31:T6b7,### Urban vs. Rural When looking at the ratio of households without internet access, rural counties have a ratio of ${format(ratioRuralWithout)} while urban counties have a ratio of ${format(ratioUrbanWithout)}, a difference of ${format(ratioRuralWithout - ratioUrbanWithout)}. This is likely to at least be partially explained by some rural areas not having the necessary telecommunications infrastructure required for connecting to the internet. While this shows that proportionately, households in rural areas are affected more than urban areas by lack of internet access, it isn't the whole story. When looking at the total number of households that lack internet access, there is a much greater number in counties that have mostly urban populations: - In counties that have mostly _urban populations_, there are ${absoluteUrbanWithout.toLocaleString("en-US")} total households that lack internet access of any kind. - In counties that have mostly or entirely _rural populations_, there are ${absoluteRuralWithout.toLocaleString("en-US")} total households that lack internet access of any kind. This makes sense given that urban areas are more densely populated. However, it still begs the question why are so many households lacking internet access in areas that are more likely to have telecommunications infrastructure? The answer likely lies in socioeconomic status: cities that have higher populations of low income and non-whites are more likely to have more households without internet access. Mostly likely this is due to financial and other barriers. **Note:** that urban and rural designation is only provided by the Census Bureau for the fifty-two US states and not its territories.32:T818,{ const svg = d3 .create("svg") .attr("viewBox", [0, 0, config.width, config.height]) .style("background", "white") .attr("font-family", "sans-serif") .attr("font-size", 12); const data = JSON.parse(JSON.stringify(forceData)); const format = d3.format(".3s"); const simulation = d3 .forceSimulation(data) .force("x", d3.forceX().strength(0.1)) .force("y", d3.forceY().strength(1)) .force("center", d3.forceCenter()) .force( "collide", d3.forceCollide().radius((d) => rScale(d.total)) ) .tick(1000); // title svg .append("text") .attr("x", 6) .attr("y", 6) .attr("dy", "0.85em") .attr("font-size", 14) .attr("font-weight", "bold") .text("Number of Households ") .append("tspan") .text("Without Internet Access") .attr("fill", config.fill); const groups = svg .selectAll("g.designation") .data(simulation.nodes()) .join("g") .attr( "transform", (d) => `translate(${config.width / 2 + d.x}, ${config.height / 2 + d.y + 12})` ) .classed("designation", true) .attr("text-anchor", "middle"); groups .append("circle") .attr("r", (d) => rScale(d.total)) .attr("cx", 0) .attr("cy", 0) .attr("fill", "steelblue"); groups .append("circle") .attr("r", (d) => rScale(d.without)) .attr("cx", 0) .attr("cy", (d) => rScale(d.total) - rScale(d.without)) .attr("fill", "#fff"); groups .append("text") .attr("x", 0) .attr("y", 0) .attr("dy", "-0.35em") .attr("fill", "#fff") .style("text-transform", "capitalize") .text((d) => d.designation) .style("font-size", 18); groups .append("text") .attr("x", 0) .attr("y", (d) => -rScale(d.total) + 14) .attr("fill", "#fff") .text((d) => format(d.total)); groups .append("text") .attr("x", 0) .attr("y", (d) => rScale(d.total) - rScale(d.without)) .attr("dy", ".35em") .attr("fill", config.fill) .text((d) => format(d.without)); return svg.node(); }33:[] 2f:{"title":"ACS Internet Access Exploration","cells":[{"id":0,"value":"# ACS Internet Access Exploration\n\nExploratory analysis of internet access using data from the U.S. Census ${dataYear} 5 year ACS. See the following notebooks for more on how the data used in this analysis was sourced and parsed: \n- [`@clhenrick/dataset-internet-access`](https://observablehq.com/@clhenrick/dataset-internet-access) \n- [`@clhenrick/dataset-us-counties-urban-rural-designation`](https://observablehq.com/@clhenrick/dataset-us-counties-urban-rural-designation)","pinned":false,"hidden":false,"mode":"md"},{"id":185,"value":"toc(\"h2,h3,h4,h5,h6\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":115,"value":"md`## Distribution of the Ratio of Households by County Without Internet Access`","pinned":false,"hidden":false,"mode":"ojs"},{"id":236,"value":"All U.S. Counties (including U.S. territories)","pinned":false,"hidden":false,"mode":"md"},{"id":34,"value":"Plot.plot({\n x: {\n round: true,\n label: \"Ratio Without Internet Access\"\n },\n y: {\n grid: true\n },\n marks: [\n Plot.rectY(\n data,\n Plot.binX({ y: \"count\" }, { x: \"ratio_without\", fill: \"steelblue\" })\n ),\n Plot.ruleY([0])\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":240,"value":"Counties considered to have a mostly urban population:","pinned":false,"hidden":false,"mode":"md"},{"id":230,"value":"Plot.plot({\n x: {\n round: true,\n label: \"Ratio Without Internet Access\"\n },\n y: {\n grid: true\n },\n marks: [\n Plot.rectY(\n urban,\n Plot.binX({ y: \"count\" }, { x: \"ratio_without\", fill: \"steelblue\" })\n ),\n Plot.ruleY([0])\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":245,"value":"Counties considered to have a mostly rural population:","pinned":false,"hidden":false,"mode":"md"},{"id":232,"value":"Plot.plot({\n x: {\n round: true,\n label: \"Ratio Without Internet Access\"\n },\n y: {\n grid: true\n },\n marks: [\n Plot.rectY(\n rural,\n Plot.binX({ y: \"count\" }, { x: \"ratio_without\", fill: \"steelblue\" })\n ),\n Plot.ruleY([0])\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":119,"value":"md`## Distribution of the Ratio of Households by County With Internet Access`","pinned":false,"hidden":false,"mode":"ojs"},{"id":249,"value":"All U.S. Counties (including U.S. territories)","pinned":false,"hidden":false,"mode":"md"},{"id":40,"value":"Plot.plot({\n x: {\n round: true,\n label: \"Ratio With Internet Access\"\n },\n y: {\n grid: true\n },\n marks: [\n Plot.rectY(\n data,\n Plot.binX({ y: \"count\" }, { x: \"ratio_with\", fill: \"steelblue\" })\n ),\n Plot.ruleY([0])\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":259,"value":"Counties considered to have a mostly urban population:","pinned":false,"hidden":false,"mode":"md"},{"id":253,"value":"Plot.plot({\n x: {\n round: true,\n label: \"Ratio With Internet Access\"\n },\n y: {\n grid: true\n },\n marks: [\n Plot.rectY(\n urban,\n Plot.binX({ y: \"count\" }, { x: \"ratio_with\", fill: \"steelblue\" })\n ),\n Plot.ruleY([0])\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":263,"value":"Counties considered to have a mostly rural population:","pinned":false,"hidden":false,"mode":"md"},{"id":255,"value":"Plot.plot({\n x: {\n round: true,\n label: \"Ratio With Internet Access\"\n },\n y: {\n grid: true\n },\n marks: [\n Plot.rectY(\n rural,\n Plot.binX({ y: \"count\" }, { x: \"ratio_with\", fill: \"steelblue\" })\n ),\n Plot.ruleY([0])\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":101,"value":"## Summary Stats for all U.S. Counties\n","pinned":false,"hidden":false,"mode":"md"},{"id":127,"value":"md`\n### No Internet Access\n- There are ${absoluteWithout.toLocaleString()} total households in the U.S. that lack internet access of any kind. \n- The ratio of households in the U.S. without internet access is ${ratioWithout.toLocaleString()}\n- The highest ratio of households with no internet access in all U.S. counties is ${format(\n maxWithout\n)}\n- 10% of counties have a ratio of ${format(\n topTenthWithout\n)} or higher for households with no internet access (${\n data.filter((d) => d.ratio_without >= topTenthWithout).length\n} counties total).\n- The mean is ${format(meanWithout)}\n- The median is ${format(medianWithout)}\n- The lowest is ${format(minWithout)}\n- The difference between the highest and lowest ratios is ${format(\n maxWithout - minWithout\n)}\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":173,"value":"### With Internet Access (Subscription or Not)\n- There are ${absoluteWith.toLocaleString(\n \"en-US\"\n)} total households in the U.S. that have access to the internet of some kind.