31: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 ? "Decile" : n === 5 ? "Quintile" : "Quantile", "NatureScore"]) .join("text") .attr("class", "column-head") .attr("font-family", franklinLight) .attr("font-size", 14) .attr("font-weight", "bold") .attr("x", (_, i) => i * margin.left) .attr("y", 10) .text(d => d); header.append("line") .attr("shape-rendering", "crispEdges") .attr("stroke", "#2a2a2a") .attr("transform", `translate(0, ${margin.top - 4})`) .attr("x2", chartwidth + margin.left + margin.right) const axis = g.append("g") .attr("class", "axis y-axis"); const tick = axis.selectAll(".tick") .data(d => d.data) .join("g") .attr("class", "tick") .attr("transform", d => `translate(0, ${y(d.quantile)})`); tick.append("text") .attr("dy", -2) .attr("font-family", franklinLight) .attr("font-size", 14) .attr("text-anchor", "start") .attr("x", -margin.left) .attr("y", y.bandwidth() / 2) .text((d, i) => { if (i === 0) { return data.find(f => f.id === d.id).labelMin } else if (i === n - 1) { return data.find(f => f.id === d.id).labelMax } else { return numberOrdinal(d.quantile); } }) tick.append("text") .attr("dy", 12) .attr("fill", "#808080") .attr("font-family", franklinLight) .attr("font-size", 12) .attr("text-anchor", "start") .attr("x", -margin.left) .attr("y", y.bandwidth() / 2) .text((d, i) => { const f = formatters[d.id] return `${f(d.ntile_min)}-${f(d.ntile_max)}` }) tick.append("line") .attr("shape-rendering", "crispEdges") .attr("stroke", "#d5d5d5") .attr("x1", -margin.left) .attr("x2", chartwidth) .attr("transform", "translate(0, -4)"); const bar = g.selectAll(".bar") .data(d => d.data) .join("g") .attr("class", "bar") .attr("transform", d => `translate(0, ${y(d.quantile)})`); bar.append("rect") .attr("fill", "#f5f5f5") .attr("height", y.bandwidth()) .attr("width", x(100)) bar.append("rect") .attr("fill", d => fill(d.naturescore_mean)) .attr("stroke", d => stroke(d.naturescore_mean)) .attr("height", y.bandwidth()) .attr("width", d => x(d.naturescore_mean)) bar.append("text") .attr("dx", 5) .attr("dy", 6) .attr("fill", d => d3.color(fill(d.naturescore_mean)).darker()) .attr("font-family", franklinLight) .attr("font-size", 16) .attr("font-weight", "bold") .attr("x", d => x(d.naturescore_mean)) .attr("y", y.bandwidth() / 2) .text(d => Math.round(d.naturescore_mean)) return wrapper.node(); }32:T62b,data = [{ id: "density", title: "Density", subtitle: width <= 380 ? "Pop./sq. mile" : "People per square mile", data: makeQuantiles(d => d.density && d.urban_rural === "urban", d => d.density, "density", n, true), labelMin: "Sparsest", labelMax: "Densest" }, { id: "income", title: "Earnings", subtitle: "Median income ($)", 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));33: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);34: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; }35: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”). You agree that by accessing this data, you have read, understood, and agreed to be bound by all of these Terms of Use. #### LIMITED USE/LICENSE We hereby grant you a limited non-exclusive, non-sublicensable, non-transferable license to use the Data for your research and academic publication purposes. #### LIMITATIONS OF LIABILITIES We, notwithstanding the cause or duration, will not be liable for any inaccuracies, omissions, delays, or any other defects arising out of the use of Data. #### ASSIGNMENT You may not assign rights or obligations under this agreement without the prior written consent of the Company. #### BRANDING REQUIREMENTS Any publications or dissemination of research created with this Data must include attribution to NatureQuant, LLC. #### PROPRIETARY RIGHTS NatureQuant owns all intellectual property rights (including, without limitation, all copyrights, trademarks, patents, and trade secrets) in and to all versions of NatureScoreTM and NatureDoseTM, including, without limitation, all software and content. One or more U.S. Patents may apply to the NatureScoreTM