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
Jan 5, 2022
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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 HexapolisThirty 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
COVID curves
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hex maps
function make_map() {
let max_size = 1200;
let break_size = 640; // Use alternative tooltip when tless than this size.
let map_width = width < max_size ? width : max_size;
let height = 0.575 * map_width;
let div = d3
.create("div")
.style("width", `${map_width}px`)
.style("height", `${height}px`)
.style("overflow", "hidden")
.style("position", "relative");
let svg = div
.append("svg")
.style("overflow", "hidden")
.attr("viewBox", [0, 0, map_width, height]);
let proj = d3
.geoIdentity()
.reflectY(true)
.fitSize([map_width, height], stateTiles);
let path = d3.geoPath().projection(proj);
let map = svg.append("g");
let density_map = new Map();
map
.selectAll("path.tile")
.data(stateTiles.features)
.join("path")
.attr("class", "tile")
.attr("data-fips", (o) => o.properties.fips)
.attr("data-name", (o) => o.properties.name)
.attr("d", path)
.attr("fill", function (d) {
let this_covid_data = covid_data.filter(
(o) => o.fips == d.properties.fips
)[0];
let rolling_mean = d3.mean(
this_covid_data.actualsTimeseries.slice(-14).map((o) => o.newCases)
);
let population = this_covid_data.population;
let density = rolling_mean / population;
density_map.set(d.properties.fips, density);
return d3.interpolateReds((density / max_case_density) ** 1);
})
.attr("data-density", (o) => density_map.get(o.properties.fips))
.attr("stroke-width", "0.8px")
.attr("stroke", function (d) {
return "#000";
})
.attr("stroke-linejoin", "round");
map
.selectAll("text")
.data(stateTiles.features)
.join("text")
.text((o) => o.properties.abbr)
.attr("font-size", `${0.022 * map_width}px`)
.attr("x", function (o) {
return path.centroid(o)[0];
})
.attr("text-anchor", "middle")
.attr("y", function (o) {
return path.centroid(o)[1];
})
.attr("dy", 0.008 * map_width)
.attr("pointer-events", "none");
if (map_width > break_size) {
// Use Tippy, if big enough
map
.selectAll("path.tile")
.nodes()
.forEach(function (t) {
let graph = make_graph(t.getAttribute("data-fips"), {
parameter: "newCases",
roll_by: 14
});
let content = html`<div><span style="font-weight:bold">${t.getAttribute(
"data-name"
)}</span>: Approximately ${Math.round(
100000 * t.getAttribute("data-density")
)} new cases per 100,000</div>${graph}`;
tippy(t, {
content: content,
theme: "light",
maxWidth: 500
});
});
} else {
// Overlay a transparent DIV, if smaller
map
.selectAll("path.tile")
.on("pointerenter", function (evt, d) {
let fips = d.properties.fips;
let graph_width = 0.96 * map_width;
let graph_height = 0.96 * height;
let graph_div = div
.append("div")
.attr("id", `graph${fips}`)
.style("width", `${graph_width}px`)
.style("height", `${graph_height}px`)
.style("position", "absolute")
.style("top", `${0.02 * graph_height}px`)
.style("left", `${0.02 * graph_width}px`)
.style("pointer-events", "none");
div
.append("div")
.attr("class", "close")
.style("width", "20px")
.style("height", "20px")
.style("background-color", "#eee")
.style("border", "solid 1px black")
.style("position", "absolute")
.style("left", "0px")
.style("top", "0px")
.style("text-align", "center")
.text("X")
.on("pointerdown", function (evt) {
evt.stopPropagation();
div.selectAll(".close").remove();
graph_div.remove();
