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Canadian Longitudinal Tract Database (CLTD)

This repository includes code to bridge Canadian historical census tract data (every five years from 1951 to 2021) to a common set of geographic boundaries (2021).

Census tracts are geographic boundaries delineated by Statistics Canada used for publishing aggregate census data. They are analogous to neighbourhoods and typically pertain to 4,000 to 7,000 residents.

Each census year, Statistics Canada re-draws (and often adds new) boundaries to account for shifting and growing populations.

This makes it difficult to examine how the demographics of neighbourhoods have changed over time (i.e. cannot always compare on a one-to-one basis).

As such, the goal of this project was to use areal interpolation methods to create a series of concordance tables indicating how boundaries from older years are spatially related to newer years. Specifically, these tables include a set of weights that indicate how to apportion data from a tract boundary in one year to boundaries in another year.

Our work began in 2016, with the creation of concordance tables from 1971 to 2016. The data and a manual describing this original work is available on Dataverse: http://dx.doi.org/10.5683/SP/EUG3DT. A paper detailing the methods was published in The Canadian Geographer.

@article{allentaylor2018,
title={A new tool for neighbourhood change research: The Canadian Longitudinal Census Tract Database, 1971--2016},
author={Allen, Jeff and Taylor, Zack},
journal={The Canadian Geographer/Le G{\'e}ographe canadien},
year={2018},
publisher={Wiley Online Library},
url={https://doi.org/10.1111/cag.12467}
}

We have since updated this to include recently digitized historical tract boundaries for 1951, 1956, 1961, and 1966 - as well as linked all tracts to 2021 tracts.

crosswalk_tables

A set of concordance tables (.csv) which link boundary identifiers between years using a set of apportionment weights.

examples

Example scripts that use these tables to apportion data linked to census tracts from a source year to a target year (e.g. from 1951 to 2021)

src

Contains the code used to generate and validate the crosswalk tables. This utilizes a combination of population weighting, area weighting, and dasymetric mapping approaches to minimize error when boundaries change over time. Most of this was conducted in PostGIS, some Python too.

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all
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})();
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try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '
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Canadian Longitudinal Tract Database (CLTD)

This repository includes code to bridge Canadian historical census tract data (every five years from 1951 to 2021) to a common set of geographic boundaries (2021).

Census tracts are geographic boundaries delineated by Statistics Canada used for publishing aggregate census data. They are analogous to neighbourhoods and typically pertain to 4,000 to 7,000 residents.

Each census year, Statistics Canada re-draws (and often adds new) boundaries to account for shifting and growing populations.

This makes it difficult to examine how the demographics of neighbourhoods have changed over time (i.e. cannot always compare on a one-to-one basis).

As such, the goal of this project was to use areal interpolation methods to create a series of concordance tables indicating how boundaries from older years are spatially related to newer years. Specifically, these tables include a set of weights that indicate how to apportion data from a tract boundary in one year to boundaries in another year.

Our work began in 2016, with the creation of concordance tables from 1971 to 2016. The data and a manual describing this original work is available on Dataverse: http://dx.doi.org/10.5683/SP/EUG3DT. A paper detailing the methods was published in The Canadian Geographer.

@article{allentaylor2018,
title={A new tool for neighbourhood change research: The Canadian Longitudinal Census Tract Database, 1971--2016},
author={Allen, Jeff and Taylor, Zack},
journal={The Canadian Geographer/Le G{\'e}ographe canadien},
year={2018},
publisher={Wiley Online Library},
url={https://doi.org/10.1111/cag.12467}
}

We have since updated this to include recently digitized historical tract boundaries for 1951, 1956, 1961, and 1966 - as well as linked all tracts to 2021 tracts.

crosswalk_tables

A set of concordance tables (.csv) which link boundary identifiers between years using a set of apportionment weights.

examples

Example scripts that use these tables to apportion data linked to census tracts from a source year to a target year (e.g. from 1951 to 2021)

src

Contains the code used to generate and validate the crosswalk tables. This utilizes a combination of population weighting, area weighting, and dasymetric mapping approaches to minimize error when boundaries change over time. Most of this was conducted in PostGIS, some Python too.