and NatureDoseTM technologies. #### CONFIDENTIALITY NatureQuant and you agree and understand that each party (the “Receiving Party”) may receive information from the other party, its respective parents and subsidiaries, and affiliates (collectively, the “Disclosing Party”); that information is “Confidential Information”. Each party agrees that (i) the Receiving Party shall not disclose any such Confidential Information to any third party; (ii) all Confidential Information shall remain the exclusive property of the Disclosing Party; and (iii) the Receiving Party shall return or destroy all copies of the Disclosing Party's Confidential Information upon request of the Disclosing Party. Notwithstanding the foregoing, Confidential Information does not include any information to the extent it (i) is or becomes a part of the public domain through no act or omission on the part of the Receiving Party, (ii) is in the Receiving Party's possession, without knowledge of an associated confidentiality obligation before disclosure, (iii) is disclosed to the Receiving Party by a third party having no associated confidentiality obligation, or (iv) is independently developed without access or reference to the Confidential Information. #### LIMITED RELATIONSHIP These Terms are not, and shall not be deemed to constitute, a partnership or joint venture between the parties, and neither party nor any of their respective directors, officers, employees, or agents shall, by virtue of the performance of their obligations under these Terms, be deemed to be an agent or employee of the other. 36:[{"id":"055c05516ded445620d52fe718e49453c6c6588ac5d09e14b94f65cdaf06e655229c14f74b491709e5b8ceaed23bd72f10a03aa32c91089780b048fb61c2cdc2","href":"https://static.observableusercontent.com/files/055c05516ded445620d52fe718e49453c6c6588ac5d09e14b94f65cdaf06e655229c14f74b491709e5b8ceaed23bd72f10a03aa32c91089780b048fb61c2cdc2","name":"ACSDT5Y2020.B01003_2024-04-01T120557.zip","type":"application/zip","lastModified":1712161226983.928,"size":1107862},{"id":"e02c4d86aaea6f423305efe415f4c192a9be3bed242e6bcc44f477a8888b59f604fa65345dbc332d0d356d096a5bf331588e37834ecff229b7382f5a0296e83a","href":"https://static.observableusercontent.com/files/e02c4d86aaea6f423305efe415f4c192a9be3bed242e6bcc44f477a8888b59f604fa65345dbc332d0d356d096a5bf331588e37834ecff229b7382f5a0296e83a","name":"ACSDT5Y2020.B02001_2024-04-01T163953.zip","type":"application/zip","lastModified":1712161226983.928,"size":3346604},{"id":"581739fbdc8b2ba42e65fa608d076edf59fb8d6b3cc7b4b3501a84938eb4cf0086628a418d1b14961bbbbd893328834c53d53f6845591fdc4ae594702f5b1c53","href":"https://static.observableusercontent.com/files/581739fbdc8b2ba42e65fa608d076edf59fb8d6b3cc7b4b3501a84938eb4cf0086628a418d1b14961bbbbd893328834c53d53f6845591fdc4ae594702f5b1c53","name":"tracts-area.csv","type":"text/csv","lastModified":1712161226983.928,"size":2140258},{"id":"f6eedfd6511918f30d324c2990d76ca333c4dbdf73e7a4ae2bfe90236043da7f89c49758e5f7679e7cf2e60e3bc13982572cf52b041feace0dc7862a9dd146a5","href":"https://static.observableusercontent.com/files/f6eedfd6511918f30d324c2990d76ca333c4dbdf73e7a4ae2bfe90236043da7f89c49758e5f7679e7cf2e60e3bc13982572cf52b041feace0dc7862a9dd146a5","name":"ACSST5Y2020.S1501-Data.csv","type":"text/csv","lastModified":1712161226983.928,"size":9437437},{"id":"41009c683342fbe02484a859997b7a0f3e0acf45d78b77518433f23ede8b5cff5bf7977cfe4ffa7353353c1ffe1b0b01b1a2dda3f07be7a543207f71c56b8456","href":"https://static.observableusercontent.com/files/41009c683342fbe02484a859997b7a0f3e0acf45d78b77518433f23ede8b5cff5bf7977cfe4ffa7353353c1ffe1b0b01b1a2dda3f07be7a543207f71c56b8456","name":"ACSST5Y2020.S1901_2024-04-01T121302.zip","type":"application/zip","lastModified":1712161226983.928,"size":18709726},{"id":"e56f1b652cd01a324793809c5c4a7ae39129622e12144019f43f196ab132d8baa17512e764edae5066dd53d2b6c0d28e25e8756cf033b779c4e7587c3298708d","href":"https://static.observableusercontent.com/files/e56f1b652cd01a324793809c5c4a7ae39129622e12144019f43f196ab132d8baa17512e764edae5066dd53d2b6c0d28e25e8756cf033b779c4e7587c3298708d","name":"tracts_naturescore_20230731.csv","type":"text/csv","lastModified":1712161226983.928,"size":20204068}] 