});
graph_div.append(() =>
make_graph(fips, {
graph_width: graph_width,
graph_height: graph_height,
roll_by: 14
})
);
})
.on("mouseout", function (evt, d) {
let fips = d.properties.fips;
div.select(`#graph${fips}`).remove();
});
}
return div.node();
}
function make_graph(fips, opts = {}) {
let {
show_axes = true,
graph_width = 450, // 0.7 * width,
graph_height = 250, // 0.7 * width,
margin = { left: 50, right: 15, bottom: 25 },
strokeOpacity = 1,
roll_by = 1,
background_opacity = 0.7
} = opts;
let this_covid_data = covid_data.filter(o => o.fips == fips)[0];
let population = this_covid_data.population;
this_covid_data = this_covid_data.actualsTimeseries.filter(o => o.newCases);
let values = this_covid_data.map(o => o.newCases);
let rolled = rolling_sum(values, roll_by);
let y_max = (100000 * d3.max(rolled)) / (roll_by * population);
this_covid_data.map((o, i) => (o.rolled = rolled[i]));
this_covid_data = this_covid_data.slice(roll_by - 1);
let x_scale = d3
.scaleTime()
.domain([this_covid_data[0].date, this_covid_data.slice(-1)[0].date])
.range([margin.left, graph_width - margin.right]);
let y_scale = d3
.scaleLinear()
.domain([0, y_max])
.range([graph_height - margin.bottom, 0]);
let pts_to_path = d3
.line()
.x(d => x_scale(d.date))
.y(d => y_scale((100000 * d.rolled) / (roll_by * population)));
let svg = d3
.create("svg")
.attr("width", graph_width)
.attr("height", graph_height);
svg
.append("rect")
.attr("width", graph_width)
.attr("height", graph_height)
.attr("fill", "white")
.attr("fill-opacity", background_opacity);
svg
.append("path")
.attr("d", pts_to_path(this_covid_data))
.style("stroke", "black")
.style("stroke-opacity", strokeOpacity)
.style("stroke-width", 2)
.style("stroke-linejoin", "round")
.style("fill", "none");
if (show_axes) {
svg
.append("g")
.attr("transform", `translate(0,${graph_height - margin.bottom})`)
.call(
d3
.axisBottom(x_scale)
.ticks(6)
.tickFormat(d3.timeFormat("%b %y"))
);
svg
.append("g")
.attr("transform", `translate(${margin.left}, 0)`)
.call(d3.axisLeft(y_scale));
}
return svg.node();
}
// Modfied from https://observablehq.com/@d3/moving-average
function rolling_sum(values, N) {
let i = 0;
let sum = 0;
let sums = new Float64Array(values.length).fill(NaN);
for (let n = Math.min(N - 1, values.length); i < n; ++i) {
sum += values[i];
}
for (let n = values.length; i < n; ++i) {
sum += values[i];
sums[i] = sum;
sum -= values[i - N + 1];
}
return sums;
}
max_vaccination_density = d3.max(
covid_data.map(o => o.actuals.vaccinationsInitiated / o.population)
)
max_case_density = d3.max(
covid_data.map(
o =>
d3.mean(o.actualsTimeseries.slice(-14).map(o => o.newCases)) /
o.population
)
)
covid_data = {
let parseDate = d3.utcParse("%Y-%m-%d");
return (await (await fetch(
'https://api.covidactnow.org/v2/states.timeseries.json?apiKey=b496b11f1f5347aa88bc5573e536eef8'
)).json()).map(function(o) {
o.actualsTimeseries.forEach(d => (d.date = parseDate(d.date)));
return o;
});
}
stateTiles = {
let stateTiles = await FileAttachment("stateTiles2.json").json();
let tiles = topojson.feature(stateTiles, stateTiles.objects.tiles);
return tiles;
}
style = html`<link style="display: none" rel="stylesheet" href="${await require.resolve(
`tippy.js/themes/light.css`
)}">`
import { Radio } from '@observablehq/inputs'
tippy = require("tippy.js@6")
topojson = require('topojson-client@3')
import { legend } from "@d3/color-legend"
d3 = require('d3@6')
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.
