${data.title[0]}${labels[j] && ` (${labels[j]})`} ${data.title[1]}${labels[i] && ` (${labels[i]})`}

2d:null 2e:["$","title",null,{"children":"Final Project | awsherman | Observable"}] 33:T44c,legend = () => { const k = 24; const arrow = DOM.uid(); return svg` ${d3.cross(d3.range(n), d3.range(n)).map(([i, j]) => svg` ${data.title[0]}${labels[j] && ` (${labels[j]})`} ${data.title[1]}${labels[i] && ` (${labels[i]})`} `)} ${data.title[0]} ${data.title[1]} `; }34:T451,legend2 = () => { const k = 24; const arrow = DOM.uid(); return svg` ${d3.cross(d3.range(n), d3.range(n)).map(([i, j]) => svg` ${data2.title[0]}${labels[j] && ` (${labels[j]})`} ${data2.title[1]}${labels[i] && ` (${labels[i]})`} `)} ${data2.title[0]} ${data2.title[1]} `; }35:T451,legend4 = () => { const k = 24; const arrow = DOM.uid(); return svg` ${d3.cross(d3.range(n), d3.range(n)).map(([i, j]) => svg` ${data4.title[0]}${labels[j] && ` (${labels[j]})`} ${data4.title[1]}${labels[i] && ` (${labels[i]})`} `)} ${data4.title[0]} ${data4.title[1]} `; }36:T451,legend3 = () => { const k = 24; const arrow = DOM.uid(); return svg` ${d3.cross(d3.range(n), d3.range(n)).map(([i, j]) => svg` ${data3.title[0]}${labels[j] && ` (${labels[j]})`} ${data3.title[1]}${labels[i] && ` (${labels[i]})`} `)} ${data3.title[0]} ${data3.title[1]} `; }37:[{"id":"a137981969b1e341c9844ba0045113671e514fac730c1b8904a70725a4ce897d7affea472eb874ff24e3e15dc8c286682758235963000e6adf1040e998b58f5c","href":"https://static.observableusercontent.com/files/a137981969b1e341c9844ba0045113671e514fac730c1b8904a70725a4ce897d7affea472eb874ff24e3e15dc8c286682758235963000e6adf1040e998b58f5c","name":"Census_Covid_Data.csv","type":"text/csv","lastModified":1619466907421.012,"size":88968},{"id":"f95a676323f7f0d8e4d65b152e6829b221e457373f92c7aee94145a0a210553b0e69c486b29ae641d20c844b2968ec5be5e7988744d0154cba26ab6fe4a0154e","href":"https://static.observableusercontent.com/files/f95a676323f7f0d8e4d65b152e6829b221e457373f92c7aee94145a0a210553b0e69c486b29ae641d20c844b2968ec5be5e7988744d0154cba26ab6fe4a0154e","name":"ia_covid19 (2).json","type":"application/json","lastModified":1619466899612.398,"size":257002}] 31:{"id":"7cd7c0d8ba1f2663","slug":"final-project","title":"Final Project","thumbnail":"442d762b9f92c6872857e0d0f3bbf1b761efb5a8e44518e1c90669266d9fd5bd","access_level":"public","create_time":"$D2021-04-26T19:54:31.646Z","update_time":"$D2021-05-02T20:35:23.135Z","view_time":null,"first_public_time":"$D2021-04-28T19:23:15.162Z","has_unpublished_edits":null,"fork":{"id":"eb35c0e027153681","slug":"bivariate-choropleth-mapping","title":"Bivariate Choropleth Mapping","owner":{"id":"a78e502fa6f6c01f","login":"awsherman","name":""}},"stars":1,"starred":false,"owner":{"type":"team","id":"a78e502fa6f6c01f","login":"awsherman","name":"","avatar_url":"https://avatars-next.observableusercontent.com/9129abc5ec33905cb1cb30859bb8a43b281e323f029f3799d79ccfb43a00bb8c","tier":"starter_2024"},"version":795,"publish_version":795,"role":null,"authored":false,"authors":[{"type":"individual","id":"19874f1c64881ed4","login":"awsherman","name":"","avatar_url":"https://avatars-next.observableusercontent.com/9129abc5ec33905cb1cb30859bb8a43b281e323f029f3799d79ccfb43a00bb8c","tier":null}],"members":[{"type":"individual","id":"19874f1c64881ed4","login":"awsherman","name":"","avatar_url":"https://avatars-next.observableusercontent.com/9129abc5ec33905cb1cb30859bb8a43b281e323f029f3799d79ccfb43a00bb8c","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"Final Project","cells":[{"id":0,"value":"md`# Final Project\n\nCovid-19 and Demographic data for Iowa Counties`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":730,"value":"viewof colors = {\n const form = html`

