31:T66d,function showResults(options = {}) { const { basemap = hexData, regions = hexRegions, labels = true, legend = true, w = 640, title, subtitle } = options; const lookup = toLookup(notionalResults2019, "fid", "winningParty"); const mpParties = [ ...new Set(notionalResults2019.map((d) => d.winningParty)) ]; const { domain, range } = partyPalette; const tuples = domain .map((d, i) => ({ domain: d, range: range[i] })) .filter((d) => mpParties.includes(d.domain)); return Plot.plot({ width: w, height: w * 1.4, title: title, subtitle: subtitle, projection: { type: "transverse-mercator", rotate: [2, 0, 0], domain: basemap }, color: { domain: tuples.map((d) => d.domain), range: tuples.map((d) => d.range), legend: legend }, marks: [ Plot.geo(basemap, { stroke: "black", strokeWidth: 0.3, fill: (d) => lookup[d.properties.fid], opacity: 0.7 }) // Plot.geo(regions, { strokeWidth: 1.5 }), // labels // ? Plot.text( // basemap.features, // Plot.centroid({ // text: (d) => d.properties.name3, // fontSize: w / 90, // opacity: 0.8 // }) // ) // : [], // // For tooltips only // Plot.geo( // basemap, // Plot.centroid({ // stroke: null, // fill: null, // tip: true, // title: (d) => // `${d.properties.PCON24NM}\n${lookup[d.properties.fid]} win` // }) // ) ] }); }32:T482,function showTurnout(options = {}) { const { basemap = hexData, regions = hexRegions, labels = true, legend = true, w = 640, title, subtitle } = options; const luTurnout = toLookup(notionalResults2019, "fid", "turnout"); const luWin = toLookup(notionalResults2019, "fid", "winningParty"); return Plot.plot({ width: w, height: w * 1.4, title: title, subtitle: subtitle, projection: { type: "transverse-mercator", rotate: [2, 0, 0], domain: basemap }, color: { legend: legend, scheme: "Oranges", tickFormat: (d) => `${d}%` }, marks: [ Plot.geo(basemap, { stroke: "black", strokeWidth: 0.3, fill: (d) => luTurnout[d.properties.fid] }), Plot.geo(regions, { strokeWidth: 1.5 }), // For tooltips only Plot.geo( basemap, Plot.centroid({ stroke: null, fill: null, tip: true, title: (d) => `${d.properties.PCON24NM}\n${ luWin[d.properties.fid] } win, turnout: ${luTurnout[d.properties.fid]}%` }) ) ] }); }33:T4e4,function showBoundaryChange(options = {}) { const { basemap = hexData, regions = hexRegions, labels = true, w = 640, title, subtitle } = options; const luChange = toLookup(notionalResults2019, "fid", "boundaryChange"); const luWin = toLookup(notionalResults2019, "fid", "winningParty"); return Plot.plot({ width: w, height: w * 1.4, title: title, subtitle: subtitle, projection: { type: "transverse-mercator", rotate: [2, 0, 0], domain: basemap }, color: { legend: true, scheme: "Oranges", tickFormat: (d) => `${d}%` }, r: { range: [0.5, 7] }, marks: [ Plot.dot( basemap.features, Plot.centroid({ fill: "firebrick", stroke: "white", strokeWidth: 0.4, r: (d) => luChange[d.properties.fid] }) ), Plot.geo(regions, { strokeWidth: 0.5 }), // For tooltips only Plot.geo( basemap, Plot.centroid({ stroke: null, fill: null, tip: true, title: (d) => `${d.properties.PCON24NM}\n${ luWin[d.properties.fid] } win, change: ${luChange[d.properties.fid]}` }) ) ] }); }34:T686,function showMajority(options = {}) { const { basemap = hexData, regions = hexRegions, labels = true, legend = true, w = 640, title, subtitle } = options; const luMajority = toLookup(notionalResults2019, "fid", "majority"); const luWin = toLookup(notionalResults2019, "fid", "winningParty"); const mpParties = [ ...new Set(notionalResults2019.map((d) => d.winningParty)) ]; const { domain, range } = partyPalette; const tuples = domain .map((d, i) => ({ domain: d, range: range[i] })) .filter((d) => mpParties.includes(d.domain)); const