Topographic MappingBubble mapChoroplethAccess to Family planningMD Counties Total Cases MapTendance de la production des déchets en Union EuropéenneAndy's Walgreens COVID-19 Tracker TrackerElection Maps for Incomplete ResultsA better U.S. house election results map?1983 Mayoral Election, Dot density mapsMastodon 🐘Cheat sheet bertinBertin.js: regular squaresWaterlinesNeumorphism Contour Density MapCartographic DoodlesStars and constellationsPlot: Grid choroplethHello Polygon MorphingMapping with pie chartsU.S. Geographic DataHow big are countries... like really!AttitudeB&W ChoroplethWeb Mercator Tile VisibilityMARTINI: Real-Time RTIN Terrain Mesh"Magnifying-Glass" projectionsTissot's indicatrixAntipodal mapMapping gridded data with a Voronoi diagramA Map of Every BuildingUrbano Monti’s Planisphere (1587)DIY HillshadeMapbox Map MakerWorld tourHillshaderSimplified Earth with curved shapesHexbin mapInner glowNicolosi vs. StereographicData-driven projections: Darwin's worldSatellite ground track visualizerDirection to shoreHello, OpenLayers!U.S. airports VoronoiHello, NYC Geosearch API!Mapbox Fly-ToSpilhaus shoreline mapWalmart’s growthHow well does population density predict U.S. voting outcomes?Drawing maps from geodata with D3 & ObservableHexgrid maps with d3-hexgridTissot's indicatrixWorld airports VoronoiSwiss Elevation Line GraphsVector tilesVersor draggingOrthographicSolar TerminatorStreaming ShapefilesFake GlobesPeirce Quincuncial🍃 LeafletU.S.G.S. World Earthquake MapUsing Mapbox GL JSUsing Google Maps
Bivariate choropleth
chart = {
const svg = d3.create("svg")
.attr("width", 975)
.attr("height", 610)
.attr("viewBox", [0, 0, 975, 610])
.attr("style", "max-width: 100%; height: auto;");
svg.append(legend)
.attr("transform", "translate(870,450)");
const x = d3.scaleQuantile(Array.from(data, d => d.diabetes), d3.range(n));
const y = d3.scaleQuantile(Array.from(data, d => d.obesity), d3.range(n));
const index = d3.index(data, d => d.county);
const path = d3.geoPath();
const color = (value) => {
if (!value) return "#ccc";
const {diabetes: a, obesity: b} = value;
return colors[y(b) + x(a) * n];
};
const format = (value) => {
if (!value) return "N/A";
const {diabetes: a, obesity: b} = value;
return `${a}% Diabetes${labels[x(a)] && ` (${labels[x(a)]})`}
${b}% Obesity${labels[y(b)] && ` (${labels[y(b)]})`}`;
};
svg.append("g")
.selectAll("path")
.data(topojson.feature(us, us.objects.counties).features)
.join("path")
.attr("fill", d => color(index.get(d.id)))
.attr("d", path)
.append("title")
.text(d => `${d.properties.name}, ${states.get(d.id.slice(0, 2)).name}
${format(index.get(d.id))}`);
svg.append("path")
.datum(topojson.mesh(us, us.objects.states, (a, b) => a !== b))
.attr("fill", "none")
.attr("stroke", "white")
.attr("stroke-linejoin", "round")
.attr("d", path);
return svg.node();
}
legend = () => {
const k = 24;
const arrow = DOM.uid();
return svg`<g font-family=sans-serif font-size=10>
<g transform="translate(-${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>Diabetes${labels[j] && ` (${labels[j]})`}
Obesity${labels[i] && ` (${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(${n / 2 * k},6)" text-anchor="middle">Diabetes</text>
<text font-weight="bold" dy="0.71em" transform="translate(${n / 2 * k},${n * k + 6})" text-anchor="middle">Obesity</text>
</g>
</g>`;
}
data = FileAttachment("cdc_diabetes_obesity_2020.csv").csv()
.then((data) => {
data.forEach((d) => {
d.obesity = +d.obesity; // type as numeric
d.diabetes = +d.diabetes;
});
return data;
})
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))
states = new Map(us.objects.states.geometries.map(d => [d.id, d.properties]))
us = FileAttachment("counties-albers-10m.json").json()
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.
