md`This notebook creates a choropleth map.`
d3 = require("d3@5")
import {legend} from "@d3/color-legend"
simple = require("simple-statistics@7.0.7/dist/simple-statistics.min.js")
format = d => `${d}%`
US = FileAttachment("dtl_cnty (1).json").json()
counties = topojson.feature(US, US.objects.dtl_cnty)
csv_data = d3.csvParse(await FileAttachment("dtl_cnty.csv").text(),({FIPS, POP_SQMI}) => [+FIPS, +POP_SQMI])
data = Object.assign(new Map(csv_data), {title: "US County Population Density (Population / Sqaure Mile)"})
data.get(1003)
popden = Array.from(csv_data.values(), d => d[1])
YlGnBu = [d3.color("#ffffcc"), d3.color("#a1dab4"), d3.color("#41b6c4"), d3.color("#2c7fb8"),d3.color("#253494")]
threshold = d3.scaleThreshold()
.domain([1, 20, 90, 500, 2000, 72588.3])
.range(["#fef0d9", "#fdd49e", "#fdbb84", "#fc8d59", "#e34a33", "#b30000"])
//more information on sequential scales: https://observablehq.com/@d3/sequential-scales
// color = d3.scaleSequentialQuantile([...data.values()], d3.interpolateBlues)
// color = d3.scaleQuantile()
// .domain(med_age)
// .range()
color = d3.scaleThreshold()
.domain([1, 20, 90, 500, 2000, 72588.3])
.range(["#fef0d9", "#fdd49e", "#fdbb84", "#fc8d59", "#e34a33", "#b30000"])
width = 975
height = 610
margin = 100
//Rotate the map sets the longitude of origin for our UTM Zone 15N projection.
//projection = d3.geoTransverseMercator().rotate([94,0]).fitExtent([[80, 80], [width, height]], counties);
//d3 reference for projections: https://d3js.org/d3-geo
//use the following url for specific projection settings: https://github.com/veltman/d3-stateplane
//Use this code to set up the map projection (if different than geographic projection)
projection = d3.geoAlbersUsa().fitExtent([[margin, margin], [width - margin, height - margin]], counties)
//projection = d3.geoMercator().fitExtent([[margin, margin], [width - margin, height - margin]], counties)
//Using a path generator to project geometry onto the map
path = d3.geoPath().projection(projection);
choropleth = {
const svg = d3.create("svg")
.attr("viewBox", [0, 0, width, height]);
svg.append("g")
.attr("transform", "translate(140,20)")
.append(() =>
legend({
color: color,
title: data.title,
width: 750,
tickFormat: "0.1f"
})
);
svg.append("g")
.selectAll("path")
.data(counties.features)
.join("path")
.attr("stroke", "black")
.attr("stroke-linejoin", "bevel")
.attr("stroke-width", 0.2)
// .attr("fill", function(d){
// console.log(color(data.get(d.properties.FIPS)[0]))
// return color(data.get(d.properties.FIPS)[0]);
// })
.attr("fill", d => color(data.get(+d.properties.FIPS)))
.attr("d", path)
.append("title")
.text(d => " Population Density: " + data.get(+d.properties.FIPS));
return svg.node();
}
data.get(36061)
color(data.get(36061))
color(40)
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.