numFormat = d3.format(","); return Plot.plot({ width: w, height: w * 1.4, title: title, subtitle: subtitle, projection: { type: "transverse-mercator", rotate: [2, 0, 0], domain: basemap }, color: { domain: tuples.map((d) => d.domain), range: tuples.map((d) => d.range), legend: legend }, r: { range: [0.5, 7] }, marks: [ Plot.dot( basemap.features, Plot.centroid({ fill: "firebrick", stroke: "black", strokeWidth: 0.4, r: (d) => luMajority[d.properties.fid], fill: (d) => luWin[d.properties.fid] }) ), Plot.geo(regions, { strokeWidth: 0.5 }), // For tooltips only Plot.geo( basemap, Plot.centroid({ stroke: null, fill: null, tip: true, title: (d) => `${d.properties.PCON24NM}\n${ luWin[d.properties.fid] } win, majority: ${numFormat(luMajority[d.properties.fid])}` }) ) ] }); }35:T86e,- `boundaryChange` is an index between 0 and 125.2 that indicated the degree to which the boundaries have changed between 2019 and 2023. Higher numbers indicate more change. - Columns `Con`, `Lab` etc. indicate the modelled number of votes cast for the following parties: - █ `Con`: Conservative and Unionist Party - █ `Lab`: Labour Party - █ `LD`: Liberal Democrats - █ `Grn`: Green Party - █ `Ref`: Reform UK (previously Brexit Party) - █ `SNP`: Scottish National Party - █ `PC`: Plaid Cymru (Wales) - █ `PC`: Workers Party of Britain - █ `DUP`: Democratic Unionist Party (Northern Ireland) - █ `SF`: Sinn Féin (Northern Ireland) - █ `All`: Alliance Party of Northern Ireland - █ `SDLP`: Social Democratic and Labour Party (Northern Ireland) - █ `UUP`: Ulster Unionist Party (Northern Ireland) - █ `TUV`: Traditional Unionist Voice (Northern Ireland) - █ `Ant`: Aontú (Northern Ireland) - █ `PBP`: People Before Profit (Northern Ireland) - █ `Ind`: Independent - █ `other`: Total of all other parties not mentioned above - █ `Speaker`: The speaker of the house does not have any party-affiliated vote in the commons even if elected via a 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2f:{"title":"UK Election 2024: Boundary Data","cells":[{"id":0,"value":"# UK Election 2024: Boundary Data","pinned":false,"hidden":false,"mode":"md"},{"id":525,"value":"Aligned datasets representing the 650 Westminster parliamentary constituencies up for election in July 2024 along with the notional 2019 General Election results projected onto the 2024 constituencies. \n\nOne set of boundaries shows geographic arrangment, the other a 'hex cartogram' where each constituency is a standard sized hexagon positioned in its approximate geographic location (derived from the hex cartogram from _Automatic Knowledge Ltd_).\n\nTo use in other pages you can import one or more of the datasets and colour mappings here:\n\n```javascript\nimport {\n geoData,\n geoDataOSGB,\n geoRegions,\n geoRegionsOSGB,\n hexData,\n hexDataPlanar,\n hexRegions,\n hexRegionsPlanar,\n notionalResults2019,\n partyClrs,\n partyPalette\n} from \"@jwolondon/uk-election-2024-boundary-data\"\n```","pinned":false,"hidden":false,"mode":"md"},{"id":94,"value":"

\n ${drawMap(hexData,{regionBoundaries:hexRegions})} ${drawMap(geoData,{regionBoundaries:geoRegions})}\n

