2b:null 2c:["$","title",null,{"children":"Bivariate Choropleth - SDOH Variables | McKinsey CRC | Observable"}] 31:Ta5f,chart = { // const svg = d3.create("svg") d3.selectAll('#sdoh-map-container').remove() const svg = d3.select('#svg-container').append('svg').attr('id', 'sdoh-map-container') // .attr('role', 'img') .attr('aria-label', 'Map of US counties that can be filtered by CVI prevalence per 100000 people') .attr('aria-description', 'Layers for poverty rate and access to transportation can be activated, with each layer bucketing counties into high-low poverty and good-poor transportation access') .attr('focusable', 'true') .attr("viewBox", [0, 0, 975, 610]) const tooltipWidth = 350 // draw counties svg.append("g") .selectAll("path") .data(topojson.feature(us, us.objects.counties).features) .join("path") .attr("fill", d => color(data.get(d.id))) .attr("d", path) .attr("stroke", "#ffffff80") .on('touchmove mousemove', function(event, d) { if (fullData.get(d.id)) { const name = d.properties.name; const [x, y] = d3.pointer(event, this); // stroke around hovered county d3.select(this).attr('stroke', '#000000').attr("stroke-width", "2px").raise() // show the tooltip if hovering over a county with data tooltip .style("opacity", 1) .style("left", `${event.offsetX - tooltipWidth / 2 - 4}px`) .style("top", `${event.offsetY + 30}px`) .html(`${d.properties.name} County, ${fullData.get(d.id).state}
Est. CVI prevalence: ${Math.round(fullData.get(d.id).cvi)} per 100k

${(povertyFilter || transportFilter) ? format(data.get(d.id), d.id, d): ''}` ); } }) .on('touchend mouseleave', function () { tooltip.style("opacity", 0).style("display", "null"); d3.select(this) .attr("stroke", "#ffffff80") // return to original outline .attr("stroke-width", "1px") .lower(); }); // draw states svg.append("path") .datum(topojson.mesh(us, us.objects.states, (a, b) => a !== b)) .attr("fill", "none") .attr("stroke", "black") .attr("stroke-linejoin", "round") .attr("d", path); // append tooltip to parent div const tooltip = d3.select('#svg-container').append('div') .style("width", `${tooltipWidth}px`) .style("pointer-events", "none") .style("display", null) .style("opacity", 0) .classed("tooltip-bivariate-map", true); return svg.node(); }32:T645,wrangleData = (raw) => { return raw.map(d=>({ name: d.county, county_fips: (""+d.county_fips).length < 5 ? "0"+d.county_fips : ""+d.county_fips, region: d.region == "midest" ? "midwest" : d.region, state: d.state_abbr, poverty: +d.sdoh_poverty_rate, povertyCat : +d.sdoh_poverty_rate < .13 ? "1-low" : +d.sdoh_poverty_rate < .18 ? "2-med" : "3-high", povertyBool : +d.sdoh_poverty_rate >= .18 ? true : false, freeLunch: +d.sdoh_children_on_free_or_reduced_lunch, freeLunchBool: +d.sdoh_children_on_free_or_reduced_lunch < .61 ? false : true, freeLunchCat: +d.sdoh_children_on_free_or_reduced_lunch < .47 ? "1-low" : +d.sdoh_children_on_free_or_reduced_lunch < .61 ? "2-med" : "3-high", transportation: +d.sdoh_transportation_access_score_algorex, transportBool: +d.sdoh_transportation_access_score_algorex < 5 ? false : true, transportCat: +d.sdoh_transportation_access_score_algorex < 5 ? "1-low" : +d.sdoh_transportation_access_score_algorex == 5 ? "2-med" : "3-high", poverty50Pct: +d.sdoh_below_50pct_federal_poverty_level, cvi: Math.round(+d.combined_cvi_prevalence_ped_100k * 100)/100, cviCount: +d.combined_cvi_pt_count, cviCat: +d.combined_cvi_prevalence_ped_100k < 95? "1-low" : +d.combined_cvi_prevalence_ped_100k < 165 ? "2-med" : "3-high", physicians: +d.sdoh_primary_care_physicians_per_100k_population, distToProviderDiagnosed: +d.diagnosed_avg_min_distance_provider_or_inst, distToProviderUndiagnosed: +d.undiagnosed_avg_min_distance_provider_or_inst, population: +d.age024_tot, //...d })).filter(d=>d.name!