30:T470,// Combine all the data, using the projection for 2080-2099 data = { return clone(data_rcp45) .map(d => { // Match with other datasets const worldbankMatch = worldbankParsed.find(d0 => d.ISO_code === d0.iso) const unMatch = unParsed.find(d0 => d.ISO_code === d0.iso); const tempsMatch = tempsParsed.find(d0 => d.ISO_code === d0.iso) // Remove countries for which there is no matching data // This will yield 201 countries and territories if (!worldbankMatch || !unMatch || !tempsMatch){ return false; } const o = {}; o.country = countryLookup.find(d0 => d0.iso === d.ISO_code).country; o.iso = d.ISO_code; o.rcp45 = d.years_2080_2099; o.rcp85 = data_rcp85.find(d0 => d0.ISO_code === d.ISO_code).years_2080_2099; o.temp_f = tempsMatch.temp_f; o.temp_c = tempsMatch.temp_c; const gdp = worldbankMatch.gdp; const pop = unMatch.pop; o.gdp_percap = gdp / pop; return o; }) .filter(d => d.rcp45 && d.gdp_percap && d.temp_f) .sort((a, b) => d3.descending(a.gdp_percap, b.gdp_percap)) }31:T5f5,xAxis = g => { const tickValues = scenario === "rcp45" ? [-300, -150, 0, 150] : [-400, -200, 0, 200, 400]; const axis = g.append("g") .attr("transform", `translate(0, ${chartheight})`) .call( d3.axisBottom(x) .tickValues(tickValues) .tickSizeOuter(0) .tickSizeInner(10) ); axis.select(".domain").remove() axis.selectAll("text") .attr("font-size", 12) .attr("font-family", franklinLight); axis.selectAll("line") .attr("stroke-width", 0.5) .attr("shape-rendering", "crispEdges") .attr("stroke", d => d ? "#c2c2c2" : "#000"); const grid = g.append("g") .call( d3.axisBottom(x) .tickValues(tickValues) .tickSize(chartheight) ) grid.select(".domain").remove(); grid.selectAll("text").remove(); grid.selectAll("line") .attr("stroke-width", 0.5) .attr("shape-rendering", "crispEdges") .attr("stroke", d => d ? "#c2c2c2" : "#000"); g.append("text") .attr("transform", `translate(${x(0)}, ${chartheight})`) .attr("text-anchor", "middle") .attr("dy", margin.bottom - 24) .html("⟵ Change in mortality linked to temperature ⟶"); g.append("text") .attr("transform", `translate(${x(0)}, ${chartheight})`) .attr("text-anchor", "middle") .attr("dy", margin.bottom - 4) .attr("font-size", 14) .text("Annual deaths per 100,000 people") return g; }32:T55a,yAxis = g => { const tickValues = unit === "temp_f" ? [0, 20, 40, 60, 80] : [-20, -10, 0, 10, 20]; const axis = g.append("g") .call( d3.axisLeft(y) .tickFormat(d => `${d}°`) .tickValues(tickValues) .tickSizeOuter(0) .tickSizeInner(10) ); axis.select(".domain").remove() const ticks = axis.selectAll(".tick"); ticks.select("line") .attr("stroke-width", 0.5) .attr("shape-rendering", "crispEdges") .attr("stroke", d => d ? "#c2c2c2" : "#000"); ticks.select("text") .attr("font-size", 12) .attr("font-family", franklinLight); const grid = g.append("g") .attr("transform", `translate(${chartwidth})`) .call( d3.axisLeft(y) .tickValues(tickValues) .tickSize(chartwidth) ); grid.select(".domain").remove(); const gridTicks = grid.selectAll(".tick"); gridTicks.select("text").remove(); gridTicks.select("line") .attr("stroke-width", 0.5) .attr("shape-rendering", "crispEdges") .attr("stroke", d => d ? "#c2c2c2" : "#000"); g.append("text") .attr("transform", `translate(0, 16)`) .attr("dx", -margin.left) .attr("dy", -margin.top) .html(`↑ Average temperature (${unit.replace("temp_", "").toUpperCase()})`) }33:T423,annotate = g => { const annos = g.selectAll(".anno") .data(annotations) .enter().append("g") .attr("class", "anno") .attr("transform", d => `translate(${[x(d[scenario]), y(d[unit])]})`); annos.append("polyline") .attr("points", d => { const l = useR ? r(d.gdp_percap) : 4; const start = geometric.pointTranslate([0, 0], d.angle, l); const end = geometric.pointTranslate(start, d.angle, 5); return [start, end]; }) .attr("stroke", "#222"); annos.append("text") .attr("transform", d => { const l = useR ? r(d.gdp_percap) : 4; return `translate(${geometric.pointTranslate([0, 0], d.angle, l + 5)})` }) .attr("text-anchor", d => d.textAnchor) .attr("font-size", 14) .attr("dy", d => d.dy) .attr("dx", d => d.dx) .attr("stroke", "white") .attr("paint-order", "stroke fill") .attr("stroke-width", 4) .attr("stroke-linejoin", "round") .attr("stroke-opacity", 0.7) .attr("pointer-events", "none") .text(d => d.label); }34:[{"id":"dc904b95417bf838dabed18c738fa3b4f78902db5152d53d1e5ba74ae6ff425540888443d9e83cdfe52717f3ee41149d5b5517005e04d21edc40606eb58bb1ae","href":"https://static.observableusercontent.com/files/dc904b95417bf838dabed18c738fa3b4f78902db5152d53d1e5ba74ae6ff425540888443d9e83cdfe52717f3ee41149d5b5517005e04d21edc40606eb58bb1ae","name":"country-lookup@2.csv","type":"text/csv","lastModified":1675868410249.575,"size":3091},{"id":"def38e50f6cb0217a75ebd89b8b69123073f6c9e25e215b9e5c012eec8d493c1005b8a858b66c3289416179d6ac833cac71f47c607cb46fdad5a6c2073bbbfea","href":"https://static.observableusercontent.com/files/def38e50f6cb0217a75ebd89b8b69123073f6c9e25e215b9e5c012eec8d493c1005b8a858b66c3289416179d6ac833cac71f47c607cb46fdad5a6c2073bbbfea","name":"API_NY@1.GDP.MKTP.CD_DS2_en_csv_v2_4770391.csv","type":"text/csv","lastModified":1675868452452.686,"size":268861},{"id":"39020437db37526d256fcebf523a60f5895e5a28b1a5b36f47d27265ef9b76cd497ea91a063db280e9a6972bfb20e6aa28fe8c6d276100c3d43c258abe638079","href":"https://static.observableusercontent.com/files/39020437db37526d256fcebf523a60f5895e5a28b1a5b36f47d27265ef9b76cd497ea91a063db280e9a6972bfb20e6aa28fe8c6d276100c3d43c258abe638079","name":"country-annual-temperatures@1.csv","type":"text/csv","lastModified":1675868482245.166,"size":714010},{"id":"19fc898795c8ba6f8b15e86043d7c0be99edff593b53f8a785542f3b71960bdcd7f118b4ff604a70f0d3e92da183d64098f64f4db6f3c99cbbb271953ec7a972","href":"https://static.observableusercontent.com/files/19fc898795c8ba6f8b15e86043d7c0be99edff593b53f8a785542f3b71960bdcd7f118b4ff604a70f0d3e92da183d64098f64f4db6f3c99cbbb271953ec7a972","name":"WPP2022_Demographic_Indicators_Medium@1.csv","type":"text/csv","lastModified":1675868544780.786,"size":20497739},{"id":"18ee8c1ad1e751808f618d84def8aa2f6c5135ad381ddb832ca005ebff01d390e1cdd0dd3efca8398b87702ce5feef0df9e246a6a4ee887fe84b0836f5eedbdd","href":"https://static.observableusercontent.com/files/18ee8c1ad1e751808f618d84def8aa2f6c5135ad381ddb832ca005ebff01d390e1cdd0dd3efca8398b87702ce5feef0df9e246a6a4ee887fe84b0836f5eedbdd","name":"unit_change_in_deathrate_geography_country_level_years_averaged_rcp45_SSP3_quantiles_mean@1.csv","type":"text/csv","lastModified":1675870149076.36,"size":14358},{"id":"2b55a3b9b475df1e173d07e29d9e4b23b3f5c20b01500c86dca3246d8265ff4e119f39dd145f69d1bea9898723a13153db8d0cc1edf3cf378c1690cb4be60b55","href":"https://static.observableusercontent.com/files/2b55a3b9b475df1e173d07e29d9e4b23b3f5c20b01500c86dca3246d8265ff4e119f39dd145f69d1bea9898723a13153db8d0cc1edf3cf378c1690cb4be60b55","name":"unit_change_in_deathrate_geography_country_level_years_averaged_rcp85_SSP3_quantiles_mean@1.csv","type":"text/csv","lastModified":1675870199184.922,"size":14320}] 