Assistant Professor of Data Science and Analytics, Georgetown University
Inputs.range([0,10], {value: 0, step: 1, label: "Kyiv"})
Plot.plot({
// width: 975,
// height: 610,
// projection: "identity",
color: {
// type: "quantize",
// n: 9,
// domain: [1, 10],
scheme: "blues",
label: "Demand",
legend: true
},
marks: [
Plot.geo(states, Plot.centroid({
fill: d => regionDataMap.get(d.properties.NAME_1),
tip: true,
channels: {
name: d => d.properties.NAME_1,
iso: d => d.properties.ISO_1
}
})),
Plot.text(states, Plot.centroid({
text: (d) => d.properties.name, fill: "currentColor", stroke: "white"
})),
]
})
//unemployment = new Map((await FileAttachment("Book1.csv").csv()).map(d => [d.id, +d.rate]))
//demand = new Map((await FileAttachment("Book1.csv").csv()).map(d => [d.id, +d.pop]))
demand = new Map([
["Cherkasy", 10],
])
//ua_topo = FileAttachment("ukraine2.json").json()
ua_topo = FileAttachment("Ukraine-regions@1.json").json()
// Keep just the inclStates
ua_topo.objects.UKR_adm1.geometries = ua_topo.objects.UKR_adm1.geometries.filter(d => inclCodes.indexOf(d.properties.ISO_1) > -1)
//ua_geojson = FileAttachment("UKR1_simplified.geojson").json()
states = topojson.feature(ua_topo, ua_topo.objects.UKR_adm1)
//tjSimplify.filter(states, d => inclCodes.indexOf(d.properties.ISO_1) > -1)
statemap = new Map(states.features.filter(d => inclCodes.indexOf(d.properties.ISO_1) > -1).map(d => [d.properties.NAME_1, d]))
regionData = d3.csv("https://docs.google.com/spreadsheets/d/e/2PACX-1vR792Vvir2BIafIxLp_9FlkcElgrmTRgTvA6O9qWbpcrrOZ1MGSh3EIAiuRphCJQhCBA50uSadv01Md/pub?gid=0&single=true&output=csv")
inclRegionData = regionData.filter(d => inclCodes.indexOf(d.iso) > -1)
regionDataMap = new Map(inclRegionData.map(d => [d.name_en,parseInt(d.initial_demand)]))
allCodes = regionData.map(d => d.iso)
reducedCodes = [
"UA-30", // Kyiv city
"UA-32" // Kyiv oblast
]
inclCodes = allCodes.filter(d => reducedCodes.indexOf(d) > -1)
// Uncomment for Leaflet version
// {
// const container = yield htl.html`<div style="height: 500px;">`;
// const map = L.map(container);
// const layer = L.geoJSON(states).addTo(map);
// map.fitBounds(layer.getBounds(), {maxZoom: 9});
// L.tileLayer("https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png", {
// attribution: "© <a href=https://www.openstreetmap.org/copyright>OpenStreetMap</a> contributors"
// }).addTo(map);
// }
// Uncomment for ObservablePlot version
// Plot.plot({
// // width: 975,
// // height: 610,
// // projection: "identity",
// color: {
// // type: "quantize",
// // n: 9,
// // domain: [1, 10],
// scheme: "blues",
// label: "Demand",
// legend: true
// },
// marks: [
// Plot.geo(states, Plot.centroid({
// fill: d => regionDataMap.get(d.properties.NAME_1),
// tip: true,
// channels: {
// name: d => d.properties.NAME_1,
// iso: d => d.properties.ISO_1
// }
// })),
// Plot.text(states, Plot.centroid({
// text: (d) => d.properties.name, fill: "currentColor", stroke: "white"
// })),
// ]
// })
// import { simplify } from "npm:topojson-simplify";
tjSimplify = require("topojson-simplify")
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.