidenced by its 99% specificity. Accuracy suffers substantially at predicting block groups that are in distress, where it is only ~28% accurate. This suggests that the model is conservative and is extremely cautious about labeling a block group as distressed unless strong evidence is present. The pattern of false negatives is in line with the fact that distressed areas are a bit rare in the data and the model prioritizes minimizing false positives over catching all distress cases.</span> 8044<span id="cb21-1264"><a href="#cb21-1264" aria-hidden="true" tabindex="-1"></a></span> 8045<span id="cb21-1265"><a href="#cb21-1265" aria-hidden="true" tabindex="-1"></a><span class="fu">### Random Effects (Evaluate Areas with Unexpectedly High Risk of Distress)</span></span> 8046<span id="cb21-1266"><a href="#cb21-1266" aria-hidden="true" tabindex="-1"></a></span> 8047<span id="cb21-1267"><a href="#cb21-1267" aria-hidden="true" tabindex="-1"></a><span class="in">```{r random_effects, message=T, warning=T}</span></span> 8048<span id="cb21-1268"><a href="#cb21-1268" aria-hidden="true" tabindex="-1"></a><span class="in">#| cache: false</span></span> 8049<span id="cb21-1269"><a href="#cb21-1269" aria-hidden="true" tabindex="-1"></a></span> 8050<span id="cb21-1270"><a href="#cb21-1270" aria-hidden="true" tabindex="-1"></a><span class="in"># get ordered unique GEOIDs</span></span> 8051<span id="cb21-1271"><a href="#cb21-1271" aria-hidden="true" tabindex="-1"></a><span class="in">geoid_levels <- unique(model_glmm$frame$GEOID)</span></span> 8052<span id="cb21-1272"><a href="#cb21-1272" aria-hidden="true" tabindex="-1"></a></span> 8053<span id="cb21-1273"><a href="#cb21-1273" aria-hidden="true" tabindex="-1"></a><span class="in"># extract random effects (each unique GEOID and its random intercept)</span></span> 8054<span id="cb21-1274"><a href="#cb21-1274" aria-hidden="true" tabindex="-1"></a><span class="in">ranef_data <-</span></span> 8055<span id="cb21-1275"><a href="#cb21-1275" aria-hidden="true" tabindex="-1"></a><span class="in"> ranef(model_glmm)$cond$GEOID %>%</span></span> 8056<span id="cb21-1276"><a href="#cb21-1276" aria-hidden="true" tabindex="-1"></a><span class="in"> as_tibble() %>%</span></span> 8057<span id="cb21-1277"><a href="#cb21-1277" aria-hidden="true" tabindex="-1"></a><span class="in"> mutate(GEOID = geoid_levels) %>%</span></span> 8058<span id="cb21-1278"><a href="#cb21-1278" aria-hidden="true" tabindex="-1"></a><span class="in"> rename(random_intercept = `(Intercept)`) %>%</span></span> 8059<span id="cb21-1279"><a href="#cb21-1279" aria-hidden="true" tabindex="-1"></a><span class="in"> relocate(GEOID, .before = random_intercept)</span></span> 8060<span id="cb21-1280"><a href="#cb21-1280" aria-hidden="true" tabindex="-1"></a></span> 8061<span id="cb21-1281"><a href="#cb21-1281" aria-hidden="true" tabindex="-1"></a><span class="in"># join random effects to block groups</span></span> 8062<span id="cb21-1282"><a href="#cb21-1282" aria-hidden="true" tabindex="-1"></a><span class="in">st_bgs.ranef <-</span></span> 8063<span id="cb21-1283"><a href="#cb21-1283" aria-hidden="true" tabindex="-1"></a><span class="in"> st_bgs_all %>%</span></span> 8064<span id="cb21-1284"><a href="#cb21-1284" aria-hidden="true" tabindex="-1"></a><span class="in"> distinct(GEOID, .keep_all = TRUE) %>%</span></span> 8065<span id="cb21-1285"><a href="#cb21-1285" aria-hidden="true" tabindex="-1"></a><span class="in"> left_join(ranef_data, by = 'GEOID')</span></span> 8066<span id="cb21-1286"><a href="#cb21-1286" aria-hidden="true" tabindex="-1"></a><span class="in">
8066 #mutate(region = sapply(county, get_region)) %>%</span></span> 8067<span id="cb21-1287"><a href="#cb21-1287" aria-hidden="true" tabindex="-1"></a><span class="in"> #relocate(region, .after = county)</span></span> 8068<span id="cb21-1288"><a href="#cb21-1288" aria-hidden="true" tabindex="-1"></a></span> 8069<span id="cb21-1289"><a href="#cb21-1289" aria-hidden="true" tabindex="-1"></a><span class="in"># make map of random effects to see which aras have a higher unexplained risk of</span></span> 8070<span id="cb21-1290"><a href="#cb21-1290" aria-hidden="true" tabindex="-1"></a><span class="in"># being distressed due to reasons not measured in the model</span></span> 8071<span id="cb21-1291"><a href="#cb21-1291" aria-hidden="true" tabindex="-1"></a><span class="in">ranef_map <-</span></span> 8072<span id="cb21-1292"><a href="#cb21-1292" aria-hidden="true" tabindex="-1"></a><span class="in"> ggplot() +</span></span> 8073<span id="cb21-1293"><a href="#cb21-1293" aria-hidden="true" tabindex="-1"></a><span class="in"> geom_sf(data = st_bgs.ranef, aes(fill = random_intercept), color = NA) +</span></span> 8074<span id="cb21-1294"><a href="#cb21-1294" aria-hidden="true" tabindex="-1"></a><span class="in"> geom_sf(data = st_full_study_area, color= 'grey80', fill = NA, linewidth = 0.1) +</span></span> 8075<span id="cb21-1295"><a href="#cb21-1295" aria-hidden="true" tabindex="-1"></a><span class="in"> geom_sf_text(data = cities, aes(label = NAME), </span></span> 8076<span id="cb21-1296"><a href="#cb21-1296" aria-hidden="true" tabindex="-1"></a><span class="in"> angle = 22.5, size = 3.75) +</span></span> 8077<span id="cb21-1297"><a href="#cb21-1297" aria-hidden="true" tabindex="-1"></a><span class="in"> geom_sf_text(data = civil, aes(label = NAME), </span></span> 8078<span id="cb21-1298"><a href="#cb21-1298" aria-hidden="true" tabindex="-1"></a><span class="in"> angle = -22.5, size = 3) +</span></span> 8079<span id="cb21-1299"><a href="#cb21-1299" aria-hidden="true" tabindex="-1"></a><span class="in"> scale_fill_gradientn(</span></span> 8080<span id="cb21-1300"><a href="#cb21-1300" aria-hidden="true" tabindex="-1"></a><span class="in"> colors = c('darkblue', 'white', 'red'),</span></span> 8081<span id="cb21-1301"><a href="#cb21-1301" aria-hidden="true" tabindex="-1"></a><span class="in"> values = rescale(c(min(st_bgs.ranef$random_intercept, na.rm = T), -3, </span></span> 8082<span id="cb21-1302"><a href="#cb21-1302" aria-hidden="true" tabindex="-1"></a><span class="in"> 3, max(st_bgs.ranef$random_intercept, na.rm = T))),</span></span> 8083<span id="cb21-1303"><a href="#cb21-1303" aria-hidden="true" tabindex="-1"></a><span class="in"> limits = c(min(st_bgs.ranef$random_intercept),</span></span> 8084<span id="cb21-1304"><a href="#cb21-1304" aria-hidden="true" tabindex="-1"></a><span class="in"> max(st_bgs.ranef$random_intercept)),</span></span> 8085<span id="cb21-1305"><a href="#cb21-1305" aria-hidden="true" tabindex="-1"></a><span class="in"> oob = squish,</span></span> 8086<span id="cb21-1306"><a href="#cb21-1306" aria-hidden="true" tabindex="-1"></a><span class="in"> name = 'Random Intercept\n(Log-Odds)',</span></span> 8087<span id="cb21-1307"><a href="#cb21-1307" aria-hidden="true" tabindex="-1"></a><span class="in"> na.value = 'white'</span></span> 8088<span id="cb21-1308"><a href="#cb21-1308" aria-hidden="true" tabindex="-1"></a><span class="in"> ) +</span></span> 8089<span id="cb21-1309"><a href="#cb21-1309" aria-hidden="true" tabindex="-1"></a><span class="in"> labs(</span></span> 8090<span id="cb21-1310"><a href="#cb21-1310" aria-hidden="true" tabindex="-1"></a><span class="in"> title = 'Unexplained Distress Risk by Block Group',</span></span> 8091<span id="cb21-1311"><a href="#cb21-1311" aria-hidden="true" tabindex="-1"></a><span class="in"> subtitle = 'Baseline Distress Risk After Controlling for Predictors'</span></span> 8092<span id="cb21-1312"><a href="#cb21-1312" aria-hidden="true" tabindex="-1"></a><span class="in"> ) +</span></span> 8093<span id="cb21-1313"><a href="#cb21-1313" aria-hidden="true" tabindex="-1"></a><span class="in"> theme_map() +</span></span> 8094<span id="cb21-1314"><a href="#cb21-1314" aria-hidden="true" tabindex="-1"></a><span class="in"> theme(</span></span> 8095<span id="cb21-1315"><a href="#cb21-1315" aria-hidden="true" tabindex="-1"></a><span class="in"> plot.margin = margin(10, 10, 10, 10),</span></span> 8096<span id="cb21-1316"><a href="#cb21-1316" aria-hidden="true" tabindex="-1"></a><span class="in"> legend.position = 'bottom'</span></span> 8097<span id="cb21-1317"><a href="#cb21-1317" aria-hidden="true" tabindex="-1"></a><span class="in"> ) +</span></span> 8098<span id="cb21-1318"><a href="#cb21-1318" aria-hidden="true" tabindex="-1"></a><span class="in"> guides(fill = guide_legend(nrow = 1)) +</span></span> 8099<span id="cb21-1319"><a href="#cb21-1319" aria-hidden="true" tabindex="-1"></a><span class="in">
8099 coord_sf(expand = FALSE)</span></span> 8100<span id="cb21-1320"><a href="#cb21-1320" aria-hidden="true" tabindex="-1"></a></span> 8101<span id="cb21-1321"><a href="#cb21-1321" aria-hidden="true" tabindex="-1"></a><span class="in">ranef_map</span></span> 8102<span id="cb21-1322"><a href="#cb21-1322" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span> 8103<span id="cb21-1323"><a href="#cb21-1323" aria-hidden="true" tabindex="-1"></a></span> 8104<span id="cb21-1324"><a href="#cb21-1324" aria-hidden="true" tabindex="-1"></a>A variance of 8.415 and standard deviation of 2.901 in the random effects show a high level of between-group variability, which suggests that unobserved block group-level factors play a large role in predicting distress in this model. It was thus justified to include the random intercepts to account for this heterogeneity.</span> 8105<span id="cb21-1325"><a href="#cb21-1325" aria-hidden="true" tabindex="-1"></a></span> 8106<span id="cb21-1326"><a href="#cb21-1326" aria-hidden="true" tabindex="-1"></a>The map highlights spatial variation in unexplained distress risk across block groups. Several areas of the Southern Tier exhibit significantly higher baseline distress risk (in red) even after controlling for socioeconomic, demographic, and built environment predictors. This suggests the presence of unmeasured local factors contributing to persistent distress. Conversely, pockets of lower-than-expected risk (in blue) indicate potential resilience factors that buffer against socioeconomic vulnerabilities. These spatial patterns warrant further investigation to identify contextual drivers of distress beyond those captured in the current model, as well as methods and policy implementations that could potentially offset the effects of poverty, unemployment rate, and other socioeconomic conditions.</span> 8107<span id="cb21-1327"><a href="#cb21-1327" aria-hidden="true" tabindex="-1"></a></span> 8108<span id="cb21-1328"><a href="#cb21-1328" aria-hidden="true" tabindex="-1"></a>Most of the larger cities in the Southern Tier (Binghamton, Jamestown, Ithaca, and Elmira) and other urban centers (such as Oneonta and Olean) express a particular "urban divide" among its census tracts, where pockets of neighborhoods have lower-than-expected distress risk and others have higher-than-expected distress risk. There is also an urban/suburban divide between more stable suburban block groups and socioeconomically vulnerable urban block groups. A vast majority of the higher-risk block groups were urban in nature. A handfull of rural areas in each county exhibited higher-than-expected distress risk, including the entirety of the Allegany Indian Reservation centered around the small but vulnerable urban core of Salamanca. Still, urban cores were significantly more likely to be distressed than rural areas.