2e:{"title":"Countries’ average temperature vs. projected change in temperature-related deaths per 100,000 people","cells":[{"id":0,"value":"# Countries’ average temperature vs. projected change in temperature-related deaths per 100,000 people\n\nAn interactive version of this chart appeared in the story “[Will global warming make temperature less deadly?](https://www.washingtonpost.com/climate-environment/interactive/2023/hot-cold-extreme-temperature-deaths/)”, published in The Washington Post on Feb. 16, 2023. For more information on the data sources, see [this notebook](https://observablehq.com/d/1b32ffa7d9b5e968).\n\nThe chart below shows the relationship between 199 countries’ average temperature and how their temperature-linked mortality is projected to change by the end of the century, according to [peer-reviewed research](https://academic.oup.com/qje/article-abstract/137/4/2037/6571943?redirectedFrom=fulltext&itid=lk_inline_enhanced-template&login=false) published in Harvard’s Quarterly Journal of Economics.","pinned":false,"hidden":false,"mode":"md"},{"id":26,"value":"viewof scenario = Inputs.radio([\"rcp45\", \"rcp85\"], { value: \"rcp45\", format: d => d.replace(\"rcp\", \"RCP \").replace(\"5\", \".5\"), label: \"Scenario:\" })","pinned":false,"hidden":false,"mode":"ojs"},{"id":443,"value":"viewof unit = Inputs.radio([\"temp_f\", \"temp_c\"], { format: (_, i) => [\"Fahrenheit\", \"Celsius\"][i], value: \"temp_f\", label: \"Unit:\" })","pinned":false,"hidden":false,"mode":"ojs"},{"id":406,"value":"viewof useR = Inputs.toggle({ label: \"Size circles by GDP per capita:\", value: true })","pinned":false,"hidden":false,"mode":"ojs"},{"id":47,"value":"{\n const svg = d3.create(\"svg\")\n .attr(\"width\", size)\n .attr(\"height\", chartheight + margin.top + margin.bottom)\n .attr(\"font-family\", franklinLight);\n\n const g = svg.append(\"g\")\n .attr(\"transform\", `translate(${[ margin.left, margin.top ]})`);\n\n g.call(yAxis);\n g.call(xAxis);\n\n const country = g.selectAll(\".country\")\n .data(data)\n .join(\"g\")\n .attr(\"class\", \"country\")\n .attr(\"transform\", d => `translate(${[ x(d[scenario]), y(d[unit]) ]})`);\n\n country.append(\"circle\")\n .attr(\"r\", d => useR ? r(d.gdp_percap) : 4)\n .attr(\"fill\", d => color(d[scenario]))\n .attr(\"fill-opacity\", 0.9)\n .attr(\"stroke\", d => annotations.find(d0 => d0.code === d.iso) ? \"black\" : d3.color(color(d[scenario])).darker())\n .attr(\"stroke-width\", d => annotations.find(d0 => d0.code === d.iso) ? 