2e:null 2f:["$","title",null,{"children":"NatureScore in U.S. Census tracts, socioeconomic analysis | Climate Lab | Observable"}] 34:Tf14,graphic = () => { const wrapper = d3.create("div"); wrapper.append("style").html(css); wrapper.append("div") .style("font-family", franklinLight) .style("font-size", "18px") .style("margin-bottom", "4px") .text("How NatureScore relates to socioeconomic variables in urban Census tracts"); const chart = wrapper.append("div").selectAll(".chart") .data(data) .join("div") .attr("class", "chart"); const titles = chart.append("div") .attr("class", "chart-titles"); titles.append("div") .attr("class", "chart-title") .text(d => d.title); titles.append("div") .attr("class", "chart-subtitle") .text(d => d.subtitle); const svg = chart.append("svg") .attr("width", chartwidth + margin.left + margin.right) .attr("height", chartheight + margin.top + margin.bottom); const g = svg.append("g") .attr("transform", `translate(${[margin.left, margin.top]})`); const header = svg.append("g") .attr("class", "header") header.selectAll(".column-head") .data([n === 10 ? 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"Pop./sq. mile" : "People per square mile", data: makeQuantiles(d => d.density && d.urban_rural === "urban", d => d.density, "density", n, true), labelMin: "Sparsest", labelMax: "Densest" }, { id: "income", title: "Earnings", subtitle: "Median income ($)", data: makeQuantiles(d => d.median_income && d.urban_rural === "urban", d => d.median_income, "income", n), labelMin: "Poorest", labelMax: "Richest" }, { id: "race", title: "Race", subtitle: width <= 520 ? "Pct. White" : width <= 768 ? "Percent White" : width <= 1020 ? "Pct. White" : "Percent White", data: makeQuantiles(d => d.white_pct && d.urban_rural === "urban", d => d.white_pct, "race", n), labelMin: "Least White", labelMax: "Most White" }, { id: "education", title: "Education", subtitle: width <= 460 ? "Pct. with H.S. diploma" : width <= 520 ? "Pct. with high school diploma" : width <= 640 ? "Percent with high school diploma" : width <= 768 ? "Percent with at least high school diploma" : width <= 900 ? "Pct. with H.S. diploma" : width <= 1020 ? "Pct. with high school diploma" : width <= 1200 ? "Percent with high school diploma" : "Percent with at least high school diploma", data: makeQuantiles(d => d.hs_pct && d.urban_rural === "urban", d => d.hs_pct, "education", n), labelMin: "Least ed.", labelMax: "Most ed." }] .map(d => { d.diff = d3.max(d.data, d0 => d0.naturescore_mean) - d3.min(d.data, d0 => d0.naturescore_mean); return d }) .sort((a, b) => d3.descending(a.diff, b.diff));36:T49f,tractsData = Array.from(tractsNatureScoreMap) .map(([geoid, natureScore]) => { const income = tractsIncomeMap.get(geoid); const population = tractsPopulationMap.get(geoid); const education = tractsEducationMap.get(geoid); const race = tractsRaceMap.get(geoid); const area = tractsAreaMap.get(geoid); const density = population["Estimate!!Total"] / sqMetersToSqMiles(area.tract_aland); // https://www.ers.usda.gov/topics/rural-economy-population/rural-classifications/what-is-rural/ const urban_rural = density >= 1e3 ? "urban" : "rural"; return { geoid, naturescore: +natureScore.naturescore, median_income: income["Estimate!!Households!!Median income (dollars)"], population: population["Estimate!!Total"], density, urban_rural, hs_pct: education["Estimate!!Total!!AGE BY EDUCATIONAL ATTAINMENT!!Population 25 years and over!!High school graduate or higher"] / education["Estimate!!Total!!AGE BY EDUCATIONAL ATTAINMENT!!Population 25 years and over"] * 100, white_pct: race["Estimate!!Total:!!White alone"] / race["Estimate!!Total:"] * 100 } }) .filter(d => d.naturescore >= 0);37:T447,function makeQuantiles(filter, mapper, id, n = 10, reverse = false){ const filtered = tractsData.filter(filter); const numbers = filtered.map(mapper); // Sort the data in ascending order const sortedData = filtered.sort((a, b) => d3.ascending(mapper(a), mapper(b))); // Calculate quantiles const quantiles = d3.range(1, n).map(d => d3.quantile(sortedData, d / n, mapper)); // Group data into quantiles const groupedData = []; for (let i = 0; i < n; i++) { const lower = i === 0 ? -Infinity : quantiles[i - 1]; const upper = i === (n - 1) ? Infinity : quantiles[i]; const entries = sortedData.filter(d => { const v = mapper ? mapper(d) : d return v > lower && v <= upper }); const quantile = reverse ? n - i : i + 1; groupedData.push({ id, quantile, // entries, naturescore_mean: d3.mean(entries, d => d.naturescore), // naturescore_median: d3.median(entries, d => d.naturescore), ntile_min: d3.min(entries, mapper), ntile_max: d3.max(entries, mapper) }); } return groupedData; }38:Tc19,## Terms of use The below terms of use apply to the NatureScore data provided by NatureQuant, Inc. ### DATA TERMS OF USE These Terms of Use constitute an agreement made between you, whether personally or on behalf of an entity, (“you”) and NatureQuant, LLC ("Company," “we,” “us,” or “our”) concerning your access to and use of the data provided between the parties (the “Data”). 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neighborhood”](https://www.washingtonpost.com/climate-environment/interactive/2024/nature-health-maps-neighborhood-city/), published in The Washington Post on April 10, 2024. The NatureScore data was provided by NatureQuant, Inc., and are subject to [restricted terms of use](https://observablehq.com/@climatelab/naturescore-census-tracts-socioeconomic-analysis#terms-of-use). 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fn(d) : d.Geography, d);\n });\n return map;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":705,"value":"function numberOrdinal(x) {\n var j = x % 10,\n k = x % 100;\n return j == 1 && k != 11 ? x + \"st\" :\n \tj == 2 && k != 12 ? x + \"nd\" :\n \tj == 3 && k != 13 ? x + \"rd\" :\n \tx + \"th\";\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":15,"value":"formatCensusCsv = data => {\n const cols = data[0];\n const colKeys = Object.keys(cols).filter(d => d);\n return data\n .slice(1)\n .map(d => {\n const o = {};\n colKeys.forEach((k, i) => {\n const v = d[k];\n const c = cols[k];\n o[c] = i < 2 || c.includes(\"PERCENT\") ? v : +v;\n });\n return o\n })\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":116,"value":"sqMetersToSqMiles = meters => meters * 0.000000386102159;","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":264,"value":"$37","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":832,"value":"$38","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":600,"value":"## Imports","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":844,"value":"import { toc } from \"@climatelab/toc@45\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":745,"value":"import { foreground } from \"@climatelab/contrasting-foreground-text@166\";","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":602,"value":"import { franklinLight } from \"@climatelab/fonts@46\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W39","stdlib":"1","titling":"auto"}} 33:"$32:body"