2f:{"title":"NatureScore in U.S. Census tracts, socioeconomic analysis","cells":[{"id":0,"value":"# NatureScore in U.S. Census tracts, socioeconomic analysis","pinned":false,"hidden":false,"mode":"md"},{"id":867,"value":"A version of this chart appeared in [“Mapping America’s access to nature, neighborhood by 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). The rest of the data are from the 2020 decennial census from the U.S. Census Bureau.","pinned":false,"hidden":false,"mode":"md"},{"id":789,"value":"graphic()","pinned":false,"hidden":false,"mode":"ojs"},{"id":848,"value":"toc({ selector: \"h2\" })","pinned":false,"hidden":false,"mode":"ojs"},{"id":787,"value":"## Graphics","pinned":false,"hidden":false,"mode":"md"},{"id":538,"value":"$31","pinned":true,"hidden":false,"mode":"ojs"},{"id":540,"value":"## Styles","pinned":false,"hidden":false,"mode":"md"},{"id":542,"value":"css = `\n.chart {\n display: inline-block;\n margin-left: ${padInner / 2}px;\n margin-right: ${padInner / 2}px;\n width: calc(${100 / cols}% - ${pad}px);\n}\n.chart:nth-of-type(${cols}n - ${cols - 1}) {\n margin-left: 0px;\n}\n.chart:nth-of-type(${cols}n) {\n margin-right: 0px;\n}\n.chart-titles {\n margin-bottom: 8px;\n}\n.chart-title, .chart-subtitle {\n font-family: ${franklinLight};\n}\n.chart-title {\n font-size: 16px;\n font-weight: bold;\n}\n.chart-subtitle {\n color: #808080;\n font-size: 16px;\n margin-top: -6px;\n}\n`","pinned":true,"hidden":false,"mode":"ojs"},{"id":582,"value":"## Color","pinned":false,"hidden":false,"mode":"md"},{"id":584,"value":"colorDomain = d3.range(0, 120, 20)","pinned":true,"hidden":false,"mode":"ojs"},{"id":586,"value":"fills = [\"#ca8be7\", \"#dfbbdb\", \"#f2e9d6\", \"#a1d785\", \"#78b35a\"]","pinned":true,"hidden":false,"mode":"ojs"},{"id":588,"value":"strokes = [\"#a974c1\", \"#bb9cb7\", \"#cac3b3\", \"#87b46f\", \"#64964b\"];","pinned":true,"hidden":false,"mode":"ojs"},{"id":590,"value":"fill = d3.scaleQuantile().domain(colorDomain).range(fills)","pinned":true,"hidden":false,"mode":"ojs"},{"id":592,"value":"stroke = d3.scaleQuantile().domain(colorDomain).range(strokes)","pinned":true,"hidden":false,"mode":"ojs"},{"id":532,"value":"## Scales","pinned":false,"hidden":false,"mode":"md"},{"id":534,"value":"x = d3.scaleLinear([0, 100], [0, chartwidth])","pinned":true,"hidden":false,"mode":"ojs"},{"id":536,"value":"y = d3.scaleBand(d3.range(1, n + 1), [0, chartheight]).paddingInner(0.2)","pinned":true,"hidden":false,"mode":"ojs"},{"id":530,"value":"## Dimensions","pinned":false,"hidden":false,"mode":"md"},{"id":505,"value":"margin = ({left: 73, right: 0, top: 18, bottom: 1})","pinned":true,"hidden":false,"mode":"ojs"},{"id":507,"value":"padInner = 16","pinned":true,"hidden":false,"mode":"ojs"},{"id":509,"value":"cols = width <= 768 ? 2 : 4","pinned":true,"hidden":false,"mode":"ojs"},{"id":512,"value":"padTotal = padInner * (cols - 1)","pinned":true,"hidden":false,"mode":"ojs"},{"id":514,"value":"pad = padTotal / cols","pinned":true,"hidden":false,"mode":"ojs"},{"id":516,"value":"basewidth = 1 / cols * width - pad","pinned":true,"hidden":false,"mode":"ojs"},{"id":524,"value":"rowheight = 36","pinned":true,"hidden":false,"mode":"ojs"},{"id":520,"value":"chartwidth = basewidth - margin.left - margin.right","pinned":true,"hidden":false,"mode":"ojs"},{"id":526,"value":"chartheight = rowheight * n","pinned":true,"hidden":false,"mode":"ojs"},{"id":51,"value":"## Data","pinned":false,"hidden":false,"mode":"md"},{"id":347,"value":"viewof n = Inputs.range([2, 50], { value: 5, step: 1, label: \"Quantiles:\" })","pinned":true,"hidden":false,"mode":"ojs"},{"id":808,"value":"formatters = ({\n density: d => d > 1e4 ? `${Math.round(d / 1e3)}K` : `${+(d / 1e3).toFixed(1)}K`,\n income: d => `${Math.round(d / 1e3)}K`,\n race: d => {\n const rounded = Math.round(d);\n return `${rounded === 0 ? \"<1\" : rounded === 100 ? \"99+\" : rounded}%`\n },\n education: d => {\n const rounded = Math.round(d);\n return `${rounded === 0 ? \"<1\" : rounded === 100 ? \"99+\" : rounded}%`\n },\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":476,"value":"$32","pinned":true,"hidden":false,"mode":"ojs"},{"id":107,"value":"$33","pinned":true,"hidden":false,"mode":"ojs"},{"id":5,"value":"tractsNatureScore = FileAttachment(\"tracts_naturescore_20230731.