","pinned":false,"hidden":false,"mode":"html"},{"id":1126,"value":"{\n const regionClrs = [\n \"#b57073\",\n \"#85c04c\",\n \"#9460cc\",\n \"#647c40\",\n \"#d07f39\",\n \"#cfad54\",\n \"#489ec3\",\n \"#ca4e98\",\n \"#70c5a3\",\n \"#d0454c\",\n \"#6476bb\",\n \"#cb9ace\"\n ];\n\n const map = t === 0 ? geoData : t === 1 ? hexData : interpMap;\n\n return Plot.plot({\n width: 500,\n height: 430,\n projection: { type: \"reflect-y\", domain: map },\n color: { range: regionClrs },\n marks: [\n Plot.geo(map, {\n stroke: \"black\",\n strokeWidth: 0.1,\n fill: (d) => d.properties.regionNM,\n fillOpacity: 0.7\n })\n ]\n });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1067,"value":"viewof t = Inputs.range([0, 1], {\n label: \"Transition between the two maps\",\n value: 0,\n step: 0.05\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":531,"value":"To export the boundary data as geoJSON files:","pinned":false,"hidden":false,"mode":"md"},{"id":533,"value":"DOM.download(\n () => {\n return new Blob([JSON.stringify(geoData)], {\n type: \"application/txt\"\n });\n },\n `geoConstituencies.json`,\n `Save long/lat geographic boundaries as geoJSON`\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1044,"value":"DOM.download(\n () => {\n return new Blob([JSON.stringify(geoDataOSGB)], {\n type: \"application/txt\"\n });\n },\n `geoConstituenciesOSGB.json`,\n `Save OSGB geographic boundaries as geoJSON`\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":537,"value":"DOM.download(\n () => {\n return new Blob([JSON.stringify(hexData)], {\n type: \"application/txt\"\n });\n },\n `hexConstituencies.json`,\n `Save hex boundaries as geoJSON`\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1376,"value":"DOM.download(\n new Blob(\n [\n `PCON24CD,electorate\\n` +\n hexData.features\n .map((d) => [d.properties.PCON24CD, d.properties.electorate])\n .join(\"\\n\")\n ],\n {\n type: \"application/txt\"\n }\n ),\n \"electorate.csv\",\n \"Save electorate data as CSV\"\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":716,"value":"## Examples","pinned":false,"hidden":false,"mode":"md"},{"id":723,"value":"showResults({title: \"2019 General Election results\", subtitle: \"Estimated projection onto 2024 constituencies\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":642,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":880,"value":"

\n ${showTurnout({\n title: \"2019 General Election turnout\",\n subtitle: \"Estimated projection onto 2024 constituencies\",\n w: 400\n})} ${showTurnout({\n basemap:geoData,\n regions:geoRegions,\n legend:false,\n w: 480\n})}\n

","pinned":false,"hidden":false,"mode":"html"},{"id":807,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":904,"value":"

\n ${showBoundaryChange({\n title: \"Constituency boundary changes 2019 to 2024\",\n w: 400\n})} ${showBoundaryChange({\n basemap:geoData,\n regions:geoRegions,\n w: 420\n})}\n

","pinned":false,"hidden":false,"mode":"html"},{"id":835,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":916,"value":"

\n ${showMajority({\n title: \"2019 General Election winning majority\",\n subtitle: \"Estimated projection onto 2024 constituencies\",\n w: 400\n})} ${showMajority({\n basemap:geoData,\n regions:geoRegions,\n w: 470,\n legend: false\n})}\n

","pinned":false,"hidden":false,"mode":"html"},{"id":913,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":969,"value":"See also, these [election forecast predictions](https://observablehq.com/@jwolondon/uk-election-2024-forecast-mapping) that combine these boundary data with polling data to predict the result of the July 2024 general election. ","pinned":false,"hidden":false,"mode":"md"},{"id":529,"value":"## Data Shaping","pinned":false,"hidden":false,"mode":"md"},{"id":244,"value":"### Geographic Boundaries\n\nThe ONS/Ordnance Survey provide generalised UK parliamentary constituency boundaries for 2024 [here](https://www.data.gov.uk/dataset/723ef91a-e7b1-40ce-b665-6cc11a8dee8a/westminster-parliamentary-constituencies-july-2024-boundaries-uk-buc). The original uses Ordnance Survey National Grid coordinates and the following steps were taken in [mapshaper](https://mapshaper.org) to create the topoJSON used here:\n\n- Exported from the ONS geoportal as a _shapefile_ in order to keep projection information;\n- Exported from mapShaper as a topoJSON file as `constituencies` feature layer (OSGB coordinates)\n- Converted to longitude/latitude with the `-proj wgs84` console option;\n- Exported as a topoJSON file as `constituencies` feature layer (long/lat coordinates).