Published
Edited
Feb 27, 2020
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md`# Assignment 1: Percent Population 5 and Younger in Alaska`
d3 = require("d3@5")
topojson = require("topojson-client@3")
Alaska = FileAttachment("Alaska.json").json()
county_features = topojson.feature(Alaska, Alaska.objects.Alaska)
csv_data = d3.csvParse(await FileAttachment("Alaska@1.csv").text(),({FIPS, AGE_UNDER5, POP2010}) => [FIPS, [+AGE_UNDER5, +AGE_UNDER5/+POP2010]])
childpct = Array.from(csv_data.values(), d => d[1][1])
data = Object.assign(new Map(csv_data), {title: ["Percent Population of Alaska: 5 and Younger"]})
md`# Linear Scale (Unclassed)`
linear = d3.scaleLinear()
.domain([d3.min(childpct),d3.max(childpct)])
.range(["white", "green"])
chart(numericSort(childpct), linear)
md`# Quantile Classification`
quantile = d3.scaleQuantile()
.domain(childpct)
.range(["white", "#99d594", "green"])
chart(numericSort(childpct), quantile)
md`# Jenks Natural Breaks Classification`
jenks = d3
.scaleThreshold()
.domain([0.06444582814445828, 0.09325355890241387, 0.12106180453143853])
.range(["white", "#99d594", "green"])
chart(numericSort(childpct), jenks)
md`# Equal Interval Classification (Quantize)`
quantize = d3.scaleQuantize()
.domain([d3.min(childpct),d3.max(childpct)])
.range(["white", "#99d594", "green"])
chart(numericSort(childpct), quantize)
md`# Threshold`
threshold = d3.scaleThreshold()
.domain([0.06444582814445828, 0.09325355890241387, 0.12106180453143853])
.range(["white", "#99d594", "green"])
chart(numericSort(childpct), threshold)
showScaleGrouping(childpct, {
scaleQuantile: quantile,
scaleThreshold: threshold,
scaleJenks: jenks,
scaleQuantize: quantize,
scaleQuantizeNice: quantize.copy().nice()
})
md`# Annex`
function numericSort(x) {
return (
x
// ensure the array is not changed in-place
.slice()
// comparator function that treats input as numeric
.sort((a, b) => a - b)
);
}
function chart(data, scale) {
const w = 30,
cols = Math.floor(Math.min(600, width) / w),
lines = Math.ceil(100 / cols);
const chart = d3
.create("svg")
.attr("width", cols * w)
.attr("height", lines * w);
chart
.append("g")
.attr("transform", "translate(2,2)")
.attr("style", "stroke:black; fill:white;")
.selectAll("rect")
.data(data)
.join("rect")
.attr("width", w - 3)
.attr("height", w - 3)
.attr("x", (_, i) => w * (i % cols))
.attr("y", (_, i) => w * ((i / cols) | 0))
.style("fill", d => (scale ? scale(d) : "#ddd"));
return chart.node();
}
simple = require("simple-statistics@7.0.7/dist/simple-statistics.min.js")
ckmeansThresholds = simple.ckmeans(childpct, 3).map(v => v.pop())
function showScaleGrouping(data, scales) {
const margins = { left: 130 };
const x = d3
.scaleLinear()
.domain(d3.extent(data))
.range([margins.left, width - 5]);
const r = 3;
const rectHeight = 10;
const chart = svg`
<svg width=${width} height="${20 * (1.5 + Object.entries(scales).length)}"
style="font-family: sans-serif; alignment-baseline: middle; font-size:12px">
<g transform="translate(2,2)">
<text x="5" y=${5 + r}>Data</text>
${data.map(
d => `<circle r=${r} cx=${x(d)} cy=5 fill=black opacity="0.3" />`
)}
</g>
<g transform="translate(2,30)">
${Object.entries(scales).map(([name, s], i) => {
const scaleCuts = s.thresholds
? s.thresholds()
: s.quantiles
? s.quantiles()
: s.domain();
const limits = [0, ...scaleCuts, d3.max(data)];
const boxLimits = limits
.slice(0, limits.length - 1)
.map((d, j) => [limits[j], limits[j + 1]]);
return (
`<text x="5" y=${(2 * i + 1) * rectHeight}>${name}</text>` +
boxLimits.map(
(l, k) => `
<rect x=${x(l[0])} y=${i * 2 * rectHeight} height=10
width=${x(l[1]) - x(l[0])}
style="stroke:black;fill:${s.range()[k]};"
/>`
)
);
})}
</g>
</svg>`;
return chart;
}
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