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":152,"value":"tractsNatureScoreMap = makeMap(tractsNatureScore, d => d.geoid)","pinned":true,"hidden":false,"mode":"ojs"},{"id":112,"value":"tractsArea = FileAttachment(\"tracts-area.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":129,"value":"tractsAreaMap = makeMap(tractsArea, d => d.tract_geoid)","pinned":true,"hidden":false,"mode":"ojs"},{"id":7,"value":"// From: https://data.census.gov./table/ACSST5Y2020.S1901?\ntractsIncomeZip = FileAttachment(\"ACSST5Y2020.S1901_2024-04-01T121302.zip\").zip()","pinned":true,"hidden":false,"mode":"ojs"},{"id":9,"value":"// Column: Estimate!!Households!!Median income (dollars)\ntractsIncome = formatCensusCsv(await tractsIncomeZip.file(\"ACSST5Y2020.S1901-Data.csv\").csv())","pinned":true,"hidden":false,"mode":"ojs"},{"id":127,"value":"tractsIncomeMap = makeMap(tractsIncome, d => d.Geography.split(\"US\")[1])","pinned":true,"hidden":false,"mode":"ojs"},{"id":56,"value":"// From: https://data.census.gov./table/ACSST5Y2022.S1501\ntractsEducation = formatCensusCsv(await FileAttachment(\"ACSST5Y2020.S1501-Data.csv\").csv())","pinned":true,"hidden":false,"mode":"ojs"},{"id":125,"value":"tractsEducationMap = makeMap(tractsEducation, d => d.Geography.split(\"US\")[1])","pinned":true,"hidden":false,"mode":"ojs"},{"id":62,"value":"// https://data.census.gov./table/ACSDT5Y2020.B01003\ntractsPopulationZip = FileAttachment(\"ACSDT5Y2020.B01003_2024-04-01T120557.zip\").zip()","pinned":true,"hidden":false,"mode":"ojs"},{"id":64,"value":"// Column: Estimate!!Total\ntractsPopulation = formatCensusCsv(await tractsPopulationZip.file(\"ACSDT5Y2020.B01003-Data.csv\").csv())","pinned":true,"hidden":false,"mode":"ojs"},{"id":91,"value":"tractsPopulationMap = makeMap(tractsPopulation, d => d.Geography.split(\"US\")[1])","pinned":true,"hidden":false,"mode":"ojs"},{"id":231,"value":"// https://data.census.gov./table/ACSDT5Y2020.B02001\ntractsRaceZip = FileAttachment(\"ACSDT5Y2020.B02001_2024-04-01T163953.zip\").zip()","pinned":true,"hidden":false,"mode":"ojs"},{"id":383,"value":"tractsRace = formatCensusCsv(await tractsRaceZip.file(\"ACSDT5Y2020.B02001-Data.csv\").csv())","pinned":true,"hidden":false,"mode":"ojs"},{"id":385,"value":"tractsRaceMap = makeMap(tractsRace, d => d.Geography.split(\"US\")[1])","pinned":true,"hidden":false,"mode":"ojs"},{"id":49,"value":"## Utilities","pinned":false,"hidden":false,"mode":"md"},{"id":121,"value":"makeMap = (data, fn) => {\n const map = new Map();\n data.forEach(d => {\n map.set(fn ? fn(d) : d.Geography, d);\n });\n return map;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"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"},{"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"},{"id":116,"value":"sqMetersToSqMiles = meters => meters * 0.000000386102159;","pinned":true,"hidden":false,"mode":"ojs"},{"id":264,"value":"$34","pinned":true,"hidden":false,"mode":"ojs"},{"id":832,"value":"$35","pinned":false,"hidden":false,"mode":"md"},{"id":600,"value":"## Imports","pinned":false,"hidden":false,"mode":"md"},{"id":844,"value":"import { toc } from \"@climatelab/toc@45\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":745,"value":"import { foreground } from \"@climatelab/contrasting-foreground-text@166\";","pinned":true,"hidden":false,"mode":"ojs"},{"id":602,"value":"import { franklinLight } from \"@climatelab/fonts@46\"","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W36","stdlib":"1","titling":"auto"}