Maps — and other representations of the world.
Dotted OceansWorld Cities GabrielAntipodal ArchipelagoBi-Hexagonal ProjectionParis, TexasUSSR, 1938Hex NDVIIGRF-90🌖 Crater-shadingModified Stereographic Alaska GLSLVolcano on A-FrameBorn in AntarcticaNatural Earth issuesHello, spl.jsNéguentropieTheoretical land coverFive OceansPantelleria Joy PlotRouting dollarsDeriving PantelleriaGuessing CzechoslovakiaGHSL — Growing and decliningHistorical shapefilesThe E.N.D GameAnn Arbor pycnophylacticSpherical signed distanceWalk-on-Spheres… on a SpherePotential dollar flowsThirty days and as many maps
Loir-et-Cher isodémographique
points0 = {
let rest = 0;
const phi = Math.PI * (3 - Math.sqrt(5));
return (
d3
// sort the features by what we're plotting, so that missing or excess
// rural population from a commune goes to another rural commune
.sort(geo.features, ({ properties: { code } }) => density.get(code))
.flatMap((f) => {
const coords = projection(d3.geoCentroid(f));
const {
properties: { code }
} = f;
const pop = population.get(code);
const length = Math.round((pop + rest) / precision);
// we keep track of the difference between the current sum and the true sum, and add
// the people we missed (or overcounted) to the next unit, so that the grand total is correct.
rest += pop - length * precision;
return Array.from({ length }, (_, i) => {
const r = 0.1 * Math.sqrt(0.5 + i);
return {
coords: [
coords[0] + r * Math.sin(i * phi),
coords[1] + r * Math.cos(i * phi)
],
code
};
});
})
);
}
points = (replay,
points0
.map((d) => d.coords)
.flat()
.map((d) => d - height / 2 + 0.01 * (Math.random() - 0.5)))
codes = points0.map((d) => d.code)
run = {
for (let i = 0; i < 150; i++) {
yield mapUrban.update(i);
}
}
fill = colorBy === "commune"
? codeColor
: colorBy === "population density"
? (code) => densityColor(density.get(code))
: (code) => ruralColor(density.get(code))
stroke = (c) => (c === code ? "white" : fill(c)) // highlight a commune
densityColor = d3
.scaleSequentialSqrt(d3.interpolateCool)
.domain(d3.extent(density, ([, v]) => v))
ruralColor = d3
.scaleThreshold()
.domain([50, 100, 250])
.range("e6deb2-9ac1ae-ecb45b-e07a5f".split("-").map((d) => `#${d}`))
codeColor = d3
.scaleOrdinal()
.domain(density.keys())
.range(
[].concat(
d3.schemeTableau10,
d3.schemeAccent,
d3.schemeCategory10,
d3.schemeDark2
)
)
discUniformTransportBatched = {
let indices = [],
projection,
deltas;
// batched
return (
points,
radius,
{ dirs = 3, strength = 1, profile = chordAreaInverse } = {}
) => {
const n = points.length / 2;
if (n !== indices.length) {
indices = Uint32Array.from(d3.range(n));
projection = new Float32Array(n);
deltas = new Float32Array(2 * n);
}
const a = 2 * Math.PI * Math.random();
deltas.fill(0);
for (let d = 0; d < dirs; d++) {
const ap = a + (Math.PI * d) / dirs,
sa = Math.sin(ap),
ca = Math.cos(ap);
for (let i = 0; i < n; i++)
projection[i] = ca * points[2 * i] + sa * points[2 * i + 1];
indices.sort((i, j) => projection[i] - projection[j]);
for (let k = 0; k < n; k++) {
const i = indices[k],
ideal = radius * profile(k / (n + 1)),
delta = ideal - projection[i];
deltas[2 * i] += ca * delta;
deltas[2 * i + 1] += sa * delta;
}
}
for (let i = 0; i < points.length; i++)
points[i] += (deltas[i] / dirs) * strength;
};
}
// jrus' fast (and precise) approximation of the chord area inverse function
import { chordAreaInverse2 as chordAreaInverse } from "@fil/chord-area-inverse"
function lloydsRelaxation(voronoi, radius) {
const {
delaunay: { points }
} = voronoi;
for (let i = 0; i < points.length / 2; i++) {
const R = Math.hypot(points[2 * i], points[2 * i + 1]);
if (R < radius) {
const c = voronoi.cellPolygon(i);
const p = d3.polygonCentroid(
c.map(([x, y]) => {
const r0 = Math.hypot(x, y);
const a = radius / Math.max(r0, radius);
return [x * a, y * a];
})
);
const f = 0.5;
points[2 * i] += (p[0] - points[2 * i]) * f;
points[2 * i + 1] += (p[1] - points[2 * i + 1]) * f;
}
}
}
function preserveContinuity(voronoi, codes) {
const {
delaunay: { points }
} = voronoi;
const counts = {};
const colors = codes.map((d) => (counts[d] = (counts[d] || 0) + 1) - 1);
let cutoff = 10;
do {
let changed = false;
for (let i = 0; i < codes.length; i++) {
for (const j of voronoi.delaunay.neighbors(i)) {
if (codes[i] === codes[j] && colors[j] !== colors[i]) {
colors[j] = colors[i] = Math.min(colors[j], colors[i]);
changed = true;
}
}
}
if (!changed) cutoff = 0;
} while (cutoff-- > 0);
for (let i = 0; i < colors.length; i++) {
const f = colors[i];
if (f > 0) {
console.log("fix continuity for point", i, codes[i]);
const base = [];
for (let j = 0; j < i; j++) if (codes[j] === codes[i]) base.push(j);
points[2 * i] +=
0.7 * (d3.mean(base, (i) => points[2 * i]) - points[2 * i]);
points[2 * i + 1] +=
0.7 * (d3.mean(base, (i) => points[2 * i + 1]) - points[2 * i + 1]);
}
}
}
projection = d3.geoMercator().fitSize([height, height], geo)
height = Math.min(670, width)
// source "https://raw.githubusercontent.com/gregoiredavid/france-geojson/master/departements/41-loir-et-cher/communes-41-loir-et-cher.geojson"
geo = FileAttachment("41.json").json()
names = new Map(geo.features.map((f) => [f.properties.code, f.properties.nom]))
density = new Map(
d3.sort(
geo.features.map((f) => [
f.properties.code,
(population.get(f.properties.code) / d3.geoArea(f)) *
((4 * Math.PI) / 510.1e6) // population per km2
]),
([, v]) => v
)
)
population = FileAttachment("population41.csv")
.csv()
.then((d) => new Map(d.map(({ code, population }) => [code, +population])))
rivers = [
{
name: "La Loire",
places: [
"Saint-Laurent-Nouan",
"Montlivault",
"Candé-sur-Beuvron",
"Monteaux"
]
},
{
name: "Le Loir",
places: [
"Saint-Jean-Froidmentel",
"Fréteval",
"Pezou",
"Lisle",
"Vendôme",
"Naveil",
"Mazangé",
"Saint-Rimay",
"Montoire-sur-le-Loir",
"Couture-sur-Loir"
]
},
{
name: "Le Cher",
places: ["Selles-sur-Cher", "Saint-Aignan", "Montrichard Val de Cher"]
}
]
import { Legend, Swatches } from "@d3/color-legend"
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.