="null" && d.region!="null" ) }33:[{"id":"6b1776f5a0a0e76e6428805c0074a8f262e3f34b1b50944da27903e014b409958dc29b03a1c9cc331949d6a2a404c19dfd0d9d36d9c32274e6ffbc07c11350ee","href":"https://static.observableusercontent.com/files/6b1776f5a0a0e76e6428805c0074a8f262e3f34b1b50944da27903e014b409958dc29b03a1c9cc331949d6a2a404c19dfd0d9d36d9c32274e6ffbc07c11350ee","name":"counties-albers-10m.json","type":"application/octet-stream","lastModified":1686683766824.781,"size":795407}] 2f:{"id":"153c97b98b46ca71","slug":null,"title":"Bivariate Choropleth - SDOH Variables","thumbnail":"8f6fbe901b8399c53282fe1f8de8f800ab24f43abfa6e9a4d3687a8c2fb7a7b5","access_level":"public","create_time":"$D2023-06-13T19:16:06.824Z","update_time":"$D2023-06-30T12:20:03.792Z","view_time":null,"first_public_time":"$D2023-06-14T15:42:41.044Z","has_unpublished_edits":null,"fork":{"id":"8b1da0468b5f1e92","slug":"bivariate-choropleth","title":"Bivariate choropleth","owner":{"id":"863951e3ebe4c0ae","login":"d3","name":"D3"}},"stars":0,"starred":false,"owner":{"type":"team","id":"d95a79bbf45d3125","login":"mckinsey-crc","name":"McKinsey CRC","avatar_url":"https://avatars-next.observableusercontent.com/ded2c89bc9b13414aa56ed539b9f78f8baf7bbf429dc5346687403785f4fb3ff","tier":"pro","bio":"Interactive data displays to inform decisions on protecting lives and livelihoods amid the COVID-19 crisis"},"version":1347,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"c097745b3b212897","login":"tuchowska","name":"Monika Tuchowska","avatar_url":"https://avatars-next.observableusercontent.com/09caa02f9916abbb16b1df5949e9eeda374531f41967f85c9027d9a181d45a08","tier":null,"bio":""}],"members":[{"type":"team","id":"d95a79bbf45d3125","login":"mckinsey-crc","name":"McKinsey CRC","avatar_url":"https://avatars-next.observableusercontent.com/ded2c89bc9b13414aa56ed539b9f78f8baf7bbf429dc5346687403785f4fb3ff","tier":"pro","role":"editor"},{"type":"individual","id":"c097745b3b212897","login":"tuchowska","name":"Monika Tuchowska","avatar_url":"https://avatars-next.observableusercontent.com/09caa02f9916abbb16b1df5949e9eeda374531f41967f85c9027d9a181d45a08","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":7,"collection":null,"type":"notebook","body":{"titling":"auto","title":"Bivariate Choropleth - SDOH Variables","cells":[{"id":0,"value":"md`# Bivariate Choropleth - SDOH Variables`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":730,"value":"import {Legend, Swatches} from \"@d3/color-legend\"\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":423,"value":"viewof csvfile = Inputs.file({label: \"CSV file\", accept: \".csv\", required: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":842,"value":"filterTitle = html`Filter counties`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":504,"value":"viewof cviFilter = Inputs.range(d3.extent([50,500]), {value: 200, step: 50, label: \"Show counties with an estimated CVI prevalence of at least:\", 'aria-label': 'test aria label'})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":874,"value":"horzLine = html`

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