I'm a professor of mathematics at the University of North Carolina Asheville. I've also done a fair amount of consulting work over the years focusing on scientific and data visualization.
I'm happy to be part of the first Observable Ambassador's cohort.
Published
Edited
Jun 26, 2021
22 stars
NC Congressional DistrictsTrivariate choropleth for racial composition in the USWNC Zoom3D Choropleth for NC County PopulationsNC CountiesTriangulated Canadian Provinces (separate)Triangulated NC CountiesAn X3D GlobeCanadian Elections 3DWNC SurroundElectoral Results TilegramThe new Electoral CollegeElectoral MapGPX rides with Leaflet and OSMOld encoded polyline rides with LeafletGPX rides with Maplibre and MaptilerBuncombe county votingThe NC Congressional redistricting battleHexapolis 538Zillow Home PricesSetting up HexapolisCOVID curvesThirty day Map Challenge - Day 1Simple grid cartogram2020 Census Apportionment TilegramMass shootings in the USHBCUsNC State Legislative Elections 2022Hierarchical Structure of the US RegionsIndigenous territories of the SoutheastD3.force to desired positionPopulation Choropleth for the Contiguous US StatesPlotting a few points on a mapMutliple bubble maps for CSV dataWorld population bubble mapReproductive rights and ... ??Gerrymandering Hexapolis by handVisualizations of gun ownership dataIncome inequality in North Carolina
Racial distribution in NC
// The main function
function make_map() {
let height = 0.4 * width;
let div = d3
.create("div")
.style("width", `${width}px`)
.style("height", `${height}px`)
.style("overflow", "hidden");
let svg = div
.append("svg")
.style("width", `${width}px`)
.style("height", `${height}px`)
.style("overflow", "hidden")
.style("background", "white");
// The map data is preprojected Albers Equal area for NC
let proj = d3
.geoIdentity()
.reflectY(true)
.fitSize([width, height], map_data.tracts);
let path = d3.geoPath().projection(proj);
let map = svg.append("g");
let tracts = map
.append("g")
.selectAll("path.tract")
.data(map_data.tracts.features)
.join("path")
.attr("d", path)
.attr("class", "tract")
.attr("id", (d, i) => `tract${i}`)
.attr("data-geoid", (d) => d.properties.GEOID)
.style("fill", function (d) {
return color_map.get(d.properties.GEOID);
})
.style("stroke-width", "0.2px")
.style("stroke-opacity", 0.2)
.style("stroke", "#000")
.style("stroke-linejoin", "round");
tracts
.on("mouseenter", function () {
d3.select(this).style("stroke-width", "1px").style("stroke-opacity", 1);
})
.on("mouseleave", function () {
d3.select(this)
.style("stroke-width", "0.2px")
.style("stroke-opacity", 0.2);
});
tracts.nodes().forEach((c) => {
tippy(c, {
allowHTML: true,
maxWidth: 420,
theme: "light",
content: table_map.get(c.getAttribute("data-geoid"))
});
});
let cities = map.append("g");
let city_names = cities
.selectAll("text.city")
.data(city_data)
.join("text")
.attr("class", "city")
.attr("x", (d) => proj([d.x, d.y])[0])
.attr("y", (d) => proj([d.x, d.y])[1])
.text((d) => d.city)
.style("font-family", "sans-serif")
.style("font-size", "14px")
.attr("pointer-events", "none");
let city_markers = cities
.selectAll("circle.city")
.data(city_data)
.join("circle")
.attr("class", "city")
.attr("cx", (d) => proj([d.x, d.y])[0])
.attr("cy", (d) => proj([d.x, d.y])[1])
.attr("r", 3)
.style("fill", "yellow")
.attr("pointer-events", "none");
svg.call(
d3
.zoom()
.extent([
[0, 0],
[width, height]
])
.translateExtent([
[0, 0],
[width, height]
])
.scaleExtent([1, 8])
.duration(750)
.on("zoom", function (evt) {
map.attr("transform", evt.transform);
map.selectAll(".highlight").remove();
// map
// .selectAll("path.tract")
// .style("stroke-width", `${0.2 / d3.event.transform.k}px`);
map.selectAll("circle.city").attr("r", `${3 / evt.transform.k}`);
map
.selectAll("text.city")
.style("font-size", `${14 / evt.transform.k ** 0.75}px`);
})
);
return div.node();
}
// The population data comes from the most recent five year
// American Community Survey estimates, which is 2018:
// https://www.census.gov/data/developers/data-sets/acs-5year.html
// https://api.census.gov/data/2018/acs/acs5/groups/B02001.html
raw_data = (await (await fetch(
'https://api.census.gov/data/2018/acs/acs5?get=NAME,B02001_001E,B02001_002E,B02001_003E&for=tract:*&in=state:37'
)).json()).slice(1)
// This map associates an HTML table with each tract GEOID
// to be shown as a tooltip.
