viewof zoom = Inputs.range([0, 1], {label: "longitude", step: .001, value: .23})
viewof Floor_water = Inputs.toggle({label: "Floor water in 50 years"})
Floor_water
Select_asset = dgi.filter: (dgi, (d) => d3.asset_type("asset_type"))
Plot.plot({width:1200,height:1200,
projection: {type: "orthographic",rotate: [80, -34], domain:circle},
r: {transform: (d) => Math.pow(10, d)}, // convert Richter to amplitude
style: "overflow: visible;", // allow dots to escape
marks: [
//Plot.geo(datasetwithdataproperty to create cloropleth, {fill: d => color(d.properties.POPULATION), fillOpacity: 0.2, type:"md"}),
//Plot.geo(_census_tracts__tabular_20231105, {fill: d => color(d.properties.POPULATION), fillOpacity: 0.2, type:"md"}),
Plot.geo(combined, {fill: d => color(d.properties.Income), fillOpacity:1,
//channels: { Income: (d) => d.properties.Income }, // Display asset_type in the tooltip
tip: true},
),
Plot.geo(Flood, {fill: "blue", fillOpacity: 0.7, type:"md"}),
Plot.geo(archivedParksProperties , {fill: "green", stroke:"green", fillOpacity: 0.5, type:"md",
//channels: { typecatego: (d) => d.properties.typecatego}, // Display asset_type in the tooltip
tip: true}),
Plot.dot(dgi.features, {x: (d) => d.geometry.coordinates[0], y: (d) => d.geometry.coordinates[1],
r: .7,
stroke: "cyan",
fill: "cyan",
fillOpacity: 0.1,
channels: { asset_type: (d) => d.properties.asset_type }, // Display asset_type in the tooltip
tip: true})
]
})
archivedParksProperties = FileAttachment("ARCHIVED - Parks Properties.geojson").json()
censusTractsTabular_20231107 = FileAttachment("2020 Census Tracts - Tabular_20231107.geojson").json()
nyc_census_tracts@1.csv
Type Table, then Shift-Enter. Ctrl-space for more options.
combined = {
var geo = censusTractsTabular_20231107.features
var data = nyc_census_tracts1
var combinedData = []
for (let i = 0; i < geo.length; i++) {
var lineName = Number(geo[i].properties.geoid)//dataset w/ lines, specify properties that has match key
for (let k = 0; k < data.length; k++) {
var dataName = data[k].CensusTract//none spatial dataset, specify properties that have match
if(lineName == dataName){//console.log('match')
combinedData.push({type:'Feature',properties:data[k],geometry:geo[i].geometry})
}
}
}
return {type: 'FeatureCollection',features:combinedData}}
Flood = FileAttachment("Sea Level Rise Maps (2050s 500-year Floodplain).geojson").json()
color = d3.scaleQuantize().domain([5000, 120000]).range(d3.schemeReds[9]);
circle = d3.geoCircle().center([-73.9, 40.68]).radius(zoom)()
dgi = FileAttachment("DEP Green Infrastructure.geojson").json()
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.