md`# Isaac McSorley: Choropleth Mapping [GEOG 3540]
Percentage of people in group homes in LA County, CA, Source: [Census 2010]`
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}%`
topojson = require("topojson-client@3")
la = FileAttachment("LA_County_Tracts1.json").json()
tracts = topojson.feature(la, la.objects.LA_County_Tracts1)
csv_data = d3.csvParse(await FileAttachment("LA_FoodAccess.csv").text(),({CensusTract, Pop2010, NUMGQTRS}) => [CensusTract, [+Pop2010]])
data = Object.assign(new Map(csv_data), {title: "Population in LA Census Tracts"})
pop_la = Array.from(csv_data.values(), d => d[1][0])
YlGnBu = [d3.color("#fef0d9"), d3.color("#fdcc8a"), d3.color("#fc8d59"), d3.color("#e34a33"),d3.color("#b30000")]
naturalbreaks = simple.ckmeans(pop_la, YlGnBu.length).map(v => v.pop())
//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(naturalbreaks)
.range(YlGnBu)
width = 975
height = 610
margin = 100
//Rotate the map sets the longitude of origin for our UTM Zone 15N projection.
projection = d3.geoTransverseMercator().rotate([118,0]).fitExtent([[80, 80], [width, height]], tracts);
//d3 reference for projections: https://github.com/d3/d3-geo/blob/master/README.md
//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.geoAlbers().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(360,20)")
.append(() =>
legend({
color: color,
title: data.title,
width: 260,
tickFormat: ".1f"
})
);
svg.append("g")
.selectAll("path")
.data(tracts.features)
.join("path")
.attr("stroke", "white")
.attr("stroke-linejoin", "round")
.attr("stroke-width", .3)
// .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.GEOID)))
.attr("d", path)
.append("title")
.text(d => " Population: " + data.get(d.properties.GEOID));
return svg.node();
}
tracts.features
One platform to build and deploy the best data apps
Experiment and prototype by building visualizations in live JavaScript notebooks. Collaborate with your team and decide which concepts to build out.
Use Observable Framework to build data apps locally. Use data loaders to build in any language or library, including Python, SQL, and R.
Seamlessly deploy to Observable. Test before you ship, use automatic deploy-on-commit, and ensure your projects are always up-to-date.