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix"); } } catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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Canadian Longitudinal Tract Database (CLTD)

This repository includes code to bridge Canadian historical census tract data (every five years from 1951 to 2021) to a common set of geographic boundaries (2021).

Census tracts are geographic boundaries delineated by Statistics Canada used for publishing aggregate census data. They are analogous to neighbourhoods and typically pertain to 4,000 to 7,000 residents.

Each census year, Statistics Canada re-draws (and often adds new) boundaries to account for shifting and growing populations.

This makes it difficult to examine how the demographics of neighbourhoods have changed over time (i.e. cannot always compare on a one-to-one basis).

As such, the goal of this project was to use areal interpolation methods to create a series of concordance tables indicating how boundaries from older years are spatially related to newer years. Specifically, these tables include a set of weights that indicate how to apportion data from a tract boundary in one year to boundaries in another year.

Our work began in 2016, with the creation of concordance tables from 1971 to 2016. The data and a manual describing this original work is available on Dataverse: http://dx.doi.org/10.5683/SP/EUG3DT. A paper detailing the methods was published in The Canadian Geographer.

@article{allentaylor2018,
title={A new tool for neighbourhood change research: The Canadian Longitudinal Census Tract Database, 1971--2016},
author={Allen, Jeff and Taylor, Zack},
journal={The Canadian Geographer/Le G{\'e}ographe canadien},
year={2018},
publisher={Wiley Online Library},
url={https://doi.org/10.1111/cag.12467}
}

We have since updated this to include recently digitized historical tract boundaries for 1951, 1956, 1961, and 1966 - as well as linked all tracts to 2021 tracts.

crosswalk_tables

A set of concordance tables (.csv) which link boundary identifiers between years using a set of apportionment weights.

examples

Example scripts that use these tables to apportion data linked to census tracts from a source year to a target year (e.g. from 1951 to 2021)

src

Contains the code used to generate and validate the crosswalk tables. This utilizes a combination of population weighting, area weighting, and dasymetric mapping approaches to minimize error when boundaries change over time. Most of this was conducted in PostGIS, some Python too.

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms"); } } catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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Canadian Longitudinal Tract Database (CLTD)

This repository includes code to bridge Canadian historical census tract data (every five years from 1951 to 2021) to a common set of geographic boundaries (2021).

Census tracts are geographic boundaries delineated by Statistics Canada used for publishing aggregate census data. They are analogous to neighbourhoods and typically pertain to 4,000 to 7,000 residents.

Each census year, Statistics Canada re-draws (and often adds new) boundaries to account for shifting and growing populations.

This makes it difficult to examine how the demographics of neighbourhoods have changed over time (i.e. cannot always compare on a one-to-one basis).

As such, the goal of this project was to use areal interpolation methods to create a series of concordance tables indicating how boundaries from older years are spatially related to newer years. Specifically, these tables include a set of weights that indicate how to apportion data from a tract boundary in one year to boundaries in another year.

Our work began in 2016, with the creation of concordance tables from 1971 to 2016. The data and a manual describing this original work is available on Dataverse: http://dx.doi.org/10.5683/SP/EUG3DT. A paper detailing the methods was published in The Canadian Geographer.

@article{allentaylor2018,
title={A new tool for neighbourhood change research: The Canadian Longitudinal Census Tract Database, 1971--2016},
author={Allen, Jeff and Taylor, Zack},
journal={The Canadian Geographer/Le G{\'e}ographe canadien},
year={2018},
publisher={Wiley Online Library},
url={https://doi.org/10.1111/cag.12467}
}

We have since updated this to include recently digitized historical tract boundaries for 1951, 1956, 1961, and 1966 - as well as linked all tracts to 2021 tracts.

crosswalk_tables

A set of concordance tables (.csv) which link boundary identifiers between years using a set of apportionment weights.

examples

Example scripts that use these tables to apportion data linked to census tracts from a source year to a target year (e.g. from 1951 to 2021)

src

Contains the code used to generate and validate the crosswalk tables. This utilizes a combination of population weighting, area weighting, and dasymetric mapping approaches to minimize error when boundaries change over time. Most of this was conducted in PostGIS, some Python too.