</span> 8109<span id="cb21-1329"><a href="#cb21-1329" aria-hidden="true" tabindex="-1"></a></span> 8110<span id="cb21-1330"><a href="#cb21-1330" aria-hidden="true" tabindex="-1"></a><span class="fu"># Discussion and Conclusions</span></span> 8111<span id="cb21-1331"><a href="#cb21-1331" aria-hidden="true" tabindex="-1"></a></span> 8112<span id="cb21-1332"><a href="#cb21-1332" aria-hidden="true" tabindex="-1"></a>The model revealed several statistically significant predictors of distress that align with and extend existing literature on rural vulnerability. Notably, the percentage of individuals not working in the past year emerged as a robust predictor, with both a block groupâs own unemployment rate and the spatially lagged rate in neighboring areas being associated with significantly increased odds of having distressed status. This finding underscores the importance of accounting for spatial spillover effects; communities do not exist in isolation, and economic hardship in one area often correlates with, and potentially contributes to, hardship in adjacent areas. The spatial lag term specifically highlights the diffusive nature of socioeconomic stress, validating recent methodological recommendations to include spatial structure in GLMMs (Dormann et al., 2007;
8112 Zuur et al., 2009).</span> 8113<span id="cb21-1333"><a href="#cb21-1333" aria-hidden="true" tabindex="-1"></a></span> 8114<span id="cb21-1334"><a href="#cb21-1334" aria-hidden="true" tabindex="-1"></a>In addition, the model identified population density, divorce rate, and urban development intensity as significant positive predictors of distress. These findings complicate conventional narratives that associate urbanization and density with economic opportunity. In the context of the Southern Tier, where many small cities and formerly industrial urban areas have suffered from long-term economic decline, these variables may reflect concentrated disadvantage, aging population and/or infrastructure, and systemic disinvestment. Conversely, higher average rent was associated with slightly lower odds of distress, potentially serving as a proxy for neighborhood stability or housing demand, although the effect was relatively modest.</span> 8115<span id="cb21-1335"><a href="#cb21-1335" aria-hidden="true" tabindex="-1"></a></span> 8116<span id="cb21-1336"><a href="#cb21-1336" aria-hidden="true" tabindex="-1"></a>The inclusion of year-group fixed effects also confirmed a significant temporal shift: block groups in the 2019â2023 period were far more likely to be classified as distressed, even after accounting for structural and spatial factors. This supports evidence that distress risk has especially been increasing in recent years, likely due to COVID-19 disruptions, continued population decline, and limited regional economic diversification.</span> 8117<span id="cb21-1337"><a href="#cb21-1337" aria-hidden="true" tabindex="-1"></a></span> 8118<span id="cb21-1338"><a href="#cb21-1338" aria-hidden="true" tabindex="-1"></a>Despite the strong predictive performance of the model, which achieved an accuracy rate of 92% and a specificity above 99%, some limitations remain. Sensitivity was lower at ~28%, suggesting that an overwhelming majority of distressed block groups may remain undetected by the model. These false negatives could reflect areas where unmeasured protective factors or data limitations obscure underlying distress. Additionally, spatial autocorrelation remains present in the residuals, indicating that the model may not fully capture all spatial dependencies. Incorporating spatially structured random effects or moving toward spatial error models may be necessary to account for latent clustering in future research.</span> 8119<span id="cb21-1339"><a href="#cb21-1339" aria-hidden="true" tabindex="-1"></a></span> 8120<span id="cb21-1340"><a href="#cb21-1340" aria-hidden="true" tabindex="-1"></a>Moreover, the method of interpolating variables across changing block group boundaries introduces further limitations. This study used population-weighted areal interpolation through centroid assignment which may produce bias in high-variance areas. As Hallisey et al. (2017) note, centroid-based interpolation can contribute to large absolute errors in count data. Future work could explore hybrid approaches, including combined population and areal weighting or dasymetric interpolation using ancillary data like land cover, to enhance spatial accuracy.</span> 8121<span id="cb21-1341"><a href="#cb21-1341" aria-hidden="true" tabindex="-1"></a></span> 8122<span id="cb21-1342"><a href="#cb21-1342" aria-hidden="true" tabindex="-1"></a>By applying the ARCâs Distressed Areas Classification System at the census block group level, this study tested its efficacy in identifying localized socioeconomic vulnerability. While the ARCâs original formulation is effective for broader regional comparisons, it may miss important within-county variation in distress patterns, particularly in geographically and demographically heterogeneous areas like the Southern Tier. The results suggest that the ARC's thresholds alone (67% of national median family income and 150% of the national poverty rate) do not identify many of the most vulnerable communities. The random intercepts in the GLMM, which are interpreted as "unexplained" risk after accounting for observed predictors, revealed significant residual variation across block groups. In other words, certain block groups experienced higher or lower distress risk than would be predicted solely from ARC-defined economic indicators and the modelâs covariates.