2f:T485,## Notes I'm not particularly an expert on the Gini Index or, for that matter, even economics in general. I've simply read a bit about it [on Wikipedia](https://en.wikipedia.org/wiki/Gini_coefficient) and [on Investopedia](https://www.investopedia.com/terms/g/gini-index.asp). In particular, I can't vouch for the importance of the thresholds used in specifying the classifications. Roughly, I came up with the thresholds after reading the Investopedia article, which states that: - Only 16 countries in the world have a Gini Index larger than 0.5 and - The Gini Index for the whole US is 0.41, which is quite high for developed nations. I also referred to the distribution of the Gini Index across the counties as shown in the bar chart below. Taking those into account, I decided to classify the Gini Index according to the following table: |Below US average|Around US average| Above US average| High|Very High| |:---:|:---:|:---:|:---:|:---:|:---:|:---:| |${tex`G<0.4`}|${tex`0.4 i % 2 == 0)) : d3.scaleSequential(d3.interpolateReds).domain([0.3, 0.55]); let counties = svg.append("g"); counties .selectAll("path") .data(nc_counties.features) .join("path") .attr("d", path) .attr("fill", (o) => color_scale(gini_by_county.get(o.properties.COUNTYFP))) .attr("stroke", "black") .attr("stroke-width", 1) .on("pointerenter", function () { d3.select(this).attr("stroke-width", 2.5).raise(); }) .on("pointerleave", function () { counties.selectAll("path").attr("stroke-width", 1); }) .each(function (d) { let name = d.properties.NAME; let countyfp = d.properties.COUNTYFP; let gini = gini_by_county.get(d.properties.COUNTYFP); tippy(this, { content: `

County: ${name}

${ include_fips ? `

COUNTYFP: ${countyfp}

` : "" }

Gini Index: ${gini}

`, allowHTML: true, theme: "light-border" }); }); nccities.forEach(function (c) { let [x, y] = projection([c.Longitude, c.Latitude]); c.x = x; c.y = y; }); let city_group = svg.append("g"); city_group .selectAll("circle") .data(nccities) .join("circle") .attr("cx", (city) => city.x) .attr("cy", (city) => city.y) .attr("r", 4) .attr("fill", "yellow") .attr("stroke", "black") .style("pointer-events", "none"); div .append("div") .selectAll("div") .data(nccities) .join("div") .style("position", "absolute") .style("font-size", `${d3.min([14, 0.0175 * w])}px`) .style("left", (o) => (o.Name == "Winston‐Salem" ? null : `${o.x}px`)) .style("right", (o) => (o.Name == "Winston‐Salem" ? `${w - o.x}px` : null)) .style("top", (o) => (o.Name == "Durham" ? null : `${o.y}px`)) .style("bottom", (o) => (o.Name == "Durham" ? `${h - o.y}px` : null)) .text((o) => o.Name) .style("border", "solid 1px black") .style("background-color", "rgba(256,256,256,0.7)") .style("pointer-events", "none"); div .append("div") .style("position", "absolute") .style("left", "10px") .style("bottom", "10px") .append(() => shade_by == "classification" ? Legend( d3.scaleOrdinal( ["< avg", "avg", "> avg", "high", "very high"], d3.schemeReds[9].filter((_, i) => i % 2 == 0) ), { title: "Gini index" } ) : Legend(d3.scaleSequential(d3.interpolateReds).domain([0.3, 0.55])) ); return div.node(); }31:[{"id":"858e121222dfb4ce0dd29895c6e7095f53d6b4536489be8cdd5b91d3161725f1682795b160c1efc5099f249197da425e80ef34cbf5701238dae6d9562d94e4ad","href":"https://static.observableusercontent.com/files/858e121222dfb4ce0dd29895c6e7095f53d6b4536489be8cdd5b91d3161725f1682795b160c1efc5099f249197da425e80ef34cbf5701238dae6d9562d94e4ad","name":"nc_counties.json","type":"application/json","lastModified":1680290929796.244,"size":134363},{"id":"45f6dceed178907a8ca134bcf2e889d1fa46703b73ea5494786cadf91ed8f48d6d0e07393fb8749a52e4bd6322d7d3a219cf092eaab2c1ae6579f87ceb9eda03","href":"https://static.observableusercontent.com/files/45f6dceed178907a8ca134bcf2e889d1fa46703b73ea5494786cadf91ed8f48d6d0e07393fb8749a52e4bd6322d7d3a219cf092eaab2c1ae6579f87ceb9eda03","name":"GiniIndexNCCounties.csv","type":"text/csv","lastModified":1680292481660.363,"size":4719},{"id":"107c05823685c5a969ffbba50cf84dfc6d5f440b88e5fc1e42a87d19338147f9eb6511e3ffabe3914dcc4e928e02d41674b9396d73481c4ec2e180a0b9d8f3d8","href":"https://static.observableusercontent.com/files/107c05823685c5a969ffbba50cf84dfc6d5f440b88e5fc1e42a87d19338147f9eb6511e3ffabe3914dcc4e928e02d41674b9396d73481c4ec2e180a0b9d8f3d8","name":"ncCities@1.csv","type":"text/csv","lastModified":1680355598169.468,"size":439}] 2d:{"title":"Income inequality in North Carolina","cells":[{"id":0,"value":"# Income inequality in North Carolina\n## an illustration of the Gini Index for NC counties\n\nThe map below displays the 100 counties in North Carolina shaded according to their Gini Index - a widely used measure of income inequality. It also illustrates the difference between shading a map by classification vs raw values.\n\nThe data all comes from the US Census Bureau. If you're interested in the process of finding that kind of data, you can read about it [in this notebook](https://observablehq.com/d/e91ffeec38b91b19).","pinned":false,"hidden":false,"mode":"md"},{"id":338,"value":"viewof shade_by = Inputs.radio([\"values\", \"classification\"], {\n value: \"classification\",\n label: \"Shade by:\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":415,"value":"map = make_map()","pinned":false,"hidden":false,"mode":"ojs"},{"id":374,"value":"$2f","pinned":false,"hidden":false,"mode":"md"},{"id":305,"value":"barplot = Plot.plot({\n width: d3.min([width, 900]),\n y: {\n grid: true\n },\n x: {\n domain: [0.3, 0.6]\n },\n marks: [\n Plot.rectY(gini_data, Plot.binX({ y: \"count\" }, { x: \"gini\" })),\n Plot.ruleY([0])\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":148,"value":"## Code","pinned":false,"hidden":false,"mode":"md"},{"id":406,"value":"$30","pinned":true,"hidden":false,"mode":"ojs"},{"id":4,"value":"nc_counties = {\n let map_data = await FileAttachment(\"nc_counties.json\").json();\n let nc_counties = topojson.feature(map_data, map_data.objects.nc_counties);\n return nc_counties;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":46,"value":"gini_by_county = {\n gini_data.forEach((o) => (o.gini = parseFloat(o.B19083_001E)));\n return d3.rollup(\n gini_data,\n (a) => a[0].gini,\n (o) => o.county\n );\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":273,"value":"nccities = FileAttachment(\"ncCities@1.csv\").csv({ typed: true })","pinned":true,"hidden":false,"mode":"ojs"},{"id":152,"value":"gini_data = FileAttachment(\"GiniIndexNCCounties.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":121,"value":"import { Legend } from \"@d3/color-legend\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":232,"value":"tippy_style = html``","pinned":true,"hidden":false,"mode":"ojs"},{"id":189,"value":"tippy = require(\"tippy.js@6\")","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W31","stdlib":"1","titling":"auto"}