Hi. I'm Nicolas Lambert, research engineer in geographic information science at CNRS (France). My job is to draw maps and teach cartography at the university. I am a geographer 🌎, and not really a developer 🖥️
Hello Bertin.jsmatch and mergetextstypochoroplethmushroomInputs.table + bertin.jsquickdraw & table2geodorling cartogramprojectionssymbolsspikesstock + chorostock + typodot cartogramlinksthicknessmercator tilesbertin.js: Tooltipsregular bubble (Points Bertin)bertin.js: official logo 😎dot density mapdeal with with geojson propertiesLayer types in brieflocation map (minimap)prop squaresWaterlinesregular squaresRegular gridSeveral Ways to Represent Dead and Missing Migrants in the Mediterranean with bertinSmoothCombine bertin with plot and d3jsCheat sheet bertinLet's play with viewofRidgeSVG paths & patternsDisplay and hide layers with BertinUpdate layers with bertinbertin globe view
prop symbols
bertin.draw({
params: { projection: d3.geoBromley() },
layers: [
{
type: "bubble",
geojson: data,
values: "pop",
k: k,
fill: "#e368c0",
//fixmax: 10000000,
tooltip: [
"$country",
(d) => Math.round(d.properties.pop / 1000) + " million inh."
],
leg_round: 0,
leg_x: 100,
leg_y: 170,
leg_round: 0,
leg_divisor: 1000,
leg_title: "World Population\nin 2018 (million inh.)"
},
{
type: "missing",
geojson: data,
values: "pop",
leg_x: 500,
leg_y: 320
},
{
type: "layer",
geojson: countries,
fill: "#f5d482",
leg_x: 600,
leg_y: 320,
leg_text: "Countries"
},
{ type: "graticule" },
{ type: "outline" }
]
})
cities = d3
.csv(
"https://raw.githubusercontent.com/neocarto/resources/master/datasets/simplemaps_worldcities_basicv1.73/worldcities.csv"
)
.then((r) => r.filter((d) => +d.population > 1000000))
bertin.draw({
params: { projection: d3.geoBertin1953() },
layers: [
{
type: "bubble",
geojson: cities,
values: "population",
k: 17,
tooltip: ["$city", "$country"],
fill: "#5c7db8",
leg_x: 680,
leg_y: 410,
leg_round: 0,
leg_fontSize2: 7,
leg_title: `Number of inhabitants
in the world's largest cities`
},
{
type: "layer",
geojson: countries,
fill: "white",
fillOpacity: 0.5,
stroke: "none"
},
{ type: "graticule" },
{ type: "outline" }
]
})
tmp = FileAttachment("API_BN@1.CAB.XOKA.CD_DS2_en_csv_v2_3733806.csv")
.csv()
.then((d) =>
d.map((d) => ({
id: d["Country Code"],
name: d["Country Name"],
bop_2020: +d[2020] / 1000000000
}))
)
bop = bertin.merge(countries, "ISO3_CODE", tmp, "id")
bertin.draw({
params: { projection: d3.geoBromley() },
layers: [
{
type: "bubble",
geojson: bop,
values: "bop_2020",
k: k,
fill: {
type: "split",
values: "bop_2020",
colors: ["#F25842", "#4a7cd9"],
split: 0,
leg_x: 100,
leg_y: 300
},
tooltip: ["$name", "$bop_2020"],
leg_round: 0,
leg_x: 100,
leg_y: 170,
leg_round: -1,
leg_title: "Balance of payment,\n(in billion $ in 2020)"
},
{
type: "missing",
geojson: bop,
values: "bop_2020"
},
{
type: "layer",
geojson: countries,
fill: "#f5d482"
},
{ type: "graticule" },
{ type: "outline" }
]
})
Type JavaScript, then Shift-Enter. Ctrl-space for more options. Arrow ↑/↓ to switch modes.
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.