\n\nThe boundary files are stored here as attachments before converting into geoJSON feature collections:","pinned":false,"hidden":false,"mode":"md"},{"id":31,"value":"geoBoundaries = FileAttachment(\"constituencies2024longLatTopo@1.json\")\n .json()\n .then((d) => topojson.feature(d, \"constituencies\"))","pinned":false,"hidden":false,"mode":"ojs"},{"id":1013,"value":"geoBoundariesOSGB = FileAttachment(\"constituencies2024OSGBTopo.json\")\n .json()\n .then((d) => topojson.feature(d, \"constituencies\"))","pinned":false,"hidden":false,"mode":"ojs"},{"id":547,"value":"Additionally, region boundaries (generated in by dissolving in mapshaper) are stored separately:","pinned":false,"hidden":false,"mode":"md"},{"id":544,"value":"geoRegions = FileAttachment(\"geoRegionsTopo@1.json\")\n .json()\n .then((d) => topojson.feature(d, \"regions\"))","pinned":false,"hidden":false,"mode":"ojs"},{"id":1050,"value":"geoRegionsOSGB = FileAttachment(\"geoRegionsOSGBTopo.json\")\n .json()\n .then((d) => topojson.feature(d, \"regions\"))","pinned":false,"hidden":false,"mode":"ojs"},{"id":246,"value":"### Hex Boundaries\n\nPhilip Brown and Alasdair Rae from _Automatic Knowledge Ltd_ provide a [hex cartogram](https://automaticknowledge.org/wpc-hex/) of the 650 Westminster parliamentary constituencies giving an often more useful base-map for election and constituency mapping ([CC by 4.0 licence and details here](https://automaticknowledge.org/wpc-hex/-readme.txt)). There are two versions of the boundary file – one approximating longitude/latitude values suitable for reprojection in many mapping systems, the other approximating pre-projected Ordnance Survey National Grid coordinates that requires no reprojection.\n\n`uk-wpc-hex-constitcode-v4-nov-2023_EPSG-4326.geojson` was downloaded, providing the hex boundaries located approximately with longitude/latitude coordinates. It was converted into topoJSON format in mapShaper and attached here before converting back to a geoJSON feature collection:","pinned":false,"hidden":false,"mode":"md"},{"id":14,"value":"hexBoundaries = FileAttachment(\"hexTopoWGS84@1.json\")\n .json()\n .then((d) => topojson.feature(d, \"hexTopo\"))","pinned":false,"hidden":false,"mode":"ojs"},{"id":977,"value":"Similarly, `uk-wpc-hex-constitcode-v4-nov-2023.geojson` was downloaded and converted into geoJSON to provide a version with projected planar coordinates:","pinned":false,"hidden":false,"mode":"md"},{"id":979,"value":"hexBoundariesPlanar = FileAttachment(\"hexTopoPlanar@1.json\")\n .json()\n .then((d) => topojson.feature(d, \"hexTopo\"))","pinned":false,"hidden":false,"mode":"ojs"},{"id":491,"value":"Additionally, region boundaries on the hex basemaps (generated in by dissolving in mapshaper) are stored separately:","pinned":false,"hidden":false,"mode":"md"},{"id":486,"value":"hexRegions = FileAttachment(\"hexRegionsTopo.json\")\n .json()\n .then((d) => topojson.feature(d, \"regions\"))","pinned":false,"hidden":false,"mode":"ojs"},{"id":997,"value":"hexRegionsPlanar = FileAttachment(\"hexRegionsPlanarTopo@1.json\")\n .json()\n .then((d) => topojson.feature(d, \"regions\"))","pinned":false,"hidden":false,"mode":"ojs"},{"id":272,"value":"### Standardising Attributes\n\nTo support mapping with either the geographic or hex boundaries, a common set of feature attributes to both datasets was created by standardising attribute names and storing region geographies. The following additional changes were made to the combined attribute table:\n\n- Commas removed from constituency names (e.g. \"Argyll, Bute and South Lochaber\") to simplify any export of attributes as CSV files.