</span> 8123<span id="cb21-1343"><a href="#cb21-1343" aria-hidden="true" tabindex="-1"></a></span> 8124<span id="cb21-1344"><a href="#cb21-1344" aria-hidden="true" tabindex="-1"></a>Mapping these random intercepts revealed both inter-county variation and stark intra-urban disparities. While rural pockets in counties like Allegany, Delaware, and Schuyler exhibited elevated unexplained distress, several cities (including Binghamton, Ithaca, Jamestown, Elmira, and Oneonta) showed a pronounced internal divide. In these cities, some block groups had much higher-than-expected baseline distress risk, while adjacent ones had negative random effects, indicating lower-than-expected risk. This âurban divideâ suggests that distress may concentrate in historically segregated or economically isolated neighborhoods within cities, reinfor
8124cing the idea that urban centers in rural regions are not uniformly distressed but fractured by block-level variation in resilience, opportunity, and historical disinvestment. These observations raise important questions about the adequacy of county-averaged or citywide metrics for targeting interventions. While the ARCâs framework provides a valuable starting point, the findings here emphasize the value of block group-level analysis to surface hidden pockets of vulnerability or resilience that are invisible at coarser scales.</span> 8125<span id="cb21-1345"><a href="#cb21-1345" aria-hidden="true" tabindex="-1"></a></span> 8126<span id="cb21-1346"><a href="#cb21-1346" aria-hidden="true" tabindex="-1"></a>To build on these insights, future analyses should explore additional explanatory variables. Potential protective factors may include social capital (e.g., volunteerism, civic engagement), access to green space, proximity to community services, or the presence of mutual aid networks. Structural variables such as redlining history, credit access, school quality, or broadband availability may also help explain residual variation. For instance, changes in volunteerism over time, particularly during crises, offer natural experiments that could be exploited to assess causal effects on community vulnerability. Studies like Makridis and Wu (2021) have demonstrated that counties with greater civic engagement and volunteerism had stronger economic resilience during the COVID-19 pandemic, suggesting shifts in social capital could be causally linked to socioeconomic outcomes. Additionally, the sudden expansion of mutual aid networks during the pandemic provides a quasi-experimental setting to evaluate whether localized increases in social support structures reduced vulnerability. One such study by Xu and Zhang (2025) utilized structural equation modeling to analyze data from participants in mutual aid groups in China during the pandemic. The research found that participation in mutual aid significantly enhanced individuals' subjective well-being. This effect was mediated through increased access to material resources and the expansion of social networks, which in turn boosted self-esteem and self-efficacy. The study highlights the mechanisms by which mutual aid can reduce vulnerability and promote resilience in times of crisis.</span> 8127<span id="cb21-1347"><a href="#cb21-1347" aria-hidden="true" tabindex="-1"></a></span> 8128<span id="cb21-1348"><a href="#cb21-1348" aria-hidden="true" tabindex="-1"></a>Integrating dynamic measuresâsuch as industry closures, pandemic impacts, or population churnâcould further improve temporal responsiveness and causal interpretation. For example, differences-in-differences designs exploiting the staggered rollout of broadband access across rural areas (Bertschek et al., 2016) could be adapted to assess how changes in structural resources affect economic distress trajectories. Methodologically, sensitivity could be improved through more flexible classification thresholds, bootstrapped uncertainty intervals, or temporal interaction terms. Moving beyond cross-sectional associations to leverage these natural variations would offer stronger evidence on causal pathways and actionable intervention points.</span> 8129<span id="cb21-1349"><a href="#cb21-1349" aria-hidden="true" tabindex="-1"></a></span> 8130<span id="cb21-1350"><a href="#cb21-1350" aria-hidden="true" tabindex="-1"></a>This study contributes to a growing body of research calling for more spatially nuanced approaches to understanding and addressing rural economic distress. By integrating predictive modeling with the ARC classification framework, it underscores both the value and the limitations of existing policy tools and demonstrates the power of glmmTMB-based modeling for applied regional analysis. The inclusion of spatial random effects and the visual analysis of residuals provide a roadmap for targeting interventions more precisely, improving both the scientific understanding and the policy relevance of distress classification systems.