36:["$","title",null,{"children":"Racial Colocation (Shareable) | Dylan | Observable"}] 3e:T9c4,viewof CountyMap = { let value = null; const svg = d3.create("svg") .attr("viewBox", [0, 0, 975, 610]) // draw counties svg.append("g") .selectAll("path") .data(topojson.feature(geography, geography.objects.counties).features) .enter().append("path") .attr("d", path) // !!!! HERE !!!! This is the conditional check for the population and death count threshholds .attr("fill", d => results[+d.id][raceVariable] === 1 ? scale(results[+d.id].countOfDays) : 'rgb(210,210,210)') // .style("opacity", d=> results[+d.id][raceVariable] === 1 ? 1 : 0.2) .attr('id', data => data.properties.name) .attr('class', 'county'); // state outline svg.append("g") .selectAll("path") .data(topojson.feature(geography, geography.objects.states).features) .enter().append("path") .attr("d", path) .attr("fill", 'none') .attr("stroke", d => 'white') .attr('id', d=> d.properties.name) .attr('class', 'state'); // state background svg.append("path") .datum(topojson.mesh(geography, geography.objects.states, (a, b) => a !== b)) .attr("fill", "none") .attr("stroke", d => 'white') .attr("stroke-linejoin", "round") .attr("pointer-events", "none") .attr("d", path); // // thanks! https://observablehq.com/@duynguyen1678/choropleth-with-tooltip const tooltip = svg.append("g") svg .selectAll(".county") .on("touchmove mousemove", function(event, d) { tooltip.call( callout, `${d.properties.name} ` ); tooltip.attr("transform", `translate(${d3.pointer(event, this)})`); d3.select(this) .raise(); }) .on("touchend mouseleave", function() { tooltip.call(callout, null); d3.select(this) .attr("stroke", null) .lower(); }); svg.append("g") .attr("transform", "translate(610,20)") .append(() => legend({color:scale, title: `Days spent in the top quantile of COVID-19 deaths per capita`, width: 260 })); svg.append("text") .attr("x", 595) .attr("y", 605) .attr("font-size", "0.7em") .text('Data Source: CDC COVID-19 Data, ACS 2019 5-Year Population Estimates'); const outline = svg.append("path") .attr("fill", "none") .attr("stroke", "black") .attr("stroke-linejoin", "round") .attr("pointer-events", "none"); return Object.assign(svg.node(), {value: null}); }3f:T440,{ let raceVariables = ['hispP','blackP','whiteP'] let tempResults = Object.values(results) let raceDayAverages = [] for (let i=0; i < raceVariables.length; i++){ let currRaceVariable = raceVariables[i] let count = 0; let sum = []; let per100k = 0; let pop = 0; for (let n=0; n a-b)[sum.length/2]; } else { medianDays = (sum.sort((a,b) => a-b)[Math.floor(sum.length/2)]+sum.sort((a,b) => a-b)[Math.ceil(sum.length/2)])/2; } raceDayAverages.push({ 'County ACS Racial Group': plainNames[currRaceVariable], 'County Median Days top 20%': medianDays, 'County Deaths Per 100k': Math.round(per100k/pop*100*100000)/100 }) } return table(raceDayAverages) }40:[] 3a:{"id":"dced17b114581cb7","slug":"racial-colocation-data-generation/2","title":"Racial Colocation (Shareable)","thumbnail":"cdc0155679f7d41cb87a2f3e93e6fa9456120587cacbbb244fbf3989bb0e89be","access_level":"public","create_time":"$D2021-04-02T01:01:05.522Z","update_time":"$D2021-04-02T14:57:50.779Z","view_time":null,"first_public_time":"$D2021-04-02T01:10:36.761Z","has_unpublished_edits":null,"fork":{"id":"dbc1c1848d2b8d2c","slug":"racial-colocation-data-generation","title":"Racial Colocation Data Generation","owner":{"id":"02a75d28143bf487","login":"nofurtherinformation","name":"Dylan"}},"stars":0,"starred":false,"owner":{"type":"team","id":"02a75d28143bf487","login":"nofurtherinformation","name":"Dylan","avatar_url":"https://avatars-next.observableusercontent.com/a494170e1e25c8f92f54468c30fecf4bcf81095a7268e37af8e633e7fd8d22ba","tier":"starter_2024","bio":"Senior Software Engineer at the Center for Spatial Data Science, UChicago. Lead engineer on the US Covid Atlas ","followed_by_you":false,"follows_you":false},"version":1092,"publish_version":1092,"role":null,"authored":false,"authors":[{"type":"individual","id":"54c23d6cacd69e20","login":"nofurtherinformation","name":"Dylan","avatar_url":"https://avatars-next.observableusercontent.com/a494170e1e25c8f92f54468c30fecf4bcf81095a7268e37af8e633e7fd8d22ba","tier":null,"bio":"Senior Software Engineer at the Center for Spatial Data Science, UChicago. Lead engineer on the US Covid Atlas ","followed_by_you":false,"follows_you":false}],"members":[{"type":"individual","id":"54c23d6cacd69e20","login":"nofurtherinformation","name":"Dylan","avatar_url":"https://avatars-next.observableusercontent.com/a494170e1e25c8f92f54468c30fecf4bcf81095a7268e37af8e633e7fd8d22ba","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"titling":"auto","title":"Racial Colocation (Shareable)","cells":[{"id":0,"value":"md`# Racial Colocation (Shareable)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":968,"value":"md`#### ACS Racial Group`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":938,"value":"// quartile, quintile, decile, etc \nviewof raceVariable = select(['hispP','blackP','whiteP'])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":954,"value":"md`## Days in the top 20% of COVID-19 deaths:
Counties with ${Math.round(popThresholds[raceVariable]*100)/100}% or more ${plainNames[raceVariable]}`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":911,"value":"$3e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":953,"value":"$3f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1013,"value":"md`___`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":840,"value":"md`## Get population threshold percentages for Dummy Variables`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":739,"value":"popThresholds = {\n let thresholds = {}\n for (let i=0; i +o[populationColumns[i]])\n const response = await gdaProxy.QuantileBreaks(+quantileNumbers, populationVariableData)\n thresholds[populationColumns[i]] = response.breaks[+quantileNumbers-1]\n }\n return thresholds\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":961,"value":"plainNames = {\n return {\n \"whiteP\":\"White People\",\n \"blackP\":\"Black or African American People\",\n \"amIndP\":\"American Indian or Native American People\",\n \"asianP\":\"Asian American People\",\n \"pacIsP\":\"Pacific Islander or Native Hawaiian People\",\n \"hispP\":\"Hispanic or Latinx People\",\n \"otherP\":\"ACS 'Other' People\",\n \"noHSP\":\"Non-Hispanic or Latinx People\",\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":845,"value":"md`## Create Table with FIPS codes, Days in Top quantile of deaths, and dummy variables for racial makeup`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":916,"value":"scale = d3.scaleThreshold().domain([50,100,150,200,250,300,350]).range(['#ffffcc',\n'#ffeda0',\n'#fed976',\n'#feb24c',\n'#fd8d3c',\n'#fc4e2a',\n'#e31a1c',\n'#b10026',])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":767,"value":"results = {\n let fipsObj = {}\n populationData.forEach(row => {\n let tempObj = {\n countOfDays: 0,\n fips: +row.GEOID\n }\n for (const property in row){\n if (property in popThresholds){\n if (+row[property] >= +popThresholds[property]) {\n tempObj[property] = 1\n } \n }\n }\n fipsObj[+row.GEOID] = tempObj\n })\n \n \n const columnDates = Object.keys(normalizedDeathData[0]).slice(1,)\n \n for (let i=0; i o[columnDates[i]]);\n const response = await gdaProxy.QuantileBreaks(+quantileNumbers, values)\n const threshold = response.breaks[+quantileNumbers-1]\n \n for (let n=0; n= threshold) {\n fipsObj[+normalizedDeathData[n].fips].countOfDays += 1\n }\n }\n }\n \n return fipsObj\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":323,"value":"// quartile, quintile, decile, etc \nviewof quantileNumbers = select([5,10,20,100])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":344,"value":"md`\n___\n## Import Data`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":272,"value":"cdcDeathData = d3.csv(`https://gist.githubusercontent.com/nofurtherinformation/977077f4e1874333f570515007537ebb/raw/fde4cc565c4e6ce8e4200ef63fc23438792ac9e9/cdc_deaths.csv`)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":261,"value":"rawPopulationData = d3.csv('https://gist.githubusercontent.com/nofurtherinformation/28547271cf378dc19984c9aeb4673b7f/raw/6614436c28e4c0b11d915b1216e1b1cb09ec190f/DS01_2018_C.csv')","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":727,"value":"populationData = {\n const cdcGeoids = cdcDeathData.map(row => +row.fips)\n return rawPopulationData.filter(row => cdcGeoids.includes(+row['GEOID']))\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":118,"value":"geography = fetch('https://cdn.jsdelivr.net/npm/us-atlas@3/counties-albers-10m.json').then(r => r.json())","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":306,"value":"// get the columns from the first data to select\npopulationColumns = Object.keys(populationData[0]).slice(3,)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":134,"value":"md`\n___\n## Assemble Data\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1016,"value":"totalDeaths = {\n let tempResults = cdcDeathData\n let perCapitaDeaths = {}\n for (let i=0; i +f.GEOID == +vals[0])?.totPopE\n }\n }\n return perCapitaDeaths\n \n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":248,"value":"normalizedDeathData = {\n // declare return data object\n let returnData = [];\n \n // loop through CDC rows of counties\n for (let i=0; i +feature.GEOID == +currentGEOID)\n \n // if missing population data, skip\n if (countyData === undefined) {\n continue\n }\n \n const countyPopulation = countyData?.totPopE\n \n // list of date columns\n const tempKeys = Object.keys(cdcDeathData[i]).slice(1,)\n \n // loop through date columns to calculate 7-day average of deaths per 100k\n for (let n=0; n