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Coding with Fire
EJ Fox & Ian Johnson streaming data, code, acid, design
Published
Edited
Jul 28, 2020
9 stars
4
Season 1
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 Patterns
Coding With Fire: NYPD CCRB
Where 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? (Data Simulation)Where Are Votes Outstanding? Visualization IdeasElectoral College Unit ChartsElectoral College Unit Charts With TextElectoral College Unit Shuffle
Also listed in…
30 Day Map Challenge
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allegationsByOfficerPrecinct = d3.csvParse(
await FileAttachment("allegations-per-officer-perprecinct.csv").text()
)
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withPrecinct = allegationsByOfficerPrecinct
.filter(d => !!d.command_at_incident)
.sort((a, b) => b.count - a.count)
// .slice(0, 2000)
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topOfficers = 200
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// parseJson(
// nodes.map(d => {
// return {
// id: d.id,
// name: d.name,
// total: d.total,
// isPrecinct: d.isPrecinct
// };
// })
// )
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// parseJson(links)
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officerColorScale = d3.scaleSequential([0, 60], d3.interpolateBlues)
// .scaleLinear()
// .domain([0, 60])
// .range(['#2c7fb8', '#2c7fb8'])
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officerStrokeScale = d3
.scaleLinear()
.domain([0, 60])
.range([0.25, 5])
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height = 750
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simpleSVGMap = {
// we create an SVG with the width and height specified
const svg = d3
.create("svg")
.attr("width", width)
.attr("height", height);

svg
.append("g")
.selectAll("path")
.data(precinctGeometry)
.join("path")
// This line renders our population data
// .attr("fill", d => populationColor(populationByCounty.get(d.id)))
.attr("fill", d => {
if (overlappingPrecincts.get(d.id)) {
return "seagreen";
} else {
return "lightgray";
}
})
.attr("d", d3.geoPath(projection));

svg
.append("g")
.selectAll("circle.centroid")
.data(pGeomCentroids)
.join("circle")
.classed("centroid", true)
// This line renders our population data
// .attr("fill", d => populationColor(populationByCounty.get(d.id)))
.attr("fill", d => {
if (overlappingPrecincts.get(d.precinct)) {
return "black";
} else {
return "gray";
}
})
.attr("r", 5)
.attr(
"transform",
d => `translate(${projection(d.centroid.geometry.coordinates)})`
)
.attr("d", d3.geoPath(projection));

// we need to return a DOM element.
// the .node() function returns the DOM element corresponding to the d3 selection.
return svg.node();
}
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overlappingPrecincts = new Map(
precincts
.filter(d => !!centroidLookup.get(weirdToNormal(d.id)))
.map(d => [weirdToNormal(d.id), d.id])
)
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// svgbubbles
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officerRadiusScale = d3
.scaleLog()
.domain(d3.extent(nodes.filter(d => !d.isPrecinct), d => d.total))
.range([2, 12])
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precinctRadiusScale = d3
.scaleLinear()
.domain(d3.extent(nodes.filter(d => d.isPrecinct), d => d.total))
.range([2, 20])
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canvasBubbles
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// parseJson(canvasBubbles)
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function parseJson(data) {
let parser = new json2csv.Parser();
let csv = parser.parse(data);
return csv;
}
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md`## Precinct Geometries
`
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pGeom = d3.json("https://data.cityofnewyork.us/resource/kmub-vria.json")
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precinctGeometry = pGeom.map(d => {
return {
type: "Feature",
id: d.precinct,
precinct: d.precinct,
geometry: d.the_geom
};
})
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projection = d3.geoAlbersUsa().fitSize([width, height], {
type: "FeatureCollection",
features: precinctGeometry
})
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ejProjection = d3
.geoTransverseMercator()
.rotate([76 + 35 / 60, -40])
.fitExtent([[10, 10], [960 - 20, 500 - 20]], {
type: "FeatureCollection",
features: precinctGeometry
})
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projection([-74.01550862599652, 40.70170515792238])
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centroidLookup = new Map(pGeomCentroids.map(d => [d.precinct, d.centroid]))
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pGeom.map(d => d.precinct)
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weirdToNormal = function(pct) {
return String(Number(pct.split(" ")[0]));
}
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weirdToNormal("075 PCT")
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margin = ({ top: 0, right: 20, bottom: 30, left: 20 })
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require('d3-jetpack')
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d3tip = require('d3-tip')
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json2csv = require("json2csv")
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turf = require("@turf/turf")
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