Student at the University of Iowa studying Sustainability Science with a certificate in GIS.
Public
Edited
May 7, 2023
Choroplethi = Plot.plot({
projection: projectioni,
marks: [
Plot.geo(polygonsi, {
stroke: "white",
fill: (d) => Age_fill.get(d.properties.gen),
title: (d) =>
`${d.properties.gen} ${format(Age_2021M.get(d.properties.gen))}`
}),
Plot.geo(polygonsi, { strokeWidth: 0.05 }),
Plot.geo(polygonsi, { stroke: "#fff", strokeWidth: .5 })
],
color: {
scheme: "Blues",
type: "quantile",
label: "Average Age by region",
legend: true,
domain: [40, 50]
}
})
csv_datai = FileAttachment("AveAgesdiff.csv").csv().then(data => data.map(d => ({region: d.gen, year: +d.jahr, age: +d.wert})))
//datai = Object.assign(new Map(d3.csvParse(await FileAttachment("AveAgesdiff.csv").text(), ({gen, jahr, wert}) => [gen, [+jahr, +wert]])), {title: ["Year", "Average Age"]})
Age_2021 = csv_datai.filter(d => d.year === 2021)
Age_2021M = new Map(Age_2021.map(({region, age}) => [region, age]))
Germanyi = FileAttachment("Ages0to19.json").json()
polygonsi = topojson.feature(Germanyi, Germanyi.objects.Ages_0to19_wgs84)
format = d3.format(".1f")
projectioni = d3i.geoTransverseMercator().rotate([-5,0]).fitExtent([[10, 10], [850, 400]], polygonsi);
sliderYear = csv_datai.filter(d => d.year === Select)
Age_fill = new Map(sliderYear.map(({region, age}) => [region, age]))
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