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links"); } } catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); } })(); (function(){ try { var __m = "youtube.com"; var __re = new RegExp('^' + "youtube\\.com" + '
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Canadian Longitudinal Tract Database (CLTD)

This repository includes code to bridge Canadian historical census tract data (every five years from 1951 to 2021) to a common set of geographic boundaries (2021).

Census tracts are geographic boundaries delineated by Statistics Canada used for publishing aggregate census data. They are analogous to neighbourhoods and typically pertain to 4,000 to 7,000 residents.

Each census year, Statistics Canada re-draws (and often adds new) boundaries to account for shifting and growing populations.

This makes it difficult to examine how the demographics of neighbourhoods have changed over time (i.e. cannot always compare on a one-to-one basis).

As such, the goal of this project was to use areal interpolation methods to create a series of concordance tables indicating how boundaries from older years are spatially related to newer years. Specifically, these tables include a set of weights that indicate how to apportion data from a tract boundary in one year to boundaries in another year.

Our work began in 2016, with the creation of concordance tables from 1971 to 2016. The data and a manual describing this original work is available on Dataverse: http://dx.doi.org/10.5683/SP/EUG3DT. A paper detailing the methods was published in The Canadian Geographer.

@article{allentaylor2018,
title={A new tool for neighbourhood change research: The Canadian Longitudinal Census Tract Database, 1971--2016},
author={Allen, Jeff and Taylor, Zack},
journal={The Canadian Geographer/Le G{\'e}ographe canadien},
year={2018},
publisher={Wiley Online Library},
url={https://doi.org/10.1111/cag.12467}
}

We have since updated this to include recently digitized historical tract boundaries for 1951, 1956, 1961, and 1966 - as well as linked all tracts to 2021 tracts.

crosswalk_tables

A set of concordance tables (.csv) which link boundary identifiers between years using a set of apportionment weights.

examples

Example scripts that use these tables to apportion data linked to census tracts from a source year to a target year (e.g. from 1951 to 2021)

src

Contains the code used to generate and validate the crosswalk tables. This utilizes a combination of population weighting, area weighting, and dasymetric mapping approaches to minimize error when boundaries change over time. Most of this was conducted in PostGIS, some Python too.

About

Canadian Longitudinal Census Tract Database

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default"); } } catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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Canadian Longitudinal Tract Database (CLTD)

This repository includes code to bridge Canadian historical census tract data (every five years from 1951 to 2021) to a common set of geographic boundaries (2021).

Census tracts are geographic boundaries delineated by Statistics Canada used for publishing aggregate census data. They are analogous to neighbourhoods and typically pertain to 4,000 to 7,000 residents.

Each census year, Statistics Canada re-draws (and often adds new) boundaries to account for shifting and growing populations.

This makes it difficult to examine how the demographics of neighbourhoods have changed over time (i.e. cannot always compare on a one-to-one basis).

As such, the goal of this project was to use areal interpolation methods to create a series of concordance tables indicating how boundaries from older years are spatially related to newer years. Specifically, these tables include a set of weights that indicate how to apportion data from a tract boundary in one year to boundaries in another year.

Our work began in 2016, with the creation of concordance tables from 1971 to 2016. The data and a manual describing this original work is available on Dataverse: http://dx.doi.org/10.5683/SP/EUG3DT. A paper detailing the methods was published in The Canadian Geographer.

@article{allentaylor2018,
title={A new tool for neighbourhood change research: The Canadian Longitudinal Census Tract Database, 1971--2016},
author={Allen, Jeff and Taylor, Zack},
journal={The Canadian Geographer/Le G{\'e}ographe canadien},
year={2018},
publisher={Wiley Online Library},
url={https://doi.org/10.1111/cag.12467}
}

We have since updated this to include recently digitized historical tract boundaries for 1951, 1956, 1961, and 1966 - as well as linked all tracts to 2021 tracts.

crosswalk_tables

A set of concordance tables (.csv) which link boundary identifiers between years using a set of apportionment weights.

examples

Example scripts that use these tables to apportion data linked to census tracts from a source year to a target year (e.g. from 1951 to 2021)

src

Contains the code used to generate and validate the crosswalk tables. This utilizes a combination of population weighting, area weighting, and dasymetric mapping approaches to minimize error when boundaries change over time. Most of this was conducted in PostGIS, some Python too.