color scheme`;\n form.i.selectedIndex = 1;\n form.oninput = () => form.value = schemes[form.i.selectedIndex].colors;\n form.oninput();\n return form;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":14,"value":"chart = {\n const svg = d3.create(\"svg\")\n .attr(\"viewBox\", [0, 0, width, height]);\n\n svg.append(legend)\n .attr(\"transform\", \"translate(100,500)\");\n\n svg.append(\"g\")\n .selectAll(\"path\")\n .data(topojson.feature(polygons, polygons.objects.ia_covid19).features)\n .join(\"path\")\n .attr(\"fill\", d => color(data.get(d.properties[idAttribute])))\n .attr(\"d\", path)\n .append(\"title\")\n .text(d => `${d.properties[idAttribute]}, ${format(data.get(d.properties[idAttribute]))}`);\n\n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":774,"value":"md` This is a map comparing the percentage of the population that is over the age of 65 to the percent of the population to the confirmed Covid-19 cases for each county in Iowa. Since people over the age of 65 were more vulnerable to the the negative syptoms of the Covid-19 virus, this map shows which communities have the population that both are high risk and were at the highest risk of exposer.You can see in the map that there is a group of Counties in Nothern Iowa where there is a high percentage of the population that is over the age of 65 and or is exposed to the Covid-19 virus. These areas are more rural so them having a high percent of people of the age of 65 is probably because most of these people are people are people who have lived there for their entire life. There is also a group of counties in eastern Iowa with a low percentage of the county having confirmed cases and over the age of 65. Based on this map, these counties probably have seen less negative impact from the virus. `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":359,"value":"// http://www.joshuastevens.net/cartography/make-a-bivariate-choropleth-map/\nschemes = [\n {\n name: \"RdBu\", \n colors: [\n \"#e8e8e8\", \"#e4acac\", \"#c85a5a\",\n \"#b0d5df\", \"#ad9ea5\", \"#985356\",\n \"#64acbe\", \"#627f8c\", \"#574249\"\n ]\n },\n {\n name: \"BuPu\", \n colors: [\n \"#e8e8e8\", \"#ace4e4\", \"#5ac8c8\",\n \"#dfb0d6\", \"#a5add3\", \"#5698b9\", \n \"#be64ac\", \"#8c62aa\", \"#3b4994\"\n ]\n },\n {\n name: \"GnBu\", \n colors: [\n \"#e8e8e8\", \"#b5c0da\", \"#6c83b5\",\n \"#b8d6be\", \"#90b2b3\", \"#567994\",\n \"#73ae80\", \"#5a9178\", \"#2a5a5b\"\n ]\n },\n {\n name: \"PuOr\", \n colors: [\n \"#e8e8e8\", \"#e4d9ac\", \"#c8b35a\",\n \"#cbb8d7\", \"#c8ada0\", \"#af8e53\",\n \"#9972af\", \"#976b82\", \"#804d36\"\n ]\n }\n]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":216,"value":"labels = [\"low\", \"\", \"high\"]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":207,"value":"n = Math.floor(Math.sqrt(colors.length))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":209,"value":"x = d3.scaleQuantile(Array.from(data.values(), d => d[0]), d3.range(n))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":204,"value":"y = d3.scaleQuantile(Array.from(data.values(), d => d[1]), d3.range(n))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":486,"value":"path = d3.geoPath().projection(projection)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":462,"value":"//Rotate the map sets the longitude of origin for our UTM projection.\nprojection = d3.geoTransverseMercator().rotate([94,0]).fitExtent([[10, 10], [width, height]], polygon_features);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":430,"value":"polygon_features = topojson.feature(polygons, polygons.objects.ia_covid19)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":467,"value":"data = Object.assign(new Map(d3.csvParse(await FileAttachment(\"Census_Covid_Data.csv\").text(), ({ia_covid19_county_Name, FIPS, AGE65PLUS_TOT, ia_covid19_county_Confirmed, ia_covid19_county_Deaths, POPESTIMATE}) => [FIPS, [100*+ia_covid19_county_Confirmed/+POPESTIMATE, 100*+AGE65PLUS_TOT/+POPESTIMATE]])), {title: [\"Confirmed Case %\", \"Pop. Over 65\"]})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":483,"value":"idAttribute = \"FIPS\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":8,"value":"polygons = FileAttachment(\"ia_covid19 (2).json\").json()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":427,"value":"height = 610","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":424,"value":"width = 875","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":480,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":477,"value":"color = {\n return value => {\n if (!value) return \"#ccc\";\n let [a, b] = value;\n return colors[y(b) + x(a) * n];\n };\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":513,"value":"formatNum = d3.format(\".1f\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":474,"value":"format = (value) => {\n if (!value) return \"N/A\";\n let [a, b] = value;\n return `${a} ${data.title[0]}${labels[x(a)] && ` (${labels[x(a)]})`}\n${formatNum(b)} ${data.title[1]}${labels[y(b)] && ` (${labels[y(b)]})`}`;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":6,"value":"topojson = require(\"topojson-client@3\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3,"value":"d3 = require(\"d3@5\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":652,"value":"u = d3.scaleQuantile(Array.from(data2.values(), d => d[0]), d3.range(n))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":654,"value":"t = d3.scaleQuantile(Array.from(data2.values(), d => d[1]), d3.range(n))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":656,"value":"data2 = Object.assign(new Map(d3.csvParse(await FileAttachment(\"Census_Covid_Data.csv\").text(), ({ia_covid19_county_Name, FIPS, AGE65PLUS_TOT, ia_covid19_county_Confirmed, ia_covid19_county_Deaths, POPESTIMATE}) => [FIPS, [100*+ia_covid19_county_Deaths/+ia_covid19_county_Confirmed, 100*+AGE65PLUS_TOT/+POPESTIMATE]])), {title: [\"Fatal Cases %\", \"Pop. Over 65\"]})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":658,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":660,"value":"color2 = {\n return value => {\n if (!value) return \"#ccc\";\n let [a, b] = value;\n return colors[t(b) + u(a) * n];\n };\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":662,"value":"format2 = (value) => {\n if (!value) return \"N/A\";\n let [a, b] = value;\n return `${a} ${data2.title[0]}${labels[u(a)] && ` (${labels[u(a)]})`}\n${formatNum(b)} ${data2.title[1]}${labels[t(b)] && ` (${labels[t(b)]})`}`;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":664,"value":"chart2 = {\n const svg = d3.create(\"svg\")\n .attr(\"viewBox\", [0, 0, width, height]);\n\n svg.append(legend2)\n .attr(\"transform\", \"translate(100,500)\");\n\n svg.append(\"g\")\n .selectAll(\"path\")\n .data(topojson.feature(polygons, polygons.objects.ia_covid19).features)\n .join(\"path\")\n .attr(\"fill\", d => color2(data2.get(d.properties[idAttribute])))\n .attr(\"d\", path)\n .append(\"title\")\n .text(d => `${d.properties[idAttribute]}, ${format2(data2.get(d.properties[idAttribute]))}`);\n\n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":780,"value":"md` This is a map comparing the percentage of the population that is over the age of 65 to the percent of the population to the counties that the highest perentage of Covid-19 cases that were fatal for each counties in Iowa. Because of the vulnerability of the age demographic, there should be some connection to counties having a high population of people over the age of 65 and the percentage of cases that were fatal. For this map, you can see some differences in the data when compared to the map above. The clusters from the first map are not in this map, instead the distribution is a bit more all over the place. There is a cluster of low fatal cases and low population percentage over 65 in central Iowa but otherwise there in not much clustering.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":709,"value":"g = d3.scaleQuantile(Array.from(data4.values(), d => d[0]), d3.range(n))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":711,"value":"h = d3.scaleQuantile(Array.from(data4.values(), d => d[1]), d3.range(n))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":713,"value":"data4 = Object.assign(new Map(d3.csvParse(await FileAttachment(\"Census_Covid_Data.csv\").text(), ({ia_covid19_county_Name, FIPS, MEDIAN_AGE_TOT, ia_covid19_county_Confirmed, ia_covid19_county_Deaths, POPESTIMATE}) => [FIPS, [100*+ia_covid19_county_Confirmed/+POPESTIMATE, +MEDIAN_AGE_TOT]])), {title: [\"Confirmed Cases %\", \"Median Age\"]})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":715,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":717,"value":"color4 = {\n return value => {\n if (!value) return \"#ccc\";\n let [a, b] = value;\n return colors[h(b) + g(a) * n];\n };\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":719,"value":"format4 = (value) => {\n if (!value) return \"N/A\";\n let [a, b] = value;\n return `${a} ${data4.title[0]}${labels[g(a)] && ` (${labels[g(a)]})`}\n${formatNum(b)} ${data4.title[1]}${labels[h(b)] && ` (${labels[h(b)]})`}`;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":722,"value":"chart4 = {\n const svg = d3.create(\"svg\")\n .attr(\"viewBox\", [0, 0, width, height]);\n\n svg.append(legend4)\n .attr(\"transform\", \"translate(100,500)\");\n\n svg.append(\"g\")\n .selectAll(\"path\")\n .data(topojson.feature(polygons, polygons.objects.ia_covid19).features)\n .join(\"path\")\n .attr(\"fill\", d => color4(data4.get(d.properties[idAttribute])))\n .attr(\"d\", path)\n .append(\"title\")\n .text(d => `${d.properties[idAttribute]}, ${format4(data4.get(d.properties[idAttribute]))}`);\n\n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":785,"value":"md` This is a map comparing the median age in the every county in Iowa to the percent of the population that had confirmed Covid-19 cases. Median age is another way to look at the age demographics for each county, with higher median ages representing populations with an old population that could be more vulnerable to COVID-19. This map similarly has not a lot of clustering expect in the Northwestern corner of the state. Here we see a cluster of counties with high percentages of confirmed cases but a low median age. These counties are next to another cluster of counties that have high median age and percentage of confirmed cases.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":680,"value":"c = d3.scaleQuantile(Array.from(data3.values(), d => d[0]), d3.range(n))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":684,"value":"e = d3.scaleQuantile(Array.from(data3.values(), d => d[1]), d3.range(n))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":678,"value":"data3 = Object.assign(new Map(d3.csvParse(await FileAttachment(\"Census_Covid_Data.csv\").text(), ({ia_covid19_county_Name, FIPS, MEDIAN_AGE_TOT, ia_covid19_county_Confirmed, ia_covid19_county_Deaths, POPESTIMATE}) => [FIPS, [100*+ia_covid19_county_Deaths/+ia_covid19_county_Confirmed, +MEDIAN_AGE_TOT]])), {title: [\"Fatal Cases %\", \"Median Age\"]})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":686,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":689,"value":"color3 = {\n return value => {\n if (!value) return \"#ccc\";\n let [a, b] = value;\n return colors[e(b) + c(a) * n];\n };\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":693,"value":"format3 = (value) => {\n if (!value) return \"N/A\";\n let [a, b] = value;\n return `${a} ${data3.title[0]}${labels[c(a)] && ` (${labels[c(a)]})`}\n${formatNum(b)} ${data3.title[1]}${labels[e(b)] && ` (${labels[e(b)]})`}`;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":695,"value":"chart3 = {\n const svg = d3.create(\"svg\")\n .attr(\"viewBox\", [0, 0, width, height]);\n\n svg.append(legend3)\n .attr(\"transform\", \"translate(100,500)\");\n\n svg.append(\"g\")\n .selectAll(\"path\")\n .data(topojson.feature(polygons, polygons.objects.ia_covid19).features)\n .join(\"path\")\n .attr(\"fill\", d => color3(data3.get(d.properties[idAttribute])))\n .attr(\"d\", path)\n .append(\"title\")\n .text(d => `${d.properties[idAttribute]}, ${format3(data3.get(d.properties[idAttribute]))}`);\n\n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":788,"value":"md` This is a map comparing the median age in the every county in Iowa to the percent of the percent of confirmed cases that were fatal. The thought process is that since older populations are more vulnerable, that counties with higher median ages may have a higher percentage of fatal cases. In this map we see a cluster of counties with a high percentage of fatal cases and high median age in Western Iowa. This is interestingly right next to a cluster of counties that are low in both factors.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W37","stdlib":"1","titling":"auto"}} 32:"$31:body"