34:["$","title",null,{"children":"Heading North: Population Growth in Southcentral Alaska, 1990–2020 | Thomas Butcher | Observable"}] 3c:T6db,## Introduction Southcentral Alaska holds most of the state's people, and for decades nearly all of them lived in or around Anchorage. Since the 1990s, the Matanuska-Susitna (Mat-Su) Borough, the valley north of the city around Wasilla and Palmer, has grown quickly. This project asks whether the way the Anchorage region grows has changed, by looking not at borough totals but at where within each borough new residents settled. **I test the hypothesis that:** *Since 1990, population growth in the Anchorage region has moved out from the city and into a few Mat-Su Valley hot spots.* In the study area, Mat-Su’s share of the population grew from about 16% in 1990 to about 25% in 2020. It received about 41% of the region’s growth. Anchorage still added more people overall, so the shift toward Mat-Su is disproportionate, not total. The population grid is a modeled estimate, so I use it for patterns, not exact counts. ## Data Sources 1. **GHS-POP R2023A population grid** (European Commission, Joint Research Centre). Estimated population in 100 m cells for 1990, 2000, 2010 and 2020. Equal-size cells over time show where growth happened. It is modeled, not a head count. [data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe](https://data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe) 2. **U.S. Census Bureau county boundaries** (2020 cartographic boundary file). Outlines of the Municipality of Anchorage and the Mat-Su Borough, used to assign each cell to a borough. [census.gov cartographic boundary files](https://www.census.gov/geographies/mapping-files/time-series/geo/cartographic-boundary.html) Resources Used: Generative AI (Claude) was used for syntax and different methods with plots in Observable JavaScript. 3d:Te9f,{ const cutoff = 20; // cells that gained this many people or fewer are hidden const xCut = 200; // empty western edge is trimmed const years = [2000, 2010, 2020]; const popMax = Math.max(...data.flatMap(d => years.map(y => d[`pop${y}`]))); const top = Math.max(...data.flatMap(d => years.map(y => d[`gain${y}_vs1990`]))); const xr = [xCut, Math.max(...data.map(d => d.x_km)) + 1]; const yr = [Math.min(...data.map(d => d.y_km)) - 1, Math.max(...data.map(d => d.y_km)) + 1]; const places = [["Anchorage", 61.2176, -149.8997], ["Wasilla", 61.581, -149.439], ["Palmer", 61.600, -149.113]]; // legend: bar shaded exactly like the cells, with real people counts under it const legendTicks = [0, 100, 500, 1000, 2000, 4000]; const legendBar = (label, c1, c2) => `

${label}

${legendTicks.map(p => `${p.toLocaleString()}`).join("")} ${Math.round(popMax).toLocaleString()}

`; const root = document.createElement("div"); root.innerHTML = `

Year: 2020 Height = people gained since 1990. Shade = people living in the cell that year.

${legendBar("Anchorage: people in the 1 km cell", "#bfe3ff", "#0a5a9c")} ${legendBar("Mat-Su: people in the 1 km cell", "#f0a8a0", "#6e0a14")}

`; const plotDiv = root.querySelector("#plot"); function draw() { const yrv = years[+root.querySelector("#yr").value]; root.querySelector("#yrlab").textContent = yrv; const key = `gain${yrv}_vs1990`, popKey = `pop${yrv}`; const rows = data.filter(d => d[key] > cutoff && d.x_km >= xCut); // one trace per borough so each gets its own tooltip color const mk = (borough, bg) => ({ type: "mesh3d", ...cubesBorough(rows.filter(d => d.borough === borough), key, popKey, popMax), flatshading: true, hoverinfo: "text", hoverlabel: {bgcolor: bg, font: {color: "#ffffff"}} }); Plotly.react(plotDiv, [mk("Anchorage", "#0a5a9c"), mk("Mat-Su", "#6e0a14")], { title: `People gained, 1990 to ${yrv} (cells gaining more than ${cutoff})`, uirevision: "keep", scene: { xaxis: {range: xr, title: "east (km)"}, yaxis: {range: yr, title: "north (km)"}, zaxis: {range: [0, top], title: "People gained per 1 km²"}, aspectmode: "manual", aspectratio: {x: 1.6, y: 1, z: 0.5}, camera: {eye: {x: -0.95, y: -0.95, z: 0.55}, center: {x: 0, y: 0, z: -0.1}, up: {x: 0, y: 0, z: 1}}, annotations: places.map(([name, la, lo]) => { const c = peakNear(la, lo, key); return {x: c.x_km, y: c.y_km, z: c[key], text: name, showarrow: true, arrowhead: 2, ax: 0, ay: -35}; }) } }); } root.querySelector("#yr").oninput = draw; setTimeout(draw, 100); return root; }3e:T160d,{ const years = [2000, 2010, 2020], minBase = 20; const root = document.createElement("div"); root.innerHTML = `

