chart = {
const svg = d3.create("svg")
.attr("viewBox", [0, 0, width, height]);
svg.append(legendbivariate)
.attr("transform", "translate(670,400)");
svg.append("g")
.attr("transform", "translate(670,500)")
.append(() =>
legend({
color: nullcolorforlegend,
title: "No Data",
width: 50,
tickFormat: ".1f"
})
);
svg.append("g")
.selectAll("path")
.data(topojson.feature(polygons, polygons.objects.MinnesotaCensusCAFO_ExportFeature).features)
.join("path")
.attr("fill", d => color(data.get(d.properties[idAttribute])))
.attr("d", path)
.append("title")
.text(d => `${format(data.get(d.properties[idAttribute]))}`);
return svg.node();
}
// http://www.joshuastevens.net/cartography/make-a-bivariate-choropleth-map/
schemes = [
{
name: "RdBu",
colors: [
"#e8e8e8", "#e4acac", "#c85a5a",
"#b0d5df", "#ad9ea5", "#985356",
"#64acbe", "#627f8c", "#574249"
]
},
{
name: "BuPu",
colors: [
"#e8e8e8", "#ace4e4", "#5ac8c8",
"#dfb0d6", "#a5add3", "#5698b9",
"#be64ac", "#8c62aa", "#3b4994"
]
},
{
name: "GnBu",
colors: [
"#e8e8e8", "#b5c0da", "#6c83b5",
"#b8d6be", "#90b2b3", "#567994",
"#73ae80", "#5a9178", "#2a5a5b"
]
},
{
name: "PuOr",
colors: [
"#e8e8e8", "#e4d9ac", "#c8b35a",
"#cbb8d7", "#c8ada0", "#af8e53",
"#9972af", "#976b82", "#804d36"
]
}
]
nancolor = d3.color("black")
nullcolorforlegend = d3.scaleOrdinal()
.domain([NaN])
.range([nancolor])
labels = ["low", "", "high"]
n = Math.floor(Math.sqrt(colors.length))
x = d3.scaleQuantile(Array.from(data.values(), d => d[0]), d3.range(n))
y = d3.scaleQuantile(Array.from(data.values(), d => d[1]), d3.range(n))
path = d3.geoPath().projection(projection)
projection = d3.geoTransverseMercator().rotate([94,0]).fitExtent([[80, 80], [width, height]], polygon_features);
data_filter= Array.from(csv_data.values(), d => d[1][0]).filter(d => d >= 0)
polygon_features = topojson.feature(polygons, polygons.objects.MinnesotaCensusCAFO_ExportFeature)
//data2 = Object.assign(new Map(d3.csvParse(await FileAttachment("ej_mpca_census (1).csv").text(), ({tractce, totnoneng, pop185x, totpov}) => [tractce, [+totnoneng/+totpov, +pop185x/+totpov]])), {title: ["% non-english", "% poverty"]})
csv_data= d3.csvParse(await FileAttachment("ej_mpca_census (1).csv").text(), ({tractce, totnoneng, pop185x, totpov}) => [tractce, [+totnoneng/+totpov, +pop185x/+totpov]])
data= Object.assign(new Map(csv_data),{title: ["% non-english", "% poverty"]})
idAttribute = "TRACTCE"
polygons = FileAttachment("MinnesotaCensusCAFO_ExportFeature.json").json()
height = 610
width = 875
legendbivariate = () => {
const k = 24;
const arrow = DOM.uid();
return svg`<g font-family=sans-serif font-size=10>
<g transform="translate(-${k * n / 2},-${k * n / 2}) rotate(-45 ${k * n / 2},${k * n / 2})">
<marker id="${arrow.id}" markerHeight=10 markerWidth=10 refX=6 refY=3 orient=auto>
<path d="M0,0L9,3L0,6Z" />
</marker>
${d3.cross(d3.range(n), d3.range(n)).map(([i, j]) => svg`<rect width=${k} height=${k} x=${i * k} y=${(n - 1 - j) * k} fill=${colors[j * n + i]}>
<title>${data.title[0]}${labels[j] && ` (${labels[j]})`}
${data.title[1]}${labels[i] && ` (${labels[i]})`}</title>
</rect>`)}
<line marker-end="${arrow}" x1=0 x2=${n * k} y1=${n * k} y2=${n * k} stroke=black stroke-width=1.5 />
<line marker-end="${arrow}" y2=0 y1=${n * k} stroke=black stroke-width=1.5 />
<text font-weight="bold" dy="0.71em" transform="rotate(90) translate(${n / 2 * k},6)" text-anchor="middle">${data.title[0]}</text>
<text font-weight="bold" dy="0.71em" transform="translate(${n / 2 * k},${n * k + 6})" text-anchor="middle">${data.title[1]}</text>
</g>
</g>`;
}
color = {
return value => {
if (!value) return "#ccc";
let [a, b] = value;
return colors[y(b) + x(a) * n];
};
}
formatNum = d3.format(".2f")
format = (value) => {
if (!value) return "N/A";
let [a, b] = value;
return `${formatNum((a) * 100)} ${data.title[0]}${labels[x(a)] && ` (${labels[x(a)]})`}
${formatNum((b) *100)} ${data.title[1]}${labels[y(b)] && ` (${labels[y(b)]})`}`;
}
import {legend} from "@d3/color-legend"
topojson = require("topojson-client@3")
d3 = require("d3@5")
Purpose-built for displays of data
Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
