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?Voronoi PurpleAirDeadline 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? Visualization IdeasElectoral College Unit ChartsElectoral College Unit Charts With TextElectoral College Unit Shuffle
Where Are Votes Outstanding? (Data Simulation)
Also listed in…
Election Explorations
eav
voteByCounty.get(fips)
fips
+eavsByCounty.get(fips)["A1a|2016"]
calculatePercent(countySVG.id)
eavsByCounty.get(countySVG.id)
projection = d3.geoAlbersUsa().fitSize([width - 10, height - 30], countyShapes)
minCounty = d3.min(countyShapes.features, d => calculatePercent(d.id))
diffScale = d3
.scaleSequential(d3.interpolateGreys)
// .domain(diffExtent)
.domain([minCounty, 1])
turf.centroid(countyShapes.features[0])
center = d => {
return projection(turf.centroid(d).geometry.coordinates) || [0, 0];
}
allYearsFlat = FileAttachment("allYearsFlat@1.csv").csv({ typed: false })
allYearsFlat.filter(d => d.State_Full == "WISCONSIN")
eavsByCounty = new Map(
allYearsFlat.map(d => {
let s = String(d.FIPSCode);
return [s.slice(0, s.length - 5).padStart(5, "0"), d];
})
)
statePctConfig = ({
min: simPct[0] * 100,
max: simPct[1] * 100
})
pctByState = {
let seed2 = "hi";
let chance = new Chance(seed2);
return new Map(
stateShapes.features.map(d => {
let s = String(d.FIPSCode);
return [d.id, chance.floating(statePctConfig)];
})
);
}
pctByCounty = {
let seed = "hi";
let chance = new Chance(seed);
return new Map(
allYearsFlat.map(d => {
let s = String(d.FIPSCode);
s = s.slice(0, s.length - 5).padStart(5, "0");
let statePct = pctByState.get(s.slice(0, 2));
return [
s,
chance.floating({
min: statePct - 10,
max: _.clamp(statePct + 10, 100)
})
];
return [s.slice(0, s.length - 5).padStart(5, "0"), statePct];
})
);
}
voteJitter = 5 // The amount added or subtracted from state percentage
voteByCounty = {
let seed = "hi";
let chance = new Chance(seed);
return new Map(
countyShapes.features.map(d => {
let fips = d.id;
let eave = eavsByCounty.get(fips);
if (!eave) return [fips, 0];
let countyActiveVoters = eave["A1a|2016"];
let statePct = pctByState.get(fips.slice(0, 2));
let countyPct = chance.floating({
min: statePct - voteJitter,
max: _.clamp(statePct + voteJitter, 100)
});
return [fips, Math.floor(countyActiveVoters * (countyPct / 100))];
})
);
}
import { legend } from "@d3/color-legend"
countySVG
fips = countySVG ? countySVG.id : ""
eav = eavsByCounty.get(fips)
eav ? eav : ""
height = 500
pop2016 = demographicsData[2016].get(fips)["B01003_001E"]
pop2018 = demographicsData[2018].get(fips)["B01003_001E"]
format = d3.format(",d")
import {
demographicsData,
selectedFields,
niceLabel,
countyShapes,
stateShapes
} from "@enjalot/us-county-datasets"
d3 = require("d3@6")
FileAttachment("keyLables.json").json()
turf = require('https://bundle.run/turf@3.0.14')
import { twitch } from "@codingwithfire/twitch"
import { rangeSlider } from "@mootari/range-slider"
Chance = require("chance")
_ = require('lodash')
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.