Show: Change from 1990 to: 2020

`; const plotDiv = root.querySelector("#plot"); // --- fixed scales (computed once from all years) --- const f = g => Math.sign(g) * Math.log10(1 + Math.abs(g)); const lossMax = Math.max(...data.flatMap(d => years.map(y => Math.max(0, -d[`gain${y}_vs1990`])))); const gainMax = Math.max(...data.flatMap(d => years.map(y => d[`gain${y}_vs1990`]))); const baseRows = data.filter(d => d.pop1990 >= minBase); const lr = baseRows.flatMap(d => years.map(y => Math.log10(Math.max(d[`pop${y}`], 0.01) / d.pop1990))); const scales = { log: {cmin: -f(lossMax), cmax: f(gainMax), val: (d, y) => f(d[`gain${y}_vs1990`]), tv: [-100, -10, 0, 10, 100, 1000], tt: v => String(v), tpos: v => f(v), title: "People gained
per 1 km²"}, pct: {cmin: Math.min(...lr), cmax: Math.max(...lr), val: (d, y) => Math.log10(Math.max(d[`pop${y}`], 0.01) / d.pop1990), tv: [-50, 0, 50, 100, 200, 400, 600], tt: v => (v > 0 ? "+" : "") + v + "%", tpos: v => Math.log10(1 + v / 100), title: "Growth since 1990
(log scale)"} }; const scaleColors = (cmin, cmax) => { const p0 = -cmin / (cmax - cmin); return [[0, "#2166ac"], [p0, "#f7f7f7"], [p0 + (1 - p0) * 0.35, "#fdb863"], [p0 + (1 - p0) * 0.7, "#e08214"], [1, "#7f3b08"]]; }; const xr = [Math.min(...data.map(d => d.x_km)) - 1, Math.max(...data.map(d => d.x_km)) + 1]; const yr = [Math.min(...data.map(d => d.y_km)) - 1, Math.max(...data.map(d => d.y_km)) + 1]; const places = [["Anchorage", 61.2176, -149.8997], ["Wasilla", 61.581, -149.439], ["Palmer", 61.600, -149.113]]; const halfCells = (b, key) => { const r = data.filter(d => d.borough === b && d[key] > 0).sort((a, c) => c[key] - a[key]); const tot = r.reduce((t, d) => t + d[key], 0); let c = 0; const out = []; for (const d of r) { out.push(d); c += d[key]; if (c >= tot / 2) break; } return out; }; const sum = (b, k) => data.filter(d => d.borough === b).reduce((t, d) => t + d[k], 0); const med = a => { a = [...a].sort((x, y) => x - y); const n = a.length; return n ? (n % 2 ? a[(n - 1) / 2] : (a[n / 2 - 1] + a[n / 2]) / 2) : NaN; }; function draw() { const mode = root.querySelector("#mode").value, yrv = years[+root.querySelector("#yr").value]; root.querySelector("#yrlab").textContent = yrv; const key = `gain${yrv}_vs1990`, popKey = `pop${yrv}`, sc = scales[mode]; const all = data.filter(d => d.pop1990 > 0.5 || d[popKey] > 0.5); const main = mode === "pct" ? all.filter(d => d.pop1990 >= minBase) : all; const small = mode === "pct" ? all.filter(d => d.pop1990 < minBase) : []; const tip = d => `${d.borough}
1990: ${Math.round(d.pop1990)}
${yrv}: ${Math.round(d[popKey])}
change: ${Math.round(d[key])}` + (d.pop1990 >= minBase ? `
growth: ${Math.round(100 * d[key] / d.pop1990)}%` : ""); const mHalf = halfCells("Mat-Su", key), aHalf = halfCells("Anchorage", key); let sub; if (mode === "log") { const gm = sum("Mat-Su", key), ga = sum("Anchorage", key); const st = 100 * sum("Mat-Su", "pop1990") / (sum("Mat-Su", "pop1990") + sum("Anchorage", "pop1990")); sub = `Mat-Su received ${(100 * gm / (gm + ga)).toFixed(0)}% of the two areas' growth, starting with ${st.toFixed(0)}% of their people`; } else { const mm = med(baseRows.filter(d => d.borough === "Mat-Su").map(d => 100 * d[key] / d.pop1990)); const am = med(baseRows.filter(d => d.borough === "Anchorage").map(d => 100 * d[key] / d.pop1990)); sub = `Median growth in cells with ${minBase}+ people in 1990: Mat-Su ${mm.toFixed(0)}%, Anchorage ${am.toFixed(0)}%. Gray = 1990 base under ${minBase}, percent not meaningful`; } const traces = []; if (small.length) traces.push({type: "scatter", mode: "markers", name: `1990 base under ${minBase} people`, x: small.map(d => d.x_km), y: small.map(d => d.y_km), marker: {symbol: "square", size: 5, color: "#bdbdbd"}, text: small.map(tip), hoverinfo: "text"}); traces.push({type: "scatter", mode: "markers", name: "1 km cell", x: main.map(d => d.x_km), y: main.map(d => d.y_km), marker: {symbol: "square", size: 5, color: main.map(d => sc.val(d, yrv)), colorscale: scaleColors(sc.cmin, sc.cmax), cmin: sc.cmin, cmax: sc.cmax, colorbar: {title: sc.title, tickvals: sc.tv.map(sc.tpos), ticktext: sc.tv.map(sc.tt)}}, text: main.map(tip), hoverinfo: "text"}); Plotly.react(plotDiv, traces, { title: `${mode === "log" ? "People gained" : "Percent growth"} per 1 km cell, 1990 to ${yrv}
_${sub}`, xaxis: {title: "east (km)", range: xr, scaleanchor: "y"}, yaxis: {title: "north (km)", range: yr}, legend: {orientation: "h", y: -0.2}, uirevision: "keep", annotations: places.map(([name, la, lo]) => { const c = peakNear(la, lo, key); return {x: c.x_km, y: c.y_km, text: name, showarrow: true, arrowhead: 2, ax: -35, ay: -30, font: {size: 12}}; }) }); } root.querySelector("#mode").onchange = draw; root.querySelector("#yr").oninput = draw; setTimeout(draw, 100); return root; }3f:T483,{ const years = [2000, 2010, 2020]; const top = Math.max(...data.flatMap(d => years.map(y => d[`gain${y}_vs1990`]))); const root = document.createElement("div"); root.innerHTML = `

