EJ Fox & Ian Johnson
streaming data, code, acid, design
Coding With Fire - Crowd Control Color PaletteCMU COVIDcast APICMU COVIDcast API: Map makerMap Maker Dot Density: Cumulative Covid CasesPattern Moire - Line & Circle Grids with ColorschemesPattern Moire - Line & Circle GridsCustom Cluster Force LayoutCMU COVIDcast API: Map maker Bubbles CanvasCMU COVIDcast API: Map maker BubblesCMU COVIDcast API: Map maker Bubbles IICMU COVIDcast API: Map maker TimelapseCoding With Fire: Mind MapGraphene MoiréGraphene Moiré FirebaseGraphene Moiré 3DCoding With Fire: Paths as PointsCoding With Fire: 3D PathsFreaky Dot PatternsCoding With Fire: NYPD CCRBWhere are votes at risk?Deadline Timers 2CMU COVIDcast API: Bubbles ExportEmojisEmoji Treemap Small MultiplesCounties with DatasetteAmerican Community Survey 2018 by CountyRegistered Voters by County 2016-2018Population Change by County 2016-2018: Bubble LinesRegistered Voters by County 2016-2018: Bubble LinesCMU COVIDcast API: Bubbles Dark ModeUS Bubble Choropleth: Travel From CentroidUS Bubble Choropleth: Travel From CentroidCMU COVIDcast API: Bubbles Textures Interactive TimelapseChoropleth with Textures.jsForce Bubble Map with Textures.jsWhere Are Votes Outstanding? (Data Simulation)Where Are Votes Outstanding? Visualization IdeasElectoral College Unit ChartsElectoral College Unit Charts With TextElectoral College Unit Shuffle
Voronoi PurpleAir
air2 = d3
.json(
"https://www.purpleair.com/data.json?opt=1/mAQI/a10/cC0&fetch=true&nwlat=43.167493434353474&selat=32.25997597364069&nwlng=-127.87343156694496&selng=-114.15416006614362&fields=pm_1"
)
.then(d =>
Object.assign(
d.data.map(b => Object.fromEntries(d.fields.map((f, i) => [f, b[i]]))),
{ columns: d.fields }
)
)
air = FileAttachment("air.json").json()
d3 = require("d3@6", "d3-geo-voronoi@1")
topojson = require("topojson-client@3")
us = d3.json("https://unpkg.com/us-atlas@3/counties-10m.json")
stateShapes = topojson.feature(us, us.objects.states)//.features
countyShapes = topojson.feature(us, us.objects.counties)
statesByName = new Map(stateShapes.features.map(d => [d.properties.name, d]))
california = statesByName.get("California")
// Choose a couple counties that define the extent I care about
countyExtent = ({
type: "FeatureCollection",
features: countyShapes.features.filter(
d =>
(d.properties.name == "Marin" || d.properties.name == "Santa Clara") &&
d.id.slice(0, 2) == "06"
)
})
caliCounties = countyShapes.features.filter(d => d.id.slice(0, 2) == "06")
// projection = d3.geoAlbersUsa().fitSize([width, height], california)
projection = d3.geoAlbersUsa().fitSize([width, height], countyExtent)
// projection = d3.geoAlbersUsa().fitSize([width, height], stateShapes)
// projection = d3.geoMercator().fitSize([width, height], countyExtent)
height = 500
projection([-121.547, 37.56])
map = {
let canvas = d3.create("canvas").node();
canvas.width = width;
canvas.height = height;
let ctx = canvas.getContext("2d");
// ctx.globalCompositeOperation = "overlay";
ctx.fillStyle = "#111";
ctx.fillRect(0, 0, width, height);
ctx.globalAlpha = 0.5;
// setting up the path function
let path = d3.geoPath(projection).context(ctx);
// rotating the canvas 90 degrees to the left
ctx.translate(width / 2, height / 2);
ctx.rotate((-90 * Math.PI) / 180);
ctx.translate(-width / 2, -height / 2);
ctx.strokeStyle = "#fff";
ctx.fillStyle = "#ccc";
ctx.beginPath();
path(polygons);
// path({ type: "FeatureCollection", features: caliCounties });
// ctx.fill();
ctx.stroke();
ctx.strokeStyle = "#fff";
ctx.beginPath();
path(california);
ctx.stroke();
for (let sensor of filtered) {
let p = projection([sensor.Lon, sensor.Lat]);
ctx.fillStyle = colorPM1(sensor.pm_1);
ctx.fillRect(p[0], p[1], 3, 3);
}
ctx.globalAlpha = 1;
for (let city of cities) {
// for (let city of uscities) {
let p = projection([city.lng, city.lat]);
if (!p) continue;
ctx.fillStyle = "white";
ctx.font = "bold 10px sans-serif";
ctx.save();
ctx.translate(p[0], p[1]);
ctx.rotate((90 * Math.PI) / 180);
ctx.translate(-p[0], -p[1]);
// ctx.rotate((90 * Math.PI) / 180, p[0], p[1]);
ctx.fillText(city.city, p[0], p[1]);
ctx.restore();
}
return canvas;
}
// loop through each voronoi cell and draw its boundaries
// trying to fill each polygon does not work
// for (let polygon of polygons.features) {
// ctx.lineWidth = 1.5;
// ctx.strokeStyle = "#fff";
// ctx.beginPath();
// path(polygon); // renders to the canvas
// ctx.fillStyle = colorPM1(polygon.properties.site[2]);
// ctx.fill();
// ctx.stroke();
// // ctx.beginPath();
// // path(polygon);
// }
uscities = d3.csvParse(await FileAttachment("uscities.csv").text(), d3.autoType)
d3.bin()(uscities.map(d => d.population))
cities = uscities.filter(d => d.population > 100000)
// cities = [
// {
// name: "San Francisco",
// lonlat: [-122.4726194, 37.7577627]
// }
// ]
colorPM1 = d3
// .scaleSequential(d3.interpolateReds)
// .domain([0, 250])
// .range(["black", "white"])
.scaleLinear()
.domain([0, 50, 100, 150, 200, 250])
.range(["green", "yellow", "orange", "red", "maroon", "maroon"])
d3.group(air, d => d.Type)
filtered = air
.filter(d => d.Lat && d.Lon && d.Type === 0)
.sort((a, b) => b.pm_1 - a.pm_1)
points = filtered.map(d => [d.Lon, d.Lat, d.pm_1])
voronoi = d3.geoVoronoi(points)
mesh = voronoi.cellMesh()
triangles = voronoi.triangles()
polygons = voronoi.polygons()
polygons.features.map(d => d.properties.site[2])
d3.bin()(filtered.map(d => d.pm_1))
d3.extent(filtered, d => d.pm_1)
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.