table_map = {
let table_map = new Map();
raw_data.forEach(function([
place,
total,
white,
black,
state_fips,
county_fips,
tract_id
]) {
let div = d3.create('div');
let [tract, county, state] = place.split(', ');
tract = tract.slice(7);
div.append('h3').text(`${tract} in ${county}`);
//.style('color', 'white');
let table = div.append('table');
let head = table.append('thead').append('tr');
head.append('th').text('Race');
head.append('th').text('Population');
head.append('th').text('Percentage');
let combined_row = table.append('tr');
combined_row.append('td').text('All');
combined_row.append('td').text(`${total}`);
let white_row = table.append('tr');
white_row.append('td').text('White');
white_row.append('td').text(`${white}`);
white_row.append('td').text(`${d3.format('0.2f')((100 * white) / total)}%`);
let black_row = table.append('tr');
black_row.append('td').text('Black');
black_row.append('td').text(`${black}`);
black_row.append('td').text(`${d3.format('0.2f')((100 * black) / total)}%`);
let other_row = table.append('tr');
other_row.append('td').text('Other');
other_row.append('td').text(`${total - white - black}`);
other_row
.append('td')
.text(`${d3.format('0.2f')((100 * (total - white - black)) / total)}%`);
table_map.set(state_fips + county_fips + tract_id, div.node().innerHTML);
});
return table_map;
}
// Associate a color with each tract GEOID.
color_map = {
let color_map = new Map();
let max = d3.max(raw_data.map(o => parseFloat(o[1])));
raw_data.forEach(function([
place,
total,
white,
black,
state_fips,
county_fips,
tract_id
]) {
let geoid = state_fips + county_fips + tract_id;
if (total > 0) {
let population_density = density_map.get(geoid);
color_map.set(
geoid,
`rgba(${(255 * white) / total}, ${(255 * black) / total}, ${(255 *
(total - white - black)) /
total}, ${Math.pow(population_density / density_map.max, 0.25)})`
);
} else {
color_map.set(geoid, '#eee');
}
});
return color_map;
}
density_map = {
let density_map = new Map();
map_data.tracts.features
.map(c => c.properties)
.forEach(function(o) {
let geoid = o.GEOID;
let pop = pop_map.get(geoid).total;
let density = pop / o.ALAND;
density_map.set(geoid, density);
});
density_map.max = d3.max(Array.from(density_map.values()));
return density_map;
}
pop_map = {
let pop_map = new Map();
raw_data.forEach(function(o) {
pop_map.set(o[4] + o[5] + o[6], { total: o[1], white: o[2], black: o[3] });
});
return pop_map;
}
// Load the TopoJSON map file
map_data = {
let map_file = await FileAttachment("nc_tracts_topo.json").json();
let nc_tracts = topojson.feature(map_file, map_file.objects.nc_tracts);
return {
tracts: nc_tracts
};
}
// Display just a few cities
city_data = d3.csvParse(`city,lat,lon,x,y
Asheville,35.6,-82.55,-243640,3758538
Charlotte,35.2271,-80.8431,-89404,3714312
Durham,35.994,-78.8986,86782,3799379
Fayetteville,35.0527,-78.8784,89635,3694965
Raleigh,35.7796,-78.6382,110555,3775851
Wilmington,34.2104,-77.886,182013,3602891
Winston-Salem,36.0999,-80.2442,-34513,3810776`)
// tippy = require("https://unpkg.com/tippy.js@2.5.4/dist/tippy.all.min.js")
tippy = require("tippy.js@6")
topojson = require("topojson-client@3")
d3 = require('d3-selection@2', 'd3-array@2', 'd3-format@2', 'd3-geo@2', 'd3-zoom@2', 'd3-dsv@2')
tippy_style = html`<link rel="stylesheet" href="${await require.resolve(
`tippy.js/themes/light.css`
)}">`
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