Year: 2020

Change from 1990 to: 2020

`; const plotDiv = root.querySelector("#plot"); function draw() { const yrv = years[+root.querySelector("#yr").value]; root.querySelector("#yrlab").textContent = yrv; const key = `gain${yrv}_vs1990`; const rows = data.filter(d => d.pop1990 > 0.5 || d[`pop${yrv}`] > 0.5); Plotly.react(plotDiv, [{type: "scatter", mode: "markers", x: rows.map(d => d.x_km), y: rows.map(d => d.y_km), marker: {symbol: "square", size: 5, color: rows.map(d => g(d[key])), colorscale: "RdBu", reversescale: true, cmin: -lim, cmax: lim, colorbar: {title: "sqrt of people gained"}}, text: rows.map(d => `${d.borough}
change: ${Math.round(d[key])}`), hoverinfo: "text"}], { title: `People gained per 1 km cell, 1990 to ${yrv} (Revision 5)`, xaxis: {title: "east (km)", range: xr, scaleanchor: "y"}, yaxis: {title: "north (km)", range: yr}, uirevision: "keep" }); } root.querySelector("#yr").oninput = draw; setTimeout(draw, 100); return root; }42:[{"id":"287dff612d2d02cb54551302575f6da0ed12d81e843d9c5c739d96acc160dbb4ac81850c886f63d616bcaa8d5c662e1e3800e675e62aa6f14068e13694898b91","href":"https://static.observableusercontent.com/files/287dff612d2d02cb54551302575f6da0ed12d81e843d9c5c739d96acc160dbb4ac81850c886f63d616bcaa8d5c662e1e3800e675e62aa6f14068e13694898b91","name":"observable_cells_1km.csv","type":"text/csv","lastModified":1792351332871,"size":273569},{"id":"3565651acc96cc99f6eb8abcd2a61193337b1da6ae81a297c31934bbf3ea801003557b926276c901a7d7264b7170809e0de53632b98c72c502a90f275bb004f7","href":"https://static.observableusercontent.com/files/3565651acc96cc99f6eb8abcd2a61193337b1da6ae81a297c31934bbf3ea801003557b926276c901a7d7264b7170809e0de53632b98c72c502a90f275bb004f7","name":"Screenshot 2026-10-18 211736.png","type":"image/png","lastModified":1792379857107,"size":141613},{"id":"f20220a336d8a8002a2a8f97287efdf4f1971daf9f9e9c4f511ab3bf1a9a96332060589fea0bd72dbf363b86c06ffa014877014c1adcc8cd758183aa96a89d15","href":"https://static.observableusercontent.com/files/f20220a336d8a8002a2a8f97287efdf4f1971daf9f9e9c4f511ab3bf1a9a96332060589fea0bd72dbf363b86c06ffa014877014c1adcc8cd758183aa96a89d15","name":"Screenshot 2026-10-18 211756.png","type":"image/png","lastModified":1792379876886,"size":114598}] 38:{"id":"c28628f24eec56f1","slug":"heading-north-population-growth-in-southcentral-alaska-1990-