\n- The ratio of households in the U.S. with internet access is ${ratioWith.toLocaleString()}\n- Of those households, ${absoluteWithCellular.toLocaleString(\n \"en-US\"\n)} only have internet access through a cellular data plan.\n- The highest ratio of households with at least some level of internet access is ${format(\n maxWith\n)}\n- 10% of counties have a ratio of ${format(\n topTenthWith\n)} or higher for households with some level of internet access (${\n data.filter((d) => d.ratio_with >= topTenthWith).length\n} counties total).\n- The mean is ${format(meanWith)}\n- The median is ${format(medianWith)}\n- The lowest is ${format(minWith)}\n- The difference between the highest and lowest is ${format(maxWith - minWith)}","pinned":false,"hidden":false,"mode":"md"},{"id":313,"value":"$31","pinned":false,"hidden":false,"mode":"md"},{"id":76,"value":"absoluteWithout = d3.sum(data, (d) => d.without_internet_access)","pinned":false,"hidden":false,"mode":"ojs"},{"id":82,"value":"absoluteWith = d3.sum(data, (d) => d.with_subscription + d.without_subscription)","pinned":false,"hidden":false,"mode":"ojs"},{"id":168,"value":"absoluteWithCellular = d3.sum(data, (d) => d.with_cellular_only)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1015,"value":"ratioWithout = d3.sum(joined, (d) => d.without_internet_access) /\n d3.sum(joined, (d) => d.total_households)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1017,"value":"ratioWith = d3.sum(\n joined,\n (d) => d.with_subscription + d.without_subscription\n) / d3.sum(joined, (d) => d.total_households)","pinned":false,"hidden":false,"mode":"ojs"},{"id":189,"value":"meanWithout = d3.mean(data, (d) => d.ratio_without)","pinned":false,"hidden":false,"mode":"ojs"},{"id":192,"value":"meanWith = d3.mean(data, (d) => d.ratio_with)","pinned":false,"hidden":false,"mode":"ojs"},{"id":85,"value":"medianWithout = d3.median(data, (d) => d.ratio_without)","pinned":false,"hidden":false,"mode":"ojs"},{"id":91,"value":"medianWith = d3.median(data, (d) => d.ratio_with)","pinned":false,"hidden":false,"mode":"ojs"},{"id":93,"value":"maxWith = d3.max(data, (d) => d.ratio_with)","pinned":false,"hidden":false,"mode":"ojs"},{"id":95,"value":"maxWithout = d3.max(data, (d) => d.ratio_without)","pinned":false,"hidden":false,"mode":"ojs"},{"id":97,"value":"minWith = d3.min(data, (d) => d.ratio_with)","pinned":false,"hidden":false,"mode":"ojs"},{"id":99,"value":"minWithout = d3.min(data, (d) => d.ratio_without)","pinned":false,"hidden":false,"mode":"ojs"},{"id":272,"value":"topTenthWithout = d3.quantileSorted(sortedWithout, 0.9)","pinned":false,"hidden":false,"mode":"ojs"},{"id":276,"value":"topTenthWith = d3.quantileSorted(sortedWith, 0.9)","pinned":false,"hidden":false,"mode":"ojs"},{"id":321,"value":"maxUrbanWithout = d3.max(urban, (d) => d.ratio_without)","pinned":false,"hidden":false,"mode":"ojs"},{"id":324,"value":"maxRuralWithout = d3.max(rural, (d) => d.ratio_without)","pinned":false,"hidden":false,"mode":"ojs"},{"id":328,"value":"maxUrbanWith = d3.max(urban, (d) => d.ratio_with)","pinned":false,"hidden":false,"mode":"ojs"},{"id":331,"value":"maxRuralWith = d3.max(rural, (d) => d.ratio_with)","pinned":false,"hidden":false,"mode":"ojs"},{"id":377,"value":"medianUrbanWithout = d3.median(urban, (d) => d.ratio_without)","pinned":false,"hidden":false,"mode":"ojs"},{"id":375,"value":"medianRuralWithout = d3.median(rural, (d) => d.ratio_without)","pinned":false,"hidden":false,"mode":"ojs"},{"id":382,"value":"meanUrbanWithout = d3.mean(urban, (d) => d.ratio_without)","pinned":false,"hidden":false,"mode":"ojs"},{"id":384,"value":"meanRuralWithout = d3.mean(rural, (d) => d.ratio_without)","pinned":false,"hidden":false,"mode":"ojs"},{"id":333,"value":"absoluteUrbanWithout = d3.sum(urban, (d) => d.without_internet_access)","pinned":false,"hidden":false,"mode":"ojs"},{"id":335,"value":"absoluteRuralWithout = d3.sum(rural, (d) => d.without_internet_access)","pinned":false,"hidden":false,"mode":"ojs"},{"id":993,"value":"ratioUrbanWithout = d3.sum(urban, (d) => d.without_internet_access) /\n d3.sum(urban, (d) => d.total_households)","pinned":false,"hidden":false,"mode":"ojs"},{"id":998,"value":"ratioRuralWithout = d3.sum(rural, (d) => d.without_internet_access) /\n d3.sum(rural, (d) => d.total_households)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1075,"value":"// where 80% of counties fall into\nd3.rollup(\n joined\n .filter((d) => d.designation)\n .filter((d) => d.ratio_without >= 0.11 && d.ratio_without < 0.333333),\n (v) => v.length,\n (d) => d.designation\n)","pinned":true,"hidden":false,"mode":"ojs"},{"id":414,"value":"md`## Choropleth Maps\n\nThese choropleth maps help us see the geographic distribution of internet access. They are inverses of one another, the first map in red shows the ratio of no internet access while the second shows the ratio of some level of internet access (subscription or not). Both these maps use a [sequential quantile scale](https://github.com/d3/d3-scale#quantile-scales) for mapping the color to ratio of internet access.\n\nSee [the notebook SWD: Declutter and Focus](https://observablehq.com/@clhenrick/swd-declutter-and-focus) for a follow up on how the colors for these maps can be improved to help communicate a clear story about the \"where\" of internet access.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":434,"value":"md`### Ratio of Households Without Internet Access`","pinned":false,"hidden":false,"mode":"ojs"},{"id":416,"value":"choroplethWithoutAccess = drawChoroplethMap({\n data: dataChoroplethWithout,\n colorScale: scaleQuantileWithout,\n tooltipContents,\n zoomEnabled: false,\n borderEnabled: false\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":470,"value":"legendWithoutAccess = legend({\n color: scaleQuantileWithout,\n tickFormat: \".2f\",\n title: \"Ratio of households without internet access\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":441,"value":"md`### Ratio of Households With Internet Access`","pinned":false,"hidden":false,"mode":"ojs"},{"id":444,"value":"choroplethWithAccess = drawChoroplethMap({\n data: dataChoroplethWith,\n colorScale: scaleQuantileWith,\n tooltipContents,\n zoomEnabled: false,\n borderEnabled: false\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":478,"value":"legendWithAccess = legend({\n color: scaleQuantileWith,\n tickFormat: \".2f\",\n title: \"Ratio of households with internet access\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":420,"value":"scaleQuantileWithout = d3.scaleSequentialQuantile(\n dataChoroplethWithout.map((d) => d.value),\n d3.interpolateReds\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":448,"value":"scaleQuantileWith = d3.scaleSequentialQuantile(\n dataChoroplethWith.map((d) => d.value),\n d3.interpolateBlues\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":418,"value":"dataChoroplethWithout = data.map(({ fips, ratio_without }) => ({\n id: fips,\n value: ratio_without\n}))","pinned":false,"hidden":false,"mode":"ojs"},{"id":450,"value":"dataChoroplethWith = data.map(({ fips, ratio_with }) => ({\n id: fips,\n value: ratio_with\n}))","pinned":false,"hidden":false,"mode":"ojs"},{"id":429,"value":"tooltipContents = ({ id, value }) =>\n `${fipsToCounty.get(id)}
