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.
NC Congressional DistrictsTrivariate choropleth for racial composition in the USRacial distribution in NCWNC 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 StatesMutliple bubble maps for CSV dataWorld population bubble mapReproductive rights and ... ??Gerrymandering Hexapolis by handVisualizations of gun ownership dataIncome inequality in North Carolina
Plotting a few points on a map
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Forum responses
map = {
let map = d3.create("svg").attr("width", w).attr("height", h);
map
.selectAll("path")
.data(contiguous_states.features)
.join("path")
.attr("d", path)
.attr("fill", "lightgray")
.attr("stroke", "#fff")
.attr("stroke-width", 1);
map
.selectAll("circle")
.data(largest_cities)
.join("circle")
.attr("cx", (c) => c.x)
.attr("cy", (c) => c.y)
.attr("r", (c) => 5 * (c.population / largest_cities.max_pop) ** 0.3)
.attr("fill", "black")
.attr("fill-opacity", 0.5)
.attr("stroke", "black")
.attr("data-name", (c) => c.name)
.attr("data-population", (c) => c.population)
.nodes()
.forEach(function (c) {
tippy(c, {
content: `
<div>
<h3>${c.getAttribute("data-name")}</h3>
<div>Population: ${c.getAttribute("data-population")}</div>
</div>`,
theme: "light-border",
allowHTML: true
});
});
return map.node();
}
path = d3.geoPath().projection(projection)
projection = d3.geoAlbers().fitSize([w, h], contiguous_states)
h = 0.625 * w
w = d3.min([width, 1000])
largest_cities = {
let largest_cities = await FileAttachment("largest_cities@1.json").json();
// Here's where we use the projection to translate the lat/lon data to xy
// coordinates. We then simply attach those xy coordinates to our data.
largest_cities.forEach(function (c) {
let xy = projection(c.lonlat);
c.x = xy[0];
c.y = xy[1];
});
largest_cities.max_pop = d3.max(largest_cities.map((c) => c.population));
return largest_cities;
}
// The map data comes from the US Census Bureau:
// https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html
// That data was simplified a bit and converted to TopoJSON using MapShaper:
// https://mapshaper.org
contiguous_states = {
let contiguous_states = await FileAttachment(
"contiguous_states_tigerline.json"
).json();
contiguous_states = topojson.feature(
contiguous_states,
contiguous_states.objects.states
);
return contiguous_states;
}
tippy_style = html`<link style="display: none" rel="stylesheet" href="${await require.resolve(
`tippy.js/themes/light-border.css`
)}">`
tippy = require("tippy.js@6")
largest_cities
largest_cities.map((row) => ({
name: row.name,
population: row.population,
lat: row.lonlat[1],
lon: row.lonlat[0]
}))
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