1 : 0.5);\n\n country.filter(d => annotations.find(d0 => d0.code === d.iso)).raise()\n \n g.call(annotate);\n \n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":706,"value":"toc({ selector: \"h2, h3\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":640,"value":"md`## Data`","pinned":false,"hidden":false,"mode":"ojs"},{"id":696,"value":"md`### Sources`","pinned":false,"hidden":false,"mode":"ojs"},{"id":5,"value":"// From researchers affiliated with the Climate Impact Lab via Human Climate Horizons\n// https://www.dropbox.com/s/qpxg51eoxg4lizy/IMPACTS%20country-level.zip?dl=0\ndata_rcp45 = FileAttachment(\"unit_change_in_deathrate_geography_country_level_years_averaged_rcp45_SSP3_quantiles_mean@1.csv\").csv({ typed: true })","pinned":true,"hidden":false,"mode":"ojs"},{"id":7,"value":"// From researchers affiliated with the Climate Impact Lab via Human Climate Horizons\n// https://www.dropbox.com/s/qpxg51eoxg4lizy/IMPACTS%20country-level.zip?dl=0\ndata_rcp85 = FileAttachment(\"unit_change_in_deathrate_geography_country_level_years_averaged_rcp85_SSP3_quantiles_mean@1.csv\").csv({ typed: true })","pinned":true,"hidden":false,"mode":"ojs"},{"id":199,"value":"// From the United Nations, Department of Economic and Social Affairs, Population Division (2022). World Population Prospects 2022, Online Edition.\n// via https://population.un.org/wpp/Download/Standard/Population/\nun = FileAttachment(\"WPP2022_Demographic_Indicators_Medium@1.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":132,"value":"// GDP data through 2021 from the World Bank\n// via https://data.worldbank.org/indicator/NY.GDP.MKTP.CD\nworldbank = FileAttachment(\"API_NY@1.GDP.MKTP.CD_DS2_en_csv_v2_4770391.csv\").text()","pinned":true,"hidden":false,"mode":"ojs"},{"id":375,"value":"// TIMESERIES CRU (Observed) data by year and country\n// from the World Bank's Climate Change Knowledge Portal\n// via https://climateknowledgeportal.worldbank.org/download-data\n// accessed Feb. 2, 2023\ntemps = FileAttachment(\"country-annual-temperatures@1.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":592,"value":"// A lookup table for country names\ncountryLookup = FileAttachment(\"country-lookup@2.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":220,"value":"// Countries to annotate on the chart\nannotations = [\n {\n code: \"AUS\",\n label: \"Australia\",\n textAnchor: \"start\",\n angle: 0,\n dy: 5,\n dx: 2,\n },\n {\n code: \"CAN\",\n label: \"Canada\",\n textAnchor: \"start\",\n angle: 0,\n dy: 5,\n dx: 2,\n },\n {\n code: \"CHN\",\n label: \"China\",\n textAnchor: \"start\",\n angle: 45,\n dy: 7,\n dx: 2\n },\n {\n code: \"FIN\",\n label: \"Finland\",\n textAnchor: \"middle\",\n angle: 270,\n dy: -2,\n dx: 0,\n },\n {\n code: \"GRL\",\n label: \"Greenland\",\n textAnchor: \"middle\",\n angle: 270,\n dy: -2,\n dx: 0,\n },\n {\n code: \"IND\",\n label: \"India\",\n textAnchor: \"start\",\n angle: 0,\n dy: 5,\n dx: 2,\n },\n {\n code: \"NER\",\n label: \"Niger\",\n textAnchor: \"middle\",\n angle: 90,\n dy: 11,\n dx: 0\n },\n {\n code: \"USA\",\n label: \"United States\",\n textAnchor: \"start\",\n angle: 45,\n dy: 7,\n dx: 2\n },\n]\n.map(d => Object.assign(d, data.find(f => f.iso === d.code)))","pinned":true,"hidden":false,"mode":"ojs"},{"id":699,"value":"md`### Parsing`","pinned":false,"hidden":false,"mode":"ojs"},{"id":202,"value":"// Year to use for GDP per capita\nyear = \"2020\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":205,"value":"unParsed = un\n .filter(d => d.Time === year && d.ISO3_code)\n .map(d => {\n return {\n country: d.Location,\n iso: d.ISO3_code,\n year,\n pop: +d.TPopulation1Jan * 1e3\n }\n })","pinned":true,"hidden":false,"mode":"ojs"},{"id":134,"value":"// Parse