${format(value)}`","pinned":false,"hidden":false,"mode":"ojs"},{"id":517,"value":"## Proportional Visualizations\n\nI recently saw an interesting type of bubble chart in an article by the [South China Morning Post](https://multimedia.scmp.com/infographics/news/world/article/3131214/covid19-vaccine-access/index.html) that uses circle packing to show the proportion of a country's population that has been vaccinated. Parent (e.g. country or region) circles are sized by their total populations. The inner circles are sized by the number of people who have been vaccinated. Countries with inner circles that are more similar in size to their parent countries indicate a higher vaccination rate. \n\nI thought that this type of visualization might be useful for showing the proportion of households without internet access in both rural and urban areas as an alternative to a pie chart. The benefit of this chart is that it allows for comparing both the proportions and absolute numbers. However after making it I feel that pie charts might be more easily understood. ","pinned":false,"hidden":false,"mode":"md"},{"id":762,"value":"viewof rMax = Inputs.range([10, 200], {\n value: config.maxR,\n step: 1,\n label: \"Max Radius\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":632,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":521,"value":"rScale = d3\n .scaleSqrt()\n .domain([0, d3.sum(urban, (d) => d.total_households)])\n .range([0, rMax])","pinned":false,"hidden":false,"mode":"ojs"},{"id":634,"value":"config = ({\n width: width / 2,\n height: Math.floor(width / 3),\n fill: d3.schemeReds[5][2],\n maxR: Math.floor(width / 9)\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":691,"value":"forceData = d3.map(grouped, ([designation, data]) => ({\n designation,\n total: d3.sum(data, (d) => d.total_households),\n without: d3.sum(data, (d) => d.without_internet_access)\n}))","pinned":false,"hidden":false,"mode":"ojs"},{"id":546,"value":"grouped = d3.group(\n joined.filter((d) => d.designation),\n (d) => d.designation\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":942,"value":"stacked_bar_charts = md`## Stacked Bar Charts\n\nInstead of visualizing the difference between rural and urban counties using the proportional circle charts above, we could also use stacked bar charts. The first bar chart shows the absolute values while the second shows relative values. Both charts help communicate that while rural areas are proportionately worse off than urban areas in terms of lack of internet access, urban areas have almost four times the number of households without internet access as rural areas. \n\nI find it compelling that even though two charts are used below versus one above to communicate the same idea, that the bar charts take up less space. Another advantage of the bar charts is that they make it easier to compare the differences of \"with\" and \"without\" internet access more easily. No wonder the lowly bar chart is often so popular a choice when visualizing data!\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":971,"value":"html`

Number of Households With and Without Internet Access

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":944,"value":"Plot.plot({\n y: {\n label: null\n },\n x: {\n tickFormat: \"s\",\n label: \"households\"\n },\n marks: [Plot.barX(barData, { y: \"designation\", x: \"value\", fill: \"access\" })]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":960,"value":"Plot.plot({\n y: {\n label: null\n },\n x: {\n tickFormat: \"%\",\n label: \"households\"\n },\n marks: [\n Plot.barX(\n barData,\n Plot.stackX({\n y: \"designation\",\n x: \"value\",\n fill: \"access\",\n offset: \"expand\"\n })\n )\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":947,"value":"barData = {\n const ruralWith = d3.sum(\n rural,\n (d) => d.with_subscription + d.without_subscription\n );\n const ruralWithout = d3.sum(rural, (d) => d.without_internet_access);\n const urbanWith = d3.sum(\n urban,\n (d) => d.with_subscription + d.without_subscription\n );\n const urbanWithout = d3.sum(urban, (d) => d.without_internet_access);\n return [\n {\n designation: \"Rural\",\n access: \"with\",\n value: ruralWith\n },\n {\n designation: \"Rural\",\n access: \"without\",\n value: ruralWithout\n },\n {\n designation: \"Urban\",\n access: \"with\",\n value: urbanWith\n },\n {\n designation: \"Urban\",\n access: \"without\",\n value: urbanWithout\n }\n ];\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1032,"value":"md`## Beeswarm Plot\n\nBeeswarm plots of both urban and rural households to emphasize individual points in a distribution instead of binning them like in a histogram.