chart = {
const svg = d3.create("svg")
.attr("viewBox", [0, 0, width, height]);
svg.append(legend)
.attr("transform", "translate(250,150)"); //legend location
svg.append("g")
.selectAll("path")
.data(topojson.feature(polygons, polygons.objects.NYCountiesWGS1984).features) //read in data
.join("path")
.attr("stroke", "grey")
.attr("fill", d => color(data.get(d.properties[idAttribute]))) //read in FIPS code
.attr("d", path)
.append("title")
.text(d => `${d.properties[idAttribute]}, ${format(data.get(d.properties[idAttribute]))}`);
return svg.node();
}
//Interpretation of Spatial Patterns
//The areas of high covid cases smoothly changes as the population density changes. There are not many areas where neighboring counties jump from one side of the legend to another. There are some strong clustering of high covid per capita rate and high population density counties in and around the NYC area.
// 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"
]
}
]
labels = ["low", "", "high"]
n = Math.floor(Math.sqrt(colors.length))
//creates a scale, Pop. Density
x = d3.scaleQuantile(Array.from(data.values(), d => d[0]), d3.range(n))
//creates a scale, COVID-19 Cases
y = d3.scaleQuantile(Array.from(data.values(), d => d[1]), d3.range(n))
path = d3.geoPath().projection(projection)
//Rotate the map sets the longitude of origin for our UTM projection.
projection = d3.geoTransverseMercator().rotate([87,0]).fitExtent([[10, 10], [width, height]], polygon_features);
polygon_features = topojson.feature(polygons, polygons.objects.NYCountiesWGS1984)
//read in csv file and map the data in one step
data = Object.assign(new Map(d3.csvParse(await FileAttachment("NYPOP.csv").text(), ({FIPS_CODE, Population, Area, CaronavirusCases}) => [FIPS_CODE, [+Population/+Area,100*+CaronavirusCases/Population]])), {title: ["Pop. Density", "% COVID-19 Cases"]})
//last part goes on the legend axis
//change this
idAttribute = "FIPS_CODE"
//read in polygon features, join with FIPS code
polygons = FileAttachment("NYCountiesWGS1984 (1).json").json()
//canvas height
height = 610
//canvas width
width = 875
//creates customized legend
legend = () => {
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];
};
}
//format values in legend and data
format = (value) => {
if (!value) return "N/A";
let [a, b] = value;
return `${a} ${data.title[0]}${labels[x(a)] && ` (${labels[x(a)]})`}
${b}% ${data.title[1]}${labels[y(b)] && ` (${labels[y(b)]})`}`;
}
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.