the World Bank data, transforming it from wide to narrow\nworldbankParsed = d3.csvParse(worldbank\n .split(\"\\n\")\n .filter((d, i) => i > 3)\n .join(\"\\n\"))\n .map(d => {\n return {\n country: d[\"Country Name\"],\n iso: d[\"Country Code\"],\n year,\n gdp: +d[year]\n }\n })","pinned":true,"hidden":false,"mode":"ojs"},{"id":381,"value":"// Parse the temperature data, calculating the 10-year average from 2011-2020\ntempsParsed = d3.groups(clone(temps), d => d.country)\n .map(([country, entries]) => {\n const last10 = entries.filter(d => +d.year <= +year && +d.year >= (+year - 9));\n const temp_c = d3.mean(last10, d => +d.temp);\n const temp_f = ((9 / 5) * temp_c) + 32;\n return {\n country,\n iso: entries[0].iso3,\n temp_c,\n temp_f\n }\n })","pinned":true,"hidden":false,"mode":"ojs"},{"id":29,"value":"$30","pinned":true,"hidden":false,"mode":"ojs"},{"id":182,"value":"md`## Generators`","pinned":false,"hidden":false,"mode":"ojs"},{"id":259,"value":"$31","pinned":true,"hidden":false,"mode":"ojs"},{"id":185,"value":"$32","pinned":true,"hidden":false,"mode":"ojs"},{"id":313,"value":"$33","pinned":true,"hidden":false,"mode":"ojs"},{"id":21,"value":"md`## Scales`","pinned":false,"hidden":false,"mode":"ojs"},{"id":24,"value":"x = d3.scaleLinear(d3.extent(data, d => d[scenario]), [ 0, chartwidth ]).nice()","pinned":true,"hidden":false,"mode":"ojs"},{"id":155,"value":"y = d3.scaleLinear(d3.extent(data, d => d[unit]), [ chartheight, 0] ).nice()","pinned":true,"hidden":false,"mode":"ojs"},{"id":293,"value":"color = d3.scaleThreshold(\n [-400, -200, -150, -100, -50, -10, 0, 10, 50, 100, 150, 200, 400],\n [\n \"#007a00\", \"#1c8d25\", \"#34a241\", \"#4db55c\", \"#69c978\", \"#88dc96\", \"#aaeeb4\",\n \"#dcd0ff\", \"#c8b2ff\", \"#b294ff\", \"#9a76ff\", \"#7e58ff\", \"#5936ff\", \"#0000ff\"\n ]\n);","pinned":true,"hidden":false,"mode":"ojs"},{"id":412,"value":"r = d3.scaleSqrt(d3.extent(data, d => d.gdp_percap), [3, 20])","pinned":true,"hidden":false,"mode":"ojs"},{"id":12,"value":"md`## Dimensions`","pinned":false,"hidden":false,"mode":"ojs"},{"id":15,"value":"margin = ({left: unit === \"temp_f\" ? 31 : 35, right: 10, top: 24, bottom: 66})","pinned":true,"hidden":false,"mode":"ojs"},{"id":147,"value":"size = Math.min(width, 640)","pinned":true,"hidden":false,"mode":"ojs"},{"id":17,"value":"chartwidth = size - margin.left - margin.right","pinned":true,"hidden":false,"mode":"ojs"},{"id":19,"value":"chartheight = size * 1.2 - margin.top - margin.bottom","pinned":true,"hidden":false,"mode":"ojs"},{"id":9,"value":"md`## Data`","pinned":false,"hidden":false,"mode":"ojs"},{"id":687,"value":"md`## Utilities`","pinned":false,"hidden":false,"mode":"ojs"},{"id":162,"value":"clone = data => JSON.parse(JSON.stringify(data))","pinned":true,"hidden":false,"mode":"ojs"},{"id":650,"value":"md`## Imports`","pinned":false,"hidden":false,"mode":"ojs"},{"id":187,"value":"import {\n franklinLight,\n franklinBold\n} from \"1dec0e3505bd3624\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":684,"value":"import { toc } from \"@harrystevens/toc\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":315,"value":"geometric = require(\"geometric@2\")","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W34","stdlib":"1","titling":"auto"}