</span> 8131<span id="cb21-1351"><a href="#cb21-1351" aria-hidden="true" tabindex="-1"></a></span> 8132<span id="cb21-1352"><a href="#cb21-1352" aria-hidden="true" tabindex="-1"></a><span class="in">```{=latex}</span></span> 8133<span id="cb21-1353"><a href="#cb21-1353" aria-hidden="true" tabindex="-1"></a><span class="in">\newpage</span></span> 8134<span id="cb21-1354"><a href="#cb21-1354" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span> 8135<span id="cb21-1355"><a href="#cb21-1355" aria-hidden="true" tabindex="-1"></a></span> 8136<span id="cb21-1356"><a href="#cb21-1356" aria-hidden="true" tabindex="-1"></a><span class="fu"># References</span></span> 8137<span id="cb21-1357"><a href="#cb21-1357" aria-hidden="true" tabindex="-1"></a></span> 8138<span id="cb21-1358"><a href="#cb21-1358" aria-hidden="true" tabindex="-1"></a><span class="fu">## Works Cited</span></span> 8139<span id="cb21-1359"><a href="#cb21-1359" aria-hidden="true" tabindex="-1"></a></span> 8140<span id="cb21-1360"><a href="#cb21-1360" aria-hidden="true" tabindex="-1"></a><span class="ss">1. </span>Appalachian Regional Commission. (2023). County Economic Status and Distressed Areas by State, FY 2024. <span class="ot"><https://www.arc.gov/about-the-appalachian-region/county-economic-status-and-distressed-areas-by-state-fy-2024></span></span> 8141<span id="cb21-1361"><a href="#cb21-1361" aria-hidden="true" tabindex="-1"></a></span> 8142<span id="cb21-1362"><a href="#cb21-1362" aria-hidden="true" tabindex="-1"></a><span class="ss">2. </span>Appalachian Regional Commission. (2024). Distressed Areas Classification System. <span class="ot"><https://www.arc.gov/distressed-areas-classification-system/></span></span> 8143<span id="cb21-1363"><a href="#cb21-1363" aria-hidden="true" tabindex="-1"></a></span> 8144<span id="cb21-1364"><a href="#cb21-1364" aria-hidden="true" tabindex="-1"></a><span class="ss">3. </span>
8144Barrett, M., Aslebagh, S., Vuong, V., et al. (2023). Spatially resolved air pollution models identify disparities in exposure by socioeconomic status. *European Respiratory Journal*, *62*(67). <span class="ot"><https://doi.org/10.1183/13993003.congress-2023.PA1608></span></span> 8145<span id="cb21-1365"><a href="#cb21-1365" aria-hidden="true" tabindex="-1"></a></span> 8146<span id="cb21-1366"><a href="#cb21-1366" aria-hidden="true" tabindex="-1"></a><span class="ss">4. </span>Bertschek, I., Briglauer, W., Hüschelrath, K., Kauf, B., & Niebel, T. (2016). The Economic Impacts of Broadband Internet: A Survey. *Review of Network Economics*, *14*(4), 201â227. <span class="ot"><https://doi.org/10.1515/rne-2016-0032></span></span> 8147<span id="cb21-1367"><a href="#cb21-1367" aria-hidden="true" tabindex="-1"></a></span> 8148<span id="cb21-1368"><a href="#cb21-1368" aria-hidden="true" tabindex="-1"></a><span class="ss">5. </span>Bivand, R. S., & Piras, G. (2015). Comparing implementations of estimation methods for spatial econometrics. Journal of Statistical Software, *63*(18), 1â36. <span class="ot"><https://doi.org/10.18637/jss.v063.i18></span></span> 8149<span id="cb21-1369"><a href="#cb21-1369" aria-hidden="true" tabindex="-1"></a></span> 8150<span id="cb21-1370"><a href="#cb21-1370" aria-hidden="true" tabindex="-1"></a><span class="ss">6. </span>Brooks M.E., Kristensen K., van Benthem K.J., et al. (2017). glmmTMB Balances Speed and Flexibility Among Packages for Zero-inflated Generalized Linear Mixed Modeling. *The R Journal*, *9*(2), 378â400. <span class="ot"><https://doi.org/doi:10.32614/RJ-2017-066></span>.</span> 8151<span id="cb21-1371"><a href="#cb21-1371" aria-hidden="true" tabindex="-1"></a></span> 8152<span id="cb21-1372"><a href="#cb21-1372" aria-hidden="true" tabindex="-1"></a><span class="ss">7. </span>Dormann, C. F., McPherson, J. M., Araújo, M. B., et al. (2007). Methods to account for spatial autocorrelation in the analysis of species distributional data: A review. *Ecography*, *30*(5), 609â628. <span class="ot"><https://doi.org/10.1111/j.2007.0906-7590.05171.x></span></span> 8153<span id="cb21-1373"><a href="#cb21-1373" aria-hidden="true" tabindex="-1"></a></span> 8154<span id="cb21-1374"><a href="#cb21-1374" aria-hidden="true" tabindex="-1"></a><span class="ss">8. </span>Ellis, G.F., Burk, D., Roberts, F. (2025). ipumsr: An R Interface for Downloading, Reading, and Handling IPUMS Data. R package version 0.8.2, <span class="ot"><https://github.com/ipums/ipumsr></span>, <span class="ot"><https://www.ipums.org></span>, <span class="ot"><https://tech.popdata.org/ipumsr/></span>. </span> 8155<span id="cb21-1375"><a href="#cb21-1375" aria-hidden="true" tabindex="-1"></a></span> 8156<span id="cb21-1376"><a href="#cb21-1376" aria-hidden="true" tabindex="-1"></a><span class="ss">9. </span>Flowerdew, R., & Green, M. (1992). Developments in areal interpolation methods and GIS. The Annals of Regional Science, 26(1), 67â78. <span class="ot"><https://doi.org/10.1007/BF01581870></span></span> 8157<span id="cb21-1377"><a href="#cb21-1377" aria-hidden="true" tabindex="-1"></a></span> 8158<span id="cb21-1378"><a href="#cb21-1378" aria-hidden="true" tabindex="-1"></a><span class="ss">10. </span>Galili, T., O'Callaghan, A., Sidi, J., & Sievert, C. (2017). heatmaply: an R package for creating interactive cluster heatmaps for online publishing. *Bioinformatics*, *34*(9), 1600-1602. <span class="ot"><https://doi.org/10.1093/bioinformatics/btx657></span></span> 8159<span id="cb21-1379"><a href="#cb21-1379" aria-hidden="true" tabindex="-1"></a></span> 8160<span id="cb21-1380"><a href="#cb21-1380" aria-hidden="true" tabindex="-1"></a><span class="ss">11. </span>Gelman, A. & Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press. <span class="ot"><https://doi.org/10.1017/CBO9780511790942></span></span> 8161<span id="cb21-1381"><a href="#cb21-1381" aria-hidden="true" tabindex="-1"></a></span> 8162<span id="cb21-1382"><a href="#cb21-1382" aria-hidden="true" tabindex="-1"></a><span class="ss">12. </span>Goodchild, M. F., & Lam, N. S. (1980). Areal interpolation: A variant of the traditional spatial problem. *Geo-Processing*, *1*(3), 297-312. <span class="ot"><
8162https://www.researchgate.net/publication/239654534_Areal_Interpolation_A_Variant_of_the_Traditional_Spatial_Problem></span></span> 8163<span id="cb21-1383"><a href="#cb21-1383" aria-hidden="true" tabindex="-1"></a></span> 8164<span id="cb21-1384"><a href="#cb21-1384" aria-hidden="true" tabindex="-1"></a><span class="ss">13. </span>Gregory, I. N. (2002). The accuracy of areal interpolation techniques: standardising the crosswalk. *Computers, Environment and Urban Systems*, *26*(3), 293-314. <span class="ot"><https://www.researchgate.net/publication/223083878_The_accuracy_of_areal_interpolation_techniques_Standardising_19th_and_20th_century_census_data_to_allow_long-term_comparisons></span></span> 8165<span id="cb21-1385"><a href="#cb21-1385" aria-hidden="true" tabindex="-1"></a></span> 8166<span id="cb21-1386"><a href="#cb21-1386" aria-hidden="true" tabindex="-1"></a><span class="ss">14. </span>Hallisey, E., Tai, E., Berens, A., et al. (2017). Transforming geographic scale: a comparison of combined population and areal weighting to other interpoaltion methods. *International Journal of Health Geographics*, *16*(29). <span class="ot"><https://doi.org/10.1186/s12942-017-0102-z></span></span> 8167<span id="cb21-1387"><a href="#cb21-1387" aria-hidden="true" tabindex="-1"></a></span> 8168<span id="cb21-1388"><a href="#cb21-1388" aria-hidden="true" tabindex="-1"></a><span class="ss">15. </span>Hartig, F. (2024). DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models. R package version 0.4.7, <span class="ot"><https://github.com/florianhartig/dharma></span>.</span> 8169<span id="cb21-1389"><a href="#cb21-1389" aria-hidden="true" tabindex="-1"></a></span> 8170<span id="cb21-1390"><a href="#cb21-1390" aria-hidden="true" tabindex="-1"></a><span class="ss">16. </span>Hijmans, R. (2025). terra: Spatial Data Analysis. R package version 1.8-6. <span class="ot"><https://github.com/rspatial/terra></span></span> 8171<span id="cb21-1391"><a href="#cb21-1391" aria-hidden="true" tabindex="-1"></a></span> 8172<span id="cb21-1392"><a href="#cb21-1392" aria-hidden="true" tabindex="-1"></a><span class="ss">17. </span>Johnson, K. M., & Lichter, D. T. (2019). Rural depopulation: Growth and decline processes over the past century. Rural Sociology, 84(1), 3â27. <span class="ot"><https://doi.org/10.1111/ruso.12266></span></span> 8173<span id="cb21-1393"><a href="#cb21-1393" aria-hidden="true" tabindex="-1"></a></span> 8174<span id="cb21-1394"><a href="#cb21-1394" aria-hidden="true" tabindex="-1"></a><span class="ss">18. </span>Jokela, M., Batty, G. D., Vahtera, J., Elovainio, M., & Kivimäki, M. (2013).</span> 8175<span id="cb21-1395"><a href="#cb21-1395" aria-hidden="true" tabindex="-1"></a>Socioeconomic inequalities in common mental disorders and psychotherapy treatment in the UK between 1991 and 2009. The British Journal of Psychiatry, 202(2), 115â120. <span class="ot"><https://doi.org/10.1192/bjp.bp.111.098863></span></span> 8176<span id="cb21-1396"><a href="#cb21-1396" aria-hidden="true" tabindex="-1"></a></span> 8177<span id="cb21-1397"><a href="#cb21-1397" aria-hidden="true" tabindex="-1"></a><span class="ss">19. </span>K C, S., Gyawali, B. R., Lucas, S., Antonious, G. F., Chiluwal, A., & Zourarakis, D. (2024). Assessing Land-Cover Change Trends, Patterns, and Transitions in Coalfield Counties of Eastern Kentucky, USA. *Land*, *13*(9), 1541. <span class="ot"><https://doi.org/10.3390/land13091541></span></span> 8178<span id="cb21-1398"><a href="#cb21-1398" aria-hidden="true" tabindex="-1"></a></span> 8179<span id="cb21-1399"><a href="#cb21-1399" aria-hidden="true" tabindex="-1"></a><span class="ss">20. </span>Ludke, R. L., Obermiller, P. J., & Rademacher, E. W. (2012). Demographic Change in Appalachia: A Tentative Analysis. *Journal of Appalachian Studies*, *18*(1/2), 48â92. <span class="ot"><http://www.jstor.org/stable/23337708></span></span> 8180<span id="cb21-1400"><a href="#cb21-1400" aria-hidden="true" tabindex="-1"></a></span> 8181<span id="cb21-1401"><a href="#cb21-1401" aria-hidden="true" tabindex="-1"></a><span class="ss">21. </span>Makridis, C. A. & Wu, C. (2021). How social capital helps communities weather the COVID-19 pandemic. *PLOS ONE*, *16*(1). <span class="ot"><https://doi.org/10.1371/journal.pone.0245135></span></span> 8182<span id="cb21-1402"><a href="#cb21-1402" aria-hidden="true" tabindex="-1"></a></span> 8183<span id="cb21-1403"><a href="#cb21-1403" aria-hidden="true" tabindex="-1"></a><span class="ss">22. </span>Manson, S., Schroeder, J., Van Riper, D., et al. (2024). IPUMS National Historical Geographic Information System: Version 19.0 <span class="co">[</span><span class="ot">dataset</span><span class="co">]</span>. Minneapolis, MN: IPUMS. <span class="ot"><http://doi.org/10.18128/D050.V19.0></span>.</span> 8184<span id="cb21-1404"><a href="#cb21-1404" aria-hidden="true" tabindex="-1"></a></span> 8185<span id="cb21-1405"><a href="#cb21-1405" aria-hidden="true" tabindex="-1"></a><span class="ss">23. </span>McMahon, E.J. (2024). Eight in 10 New York towns and cities have lost population since 2020. *Empire Center*. <span class="ot"><https://www.empirecenter.org/publications/eight-in-10-new-york-towns-and-cities-have-lost-population-since-2020></span></span> 8186<span id="cb21-1406"><a href="#cb21-1406" aria-hidden="true" tabindex="-1"></a></span> 8187<span id="cb21-1407"><a href="#cb21-1407" aria-hidden="true" tabindex="-1"></a><span class="ss">24. </span>Partridge, M. D., Rickman, D. S., Ali, K., & Olfert, M. R. (2008).</span> 8188<span id="cb21-1408"><a href="#cb21-1408" aria-hidden="true" tabindex="-1"></a>Lost in space: Population growth in the American hinterlands and small cities.</span> 8189<span id="cb21-1409"><a href="#cb21-1409" aria-hidden="true" tabindex="-1"></a>Journal of Economic Geography, 8(6), 727â757.</span> 8190<span id="cb21-1410"><a href="#cb21-1410" aria-hidden="true" tabindex="-1"></a><span class="ot"><https://doi.org/10.1093/jeg/lbn038></span></span> 8191<span id="cb21-1411"><a href="#cb21-1411" aria-hidden="true" tabindex="-1"></a></span> 8192<span id="cb21-1412"><a href="#cb21-1412" aria-hidden="true" tabindex="-1"></a><span class="ss">25. </span>Thiede, B. C., Lichter, D. T., & Slack, T. (2018). Working, but poor: The good life in rural America? Journal of Rural Studies, 59, 183â193. <span class="ot"><https://doi.org/10.1016/j.jrurstud.2016.02.007></span></span> 8193<span id="cb21-1413"><a href="#cb21-1413" aria-hidden="true" tabindex="-1"></a></span> 8194<span id="cb21-1414"><a href="#cb21-1414" aria-hidden="true" tabindex="-1"></a><span class="ss">26. </span>U.S. Geological Survey. (2024). Annual National Land Cover Database (NLCD) Collection 1 Science Products (2008â2023) <span class="co">[</span><span class="ot">Data set</span><span class="co">]</span>. Multi-Resolution Land Characteristics Consortium (<span class="co">[</span><span class="ot">MRLC</span><span class="co">](https://www.mrlc.gov/sites/default/files/docs/LSDS-2103%20Annual%20National%20Land%20Cover%20Database%20%28NLCD%29%20Collection%201%20Science%20Product%20User%20Guide%20-v1.0%202024_10_15.pdf)</span>).</span> 8195<span id="cb21-1415"><a href="#cb21-1415" aria-hidden="true" tabindex="-1"></a></span> 8196<span id="cb21-1416"><a href="#cb21-1416" aria-hidden="true" tabindex="-1"></a><span class="ss">27. </span>Walker, K. (2023). "
81967.3 Small area time-series analysis," in *Analyzing US Census Data: Methods, Maps, and Models in R*. CRC Press: Boca Raton, Florida, <span class="ot"><https://walker-data.com/census-r/spatial-analysis-with-us-census-data.html#small-area-time-series-analysis></span></span> 8197<span id="cb21-1417"><a href="#cb21-1417" aria-hidden="true" tabindex="-1"></a></span> 8198<span id="cb21-1418"><a href="#cb21-1418" aria-hidden="true" tabindex="-1"></a><span class="ss">28. </span>Walker, K. & Herman, M. (2024). tidycensus: Load US Census Boundary and Attribute Data as 'tidyverse' and 'sf'-Ready Data Frames. R package version 1.6.6. <span class="ot"><https://walker-data.com/tidycensus></span></span> 8199<span id="cb21-1419"><a href="#cb21-1419" aria-hidden="true" tabindex="-1"></a></span> 8200<span id="cb21-1420"><a href="#cb21-1420" aria-hidden="true" tabindex="-1"></a><span class="ss">29. </span>Xu, A., & Zhang, Y. (2025). The effect and mechanism of mutual aid on the subjective well-being of participants under the COVID-19 pandemic. BMC Psychology, 13, Article 48. https://doi.org/10.1186/s40359-025-02360-5</span> 8201<span id="cb21-1421"><a href="#cb21-1421" aria-hidden="true" tabindex="-1"></a></span> 8202<span id="cb21-1422"><a href="#cb21-1422" aria-hidden="true" tabindex="-1"></a><span class="ss">30. </span>Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A., & Smith, G. M. (2009). Mixed Effects Models and Extensions in Ecology with R. Springer Science & Business Media. <span class="ot"><https://doi.org/10.1007/978-0-387-87458-6></span></span> 8203<span id="cb21-1423"><a href="#cb21-1423" aria-hidden="true" tabindex="-1"></a></span> 8204<span id="cb21-1424"><a href="#cb21-1424" aria-hidden="true" tabindex="-1"></a><span class="in">```{=latex}</span></span> 8205<span id="cb21-1425"><a href="#cb21-1425" aria-hidden="true" tabindex="-1"></a><span class="in">\newpage</span></span> 8206<span id="cb21-1426"><a href="#cb21-1426" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span> 8207<span id="cb21-1427"><a href="#cb21-1427" aria-hidden="true" tabindex="-1"></a></span> 8208<span id="cb21-1428"><a href="#cb21-1428" aria-hidden="true" tabindex="-1"></a><span class="fu">## Data Sources and Methodology</span></span> 8209<span id="cb21-1429"><a href="#cb21-1429" aria-hidden="true" tabindex="-1"></a></span> 8210<span id="cb21-1430"><a href="#cb21-1430" aria-hidden="true" tabindex="-1"></a><span class="co">[</span><span class="ot">2009 U.S. Data</span><span class="co">]</span>{.underline}</span> 8211<span id="cb21-1431"><a href="#cb21-1431" aria-hidden="true" tabindex="-1"></a></span> 8212<span id="cb21-1432"><a href="#cb21-1432" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span>Total Population: <span class="ot"><https://api.census.gov/data/2009/acs/acs5?get=B02001_001E,NAME&for=us></span></span> 8213<span id="cb21-1433"><a href="#cb21-1433" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span>Median Family Income: U.S. Department of Housing and Urban Development. (19 March 2009). "Estimated Median Family Incomes for Fiscal Year 2009," PDR-2009-01. <span class="ot"><https://www.huduser.gov/portal/datasets/il/il09/Medians2009.pdf></span></span> 8214<span id="cb21-1434"><a href="#cb21-1434" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span>Poverty Rate: Bishaw, A. & Macartney, S. (September 2010). "Poverty: 2008 and 2009," American Community Survey Briefs, ACSBR/09-1. U.S. Census Bureau, Washington, D.C. <span class="ot"><https://www2.census.gov/library/publications/2010/acs/acsbr09-01.pdf></span></span> 8215<span id="cb21-1435"><a href="#cb21-1435" aria-hidden="true" tabindex="-1"></a></span> 8216<span id="cb21-1436"><a href="#cb21-1436" aria-hidden="true" tabindex="-1"></a><span class="co">[</span><span class="ot">2010 U.S. Data</span><span class="co">]</span>{.underline}</span> 8217<span id="cb21-1437"><a href="#cb21-1437" aria-hidden="true" tabindex="-1"></a></span> 8218<span id="cb21-1438"><a href="#cb21-1438" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span>Total Population: U.S. Census Bureau. (2010). 2010 Summary File 1 Tables <span class="co">[</span><span class="ot">All 50 States & DC</span><span class="co">]</span>. <span class="ot"><https://api.census.gov/data/2010/dec/sf1?></span></span> 8219<span id="cb21-1439"><a href="#cb21-1439" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span>Median Family Income: U.S. Department of Housing and Urban Development. (14 May 2010). "Estimated Median Family Incomes for Fiscal Year 2010," PDR-2010-01. <span class="ot"><https://www.huduser.gov/portal/datasets/il/il10/Medians2010.pdf></span></span> 8220<span id="cb21-1440"><a href="#cb21-1440" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span>Poverty Rate: Bishaw, A. (September 2012). "Poverty: 2010 and 2011," American Community Survey Briefs, ACSBR/11-01. U.S. Census Bureau, Washington, D.C. <span class="ot"><https://www2.census.gov/library/publications/2012/acs/acsbr11-01.pdf></span>, pg. 3.</span> 8221<span id="cb21-1441"><a href="#cb21-1441" aria-hidden="true" tabindex="-1"></a></span> 8222<span id="cb21-1442"><a href="#cb21-1442" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span>2009 & 2010 Per Capita Income and Haven't Worked Population: <span class="co">[</span><span class="ot">U.S. Bureau of Economic Analysis</span><span class="co">](https://apps.bea.gov/iTable/?reqid=70&step=30&isuri=1&major_area=0&area=xx&year=2018,2017,2016,2015,2014,2013,2012,2011,2010,2009,2008,2007,2006,2005,2004,2003,2002,2001,2000,1999,1998,1997,1996&tableid=21&category=421&area_type=0&year_end=-1&classification=non-industry&state=0&statistic=3&yearbegin=-1&unit_of_measure=levels)</span>.</span> 8223<span id="cb21-1443"><a href="#cb21-1443" aria-hidden="true" tabindex="-1"></a></span> 8224<span id="cb21-1444"><a href="#cb21-1444" aria-hidden="true" tabindex="-1"></a><span class="co">[</span><span class="ot">American Community Survey (ACS) Data</span><span class="co">]</span>{.underline}</span> 8225<span id="cb21-1445"><a href="#cb21-1445" aria-hidden="true" tabindex="-1"></a></span> 8226<span id="cb21-1446"><a href="#cb21-1446" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span>U.S. Census Bureau. (2009-2023). American Community Survey 5-Year Estimates: Comparison Profiles 5-Year. <span class="ot"><http://api.census.gov/data/2010/acs/acs5></span></span> 8227<span id="cb21-1447"><a href="#cb21-1447" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span>U.S. Census Bureau. (2010). "Selected Economic Characteristics." American Community Survey 5-Year Estimates Subject Tables, Table DP03. <span class="ot"><https://data.census.gov/table/ACSDP5YSPT2010.DP03></span></span> 8228<span id="cb21-1448"><a href="#cb21-1448" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span>U.S. Census Bureau. (2019). "Selected Economic Characteristics." American Community Survey 5-Year Estimates Subject Tables, Table DP03. <span class="ot"><https://data.census.gov/table/ACSDP5Y2019.DP03></span></span> 8229<span id="cb21-1449"><a href="#cb21-1449" aria-hidden="true" tabindex="-1"></a></span> 8230<span id="cb21-1450"><a href="#cb21-1450" aria-hidden="true" tabindex="-1"></a><span class="co">[</span><span class="ot">Land Cover Data</span><span class="co">]</span>{.underline}</span> 8231<span id="cb21-1451"><a href="#cb21-1451" aria-hidden="true" tabindex="-1"></a></span> 8232<span id="cb21-1452"><a href="#cb21-1452" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span>U.S. Geological Survey (USGS). (2008-2023). Annual National Land Cover Data Collection 1 Science Products: U.S. Geological Survey data releases. Data downloaded from: <span class="ot"><https://www.mrlc.gov/data?f[0]=category%3ALand%20Cover></span></span> 8233<span id="cb21-1453"><a href="#cb21-1453" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span>Land Cover Classifications from the <span class="co">[</span><span class="ot">Multi-Resolution Land Characteristics Consortium</span><span class="co">](https://www.mrlc.gov/data/type/land-cover)</span></span></code><button title="Copy to Clipboard" class="code-copy-button" data-in-quarto-modal><i class="bi"></i></button></pre></div> 8234</div></div></div></div></div> 8235</div> <!-- /content --> 8236 8237 8238 8239
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Line numbers count LF bytes from the start of the resource, as the search results do. Vendor segments are library code the classifier recognised; they are stored but not indexed. Bytes are shown as Latin1 characters, one per byte.