\n- The hex maps were originally produced using November 2023 GSS codes, which for 5 Scottish constituencies are different to the 2024 GSS code. They were therefore renamed to their 2024 code in both hex files.","pinned":false,"hidden":false,"mode":"md"},{"id":287,"value":"Inputs.table(\n [\n {\n name: \"Ayr, Carrick and Cumnock\",\n november2023Code: \"S14000006\",\n july2024Code: \"S14000107\"\n },\n {\n name: \"Berwickshire, Roxburgh and Selkirk\",\n november2023Code: \"S14000008\",\n july2024Code: \"S14000108\"\n },\n {\n name: \"Central Ayrshire\",\n november2023Code: \"S14000010\",\n july2024Code: \"S14000109\"\n },\n {\n name: \"Kilmarnock and Loudoun\",\n november2023Code: \"S14000040\",\n july2024Code: \"S14000110\"\n },\n {\n name: \"West Aberdeenshire and Kincardine\",\n november2023Code: \"S14000058\",\n july2024Code: \"S14000111\"\n }\n ],\n { layout: \"auto\" }\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":299,"value":"- Excel can incorrectly convert non-ASCII characters so these were replaced with the correct accented characters for affected Welsh names:\n\n * _Gŵyr_\n * _Maldwyn a Glyndŵr_\n * _Merthyr Tudful ac Aberdâre_\n * _Ynys Môn_\n ","pinned":false,"hidden":false,"mode":"md"},{"id":297,"value":"Attributes are stored here as an attachment and shown below:","pinned":false,"hidden":false,"mode":"md"},{"id":233,"value":"attributes = FileAttachment(\"attributes@8.csv\").csv({ typed: true })","pinned":false,"hidden":false,"mode":"ojs"},{"id":280,"value":"Inputs.table(attributes, { layout: \"auto\", rows: 8 })","pinned":false,"hidden":false,"mode":"ojs"},{"id":327,"value":"### Combined datasets\n\nThe attribute objects were combined with the geometry of the two boundary files (hex and geographic) to provide a single pair of geoJSONs for use in Observable for visualization. These can be easily imported into other documents for visualization.","pinned":false,"hidden":false,"mode":"md"},{"id":338,"value":"geoData = setAttributes(geoBoundaries)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1040,"value":"geoDataOSGB = setAttributes(geoBoundariesOSGB)","pinned":false,"hidden":false,"mode":"ojs"},{"id":407,"value":"hexData = setAttributes(hexBoundaries, {\n srcNameKey: \"GSScode\",\n attribNameKey: \"PCON24CD\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":985,"value":"hexDataPlanar = setAttributes(hexBoundariesPlanar, {\n srcNameKey: \"GSScode\",\n attribNameKey: \"PCON24CD\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":552,"value":"### Notional 2019 Results\n\nThrasher and Rallings re-aggregated the 2019 General Election results according to the new electoral boundaries used in the 2024 election. For constituencies where the boundaries have changed they modelled the finer grained results based on local election results to estimate the new vote totals. Their modelled results can be found [from the BBC](https://downloads.bbc.co.uk/news/nol/shared/spl/xls_spreadsheets/results_spreadsheet.ods) and further explained on [Wikipedia](https://en.wikipedia.org/wiki/Notional_results_of_the_2019_United_Kingdom_general_election_by_2024_constituency). \n\nBelow is a formatted version that uses the feature ID (`fid`), GSS code (`PCCON24CD`) and constituency name (`PCON24NM`) consistent with the boundary data used here:","pinned":false,"hidden":false,"mode":"md"},{"id":555,"value":"notionalResults2019 = FileAttachment(\"notionalResults2019@5.csv\").csv({\n typed: true\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":558,"value":"Inputs.table(notionalResults2019, { layout: \"auto\", rows: 8 })","pinned":false,"hidden":false,"mode":"ojs"},{"id":564,"value":"$35","pinned":false,"hidden":false,"mode":"md"},{"id":971,"value":"fullPartyName = new Map([\n [\"Con\", \"Conservative Party\"],\n [\"Lab\", \"Labour Party\"],\n [\"LD\", \"Liberal Democrats\"],\n [\"Grn\", \"Green Party\"],\n [\"Ref\", \"Reform UK\"],\n [\"Brx\", \"Brexit Party\"],\n [\"SNP\", \"Scottish National Party\"],\n [\"PC\", \"Plaid Cymru\"],\n [\"WP\", \"Workers Party of Britain\"],\n [\"DUP\", \"Democratic Unionist Party\"],\n [\"SF\", \"Sinn Féin\"],\n [\"All\", \"Alliance Party of Northern Ireland\"],\n [\"SDLP\", \"Social Democratic and Labour Party\"],\n [\"UUP\", \"Ulster Unionist Party\"],\n [\"TUV\", \"Traditional Unionist Voice\"],\n [\"Ant\", \"Aontú\"],\n [\"PBP\", \"People Before Profit\"],\n [\"Ind\", \"Independent\"],\n [\"other\", \"all other parties\"],\n [\"Speaker\", \"Commons Speaker\"]\n])","pinned":false,"hidden":false,"mode":"ojs"},{"id":629,"value":"Party labels to their associated colour mappings are available here:","pinned":false,"hidden":false,"mode":"md"},{"id":579,"value":"partyClrs = new Map([\n [\"Con\", \"rgb(59,120,214)\"],\n [\"Lab\", \"rgb(209,45,65)\"],\n [\"LD\", \"rgb(238,170,65)\"],\n [\"Grn\", \"rgb(75,166,99)\"],\n [\"Ref\", \"rgb(88,210,211)\"],\n [\"SNP\", \"rgb(251,243,155)\"],\n [\"PC\", \"rgb(37,90,84)\"],\n [\"WP\", \"rgb(230,160,180)\"],\n [\"DUP\", \"rgb(198,112,83)\"],\n [\"SF\", \"rgb(63,102,96)\"],\n [\"All\", \"rgb(239,205,82)\"],\n [\"SDLP\", \"rgb(84,166,64)\"],\n [\"UUP\", \"rgb(97,163,232)\"],\n [\"TUV\", \"rgb(88,191,201)\"],\n [\"Ant\", \"rgb(71,83,47)\"],\n [\"PBP\", \"rgb(214,56,84)\"],\n [\"Ind\", \"rgb(140,0,100)\"],\n [\"other\", \"rgb(180,180,180)\"],\n [\"Speaker\", \"rgb(220,220,220)\"]\n])","pinned":false,"hidden":false,"mode":"ojs"},{"id":684,"value":"Or as a 'domain/range' object suitable for Observable Plot / D3:","pinned":false,"hidden":false,"mode":"md"},{"id":688,"value":"partyPalette = ({\n domain: [...partyClrs].map(([d, r]) => d),\n range: [...partyClrs].map(([d, r]) => r)\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":329,"value":"---\n\n## Appendices","pinned":false,"hidden":false,"mode":"md"},{"id":1062,"value":"_For animation between geographic and hex maps:_","pinned":false,"hidden":false,"mode":"md"},{"id":1059,"value":"flubber = require(\"flubber\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":1064,"value":"interpMap = {\n const interpCoords = flubber.interpolateAll(\n geoPolys.map((d) => d.geometry.coordinates[0]),\n hexPolys.map((d) => d.geometry.coordinates[0]),\n {\n string: false,\n single: true\n }\n )(t);\n\n return {\n type: \"FeatureCollection\",\n features: interpCoords.map((d, i) => ({\n type: \"Feature\",\n geometry: { type: \"Polygon\", coordinates: [interpCoords[i]] },\n properties: hexPolys[i].properties\n }))\n };\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1361,"value":"_To replace any multipolygons in a geoJSON with the largest single simple polygon:_","pinned":false,"hidden":false,"mode":"md"},{"id":1101,"value":"function toRingFeatures(geojson) {\n const sortedFeats = geojson.features.sort((f1, f2) =>\n f2.properties.PCON24CD.localeCompare(f1.properties.PCON24CD)\n );\n for (const feat of sortedFeats) {\n if (feat.geometry.type === \"MultiPolygon\") {\n const largestPolygon = feat.geometry.coordinates.reduce(\n (largest, current) =>\n d3.polygonArea(current[0]) > d3.polygonArea(largest[0])\n ? current\n : largest,\n feat.geometry.coordinates[0]\n );\n feat.geometry.type = \"Polygon\";\n feat.geometry.coordinates = largestPolygon;\n }\n }\n return sortedFeats;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1070,"value":"geoPolys = toRingFeatures(geoData)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1079,"value":"hexPolys = toRingFeatures(hexData)","pinned":false,"hidden":false,"mode":"ojs"},{"id":503,"value":"_For transferring a common set of attributes to both geoJSONs:_","pinned":false,"hidden":false,"mode":"md"},{"id":336,"value":"function setAttributes(geoJSON, options = {}) {\n const { srcNameKey = \"PCON24NM\", attribNameKey = \"PCON24NM\" } = options;\n const lu = new Map(attributes.map((d) => [d[attribNameKey], d]));\n\n const combined = { type: \"FeatureCollection\", features: [] };\n for (const feat of geoJSON.features) {\n combined.features.push({\n type: \"Feature\",\n properties: lu.get(feat.properties[srcNameKey].replace(\",\", \"\")),\n geometry: feat.geometry\n });\n }\n return combined;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":653,"value":"_For combining multiple tables via a lookup object:_","pinned":false,"hidden":false,"mode":"md"},{"id":655,"value":"function toLookup(objects, keys = \"key\", value = \"value\", separator = \"_\") {\n return objects.reduce((acc, obj) => {\n const accKey = Array.isArray(keys)\n ? keys.map((key) => obj[key]).join(separator)\n : obj[keys];\n const accValue = Array.isArray(value)\n ? value.reduce((valAcc, val) => ({ ...valAcc, [val]: obj[val] }), {})\n : obj[value];\n return { ...acc, [accKey]: accValue };\n }, {});\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1367,"value":"_For drawing a map with coloured attributes:_","pinned":false,"hidden":false,"mode":"md"},{"id":85,"value":"function drawMap(constituencyData, options = {}) {\n const {\n regionBoundaries,\n regionClrs = [\n \"#b57073\",\n \"#85c04c\",\n \"#9460cc\",\n \"#647c40\",\n \"#d07f39\",\n \"#cfad54\",\n \"#489ec3\",\n \"#ca4e98\",\n \"#70c5a3\",\n \"#d0454c\",\n \"#6476bb\",\n \"#cb9ace\"\n ]\n } = options;\n\n return Plot.plot({\n projection: {\n // Approximates OSGB but rotated anticlockwise by 4 degrees (OSGB would be to rotate by -2 degrees)\n // to make central line of hexes vertical\n type: \"transverse-mercator\",\n rotate: [2, 0, 0],\n domain: constituencyData\n },\n color: { range: regionClrs },\n marks: [\n Plot.geo(constituencyData.features, {\n stroke: \"black\",\n strokeWidth: 0.1,\n fill: (d) => d.properties.regionNM,\n fillOpacity: 0.7\n }),\n regionBoundaries\n ? Plot.geo(regionBoundaries, { stroke: \"white\", strokeWidth: 0.5 })\n : []\n ]\n });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":331,"value":"_For exporting the attributes from the original constituency files:_","pinned":false,"hidden":false,"mode":"md"},{"id":218,"value":"DOM.download(\n () => {\n return new Blob([d3.csvFormat(hexData.features.map((d) => d.properties))], {\n type: \"application/txt\"\n });\n },\n `hexProperties.csv`,\n `Save hex attributes as CSV file`\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":225,"value":"DOM.download(\n () => {\n return new Blob([d3.csvFormat(geoData.features.map((d) => d.properties))], {\n type: \"application/txt\"\n });\n },\n `constituencyProperties.csv`,\n `Save constituencies attributes as CSV file`\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1393,"value":"","pinned":false,"hidden":false,"mode":"html"}],"files":"$W36","stdlib":"1","titling":"auto"}