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Canadian Longitudinal Census Tract Database

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers"); } } catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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Canadian Longitudinal Tract Database (CLTD)

This repository includes code to bridge Canadian historical census tract data (every five years from 1951 to 2021) to a common set of geographic boundaries (2021).

Census tracts are geographic boundaries delineated by Statistics Canada used for publishing aggregate census data. They are analogous to neighbourhoods and typically pertain to 4,000 to 7,000 residents.

Each census year, Statistics Canada re-draws (and often adds new) boundaries to account for shifting and growing populations.

This makes it difficult to examine how the demographics of neighbourhoods have changed over time (i.e. cannot always compare on a one-to-one basis).

As such, the goal of this project was to use areal interpolation methods to create a series of concordance tables indicating how boundaries from older years are spatially related to newer years. Specifically, these tables include a set of weights that indicate how to apportion data from a tract boundary in one year to boundaries in another year.

Our work began in 2016, with the creation of concordance tables from 1971 to 2016. The data and a manual describing this original work is available on Dataverse: http://dx.doi.org/10.5683/SP/EUG3DT. A paper detailing the methods was published in The Canadian Geographer.

@article{allentaylor2018,
title={A new tool for neighbourhood change research: The Canadian Longitudinal Census Tract Database, 1971--2016},
author={Allen, Jeff and Taylor, Zack},
journal={The Canadian Geographer/Le G{\'e}ographe canadien},
year={2018},
publisher={Wiley Online Library},
url={https://doi.org/10.1111/cag.12467}
}

We have since updated this to include recently digitized historical tract boundaries for 1951, 1956, 1961, and 1966 - as well as linked all tracts to 2021 tracts.

crosswalk_tables

A set of concordance tables (.csv) which link boundary identifiers between years using a set of apportionment weights.

examples

Example scripts that use these tables to apportion data linked to census tracts from a source year to a target year (e.g. from 1951 to 2021)

src

Contains the code used to generate and validate the crosswalk tables. This utilizes a combination of population weighting, area weighting, and dasymetric mapping approaches to minimize error when boundaries change over time. Most of this was conducted in PostGIS, some Python too.

About

Canadian Longitudinal Census Tract Database

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode"); } } catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); } })(); })();
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Canadian Longitudinal Tract Database (CLTD)

This repository includes code to bridge Canadian historical census tract data (every five years from 1951 to 2021) to a common set of geographic boundaries (2021).

Census tracts are geographic boundaries delineated by Statistics Canada used for publishing aggregate census data. They are analogous to neighbourhoods and typically pertain to 4,000 to 7,000 residents.

Each census year, Statistics Canada re-draws (and often adds new) boundaries to account for shifting and growing populations.

This makes it difficult to examine how the demographics of neighbourhoods have changed over time (i.e. cannot always compare on a one-to-one basis).

As such, the goal of this project was to use areal interpolation methods to create a series of concordance tables indicating how boundaries from older years are spatially related to newer years. Specifically, these tables include a set of weights that indicate how to apportion data from a tract boundary in one year to boundaries in another year.

Our work began in 2016, with the creation of concordance tables from 1971 to 2016. The data and a manual describing this original work is available on Dataverse: http://dx.doi.org/10.5683/SP/EUG3DT. A paper detailing the methods was published in The Canadian Geographer.

@article{allentaylor2018,
title={A new tool for neighbourhood change research: The Canadian Longitudinal Census Tract Database, 1971--2016},
author={Allen, Jeff and Taylor, Zack},
journal={The Canadian Geographer/Le G{\'e}ographe canadien},
year={2018},
publisher={Wiley Online Library},
url={https://doi.org/10.1111/cag.12467}
}

We have since updated this to include recently digitized historical tract boundaries for 1951, 1956, 1961, and 1966 - as well as linked all tracts to 2021 tracts.

crosswalk_tables

A set of concordance tables (.csv) which link boundary identifiers between years using a set of apportionment weights.

examples

Example scripts that use these tables to apportion data linked to census tracts from a source year to a target year (e.g. from 1951 to 2021)

src

Contains the code used to generate and validate the crosswalk tables. This utilizes a combination of population weighting, area weighting, and dasymetric mapping approaches to minimize error when boundaries change over time. Most of this was conducted in PostGIS, some Python too.

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