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1041,"value":"// beeswarmChartUrban","pinned":false,"hidden":false,"mode":"ojs"},{"id":1062,"value":"// beeswarmChartRural","pinned":false,"hidden":false,"mode":"ojs"},{"id":1043,"value":"beeswarmHeight = 500","pinned":false,"hidden":false,"mode":"ojs"},{"id":1037,"value":"beeswarmDataUrban = urban.map(({ name, ratio_without }) => ({\n name,\n value: ratio_without\n}))","pinned":false,"hidden":false,"mode":"ojs"},{"id":1056,"value":"beeswarmDataRural = rural.map(({ name, ratio_without }) => ({\n name,\n value: ratio_without\n}))","pinned":false,"hidden":false,"mode":"ojs"},{"id":1035,"value":"import { chart as beeswarmChartUrban } with {\n beeswarmDataUrban as data,\n beeswarmHeight as height\n} from \"@d3/beeswarm-ii\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":1060,"value":"import { chart as beeswarmChartRural } with {\n beeswarmDataRural as data,\n beeswarmHeight as height\n} from \"@d3/beeswarm-ii\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":932,"value":"md`---`","pinned":false,"hidden":false,"mode":"ojs"},{"id":157,"value":"md`## Data Wrangling`","pinned":false,"hidden":false,"mode":"ojs"},{"id":132,"value":"format = d3.format(\".3f\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":303,"value":"md`Sorted values for ratio with and ratio without to speed up quantile calculations:`","pinned":false,"hidden":false,"mode":"ojs"},{"id":267,"value":"sortedWith = data\n .map((d) => d.ratio_with)\n .filter((d) => d !== null && !isNaN(d))\n .sort(d3.ascending)","pinned":true,"hidden":false,"mode":"ojs"},{"id":270,"value":"sortedWithout = data\n .map((d) => d.ratio_without)\n .filter((d) => d !== null && !isNaN(d))\n .sort(d3.ascending)","pinned":true,"hidden":false,"mode":"ojs"},{"id":213,"value":"md`**Data join on urban / rural designation:**`","pinned":false,"hidden":false,"mode":"ojs"},{"id":295,"value":"data.length","pinned":true,"hidden":false,"mode":"ojs"},{"id":226,"value":"urban.length + rural.length","pinned":true,"hidden":false,"mode":"ojs"},{"id":221,"value":"urban = joined.filter((d) => d.designation === \"urban\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":223,"value":"rural = joined.filter((d) => d.designation === \"rural\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":205,"value":"joined = {\n const urbanRuralLookup = new Map(\n designationUrbanRural.map(({ geoid, pct_rural_2010 }) => [\n geoid,\n pct_rural_2010 >= 50 ? \"rural\" : \"urban\"\n ])\n );\n\n return data.map((d) => {\n const designation = urbanRuralLookup.get(d.fips);\n return {\n ...d,\n designation\n };\n });\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":496,"value":"data","pinned":true,"hidden":false,"mode":"ojs"},{"id":494,"value":"fipsToCounty = new Map(data.map(({ fips, name }) => [fips, name]))","pinned":true,"hidden":false,"mode":"ojs"},{"id":105,"value":"md`## Dependencies`","pinned":false,"hidden":false,"mode":"ojs"},{"id":182,"value":"import { toc } from \"@nebrius/indented-toc\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":201,"value":"import { data as designationUrbanRural } from \"@clhenrick/dataset-us-counties-urban-rural-designation\";","pinned":false,"hidden":false,"mode":"ojs"},{"id":2,"value":"import { data, year as dataYear } from \"@clhenrick/dataset-internet-access\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":411,"value":"import { drawChoroplethMap } from \"@clhenrick/reusable-choropleth-map\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":468,"value":"import { legend } from \"@d3/color-legend\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":21,"value":"import { Plot, d3 } from \"@observablehq/plot\"","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W33","stdlib":"1","titling":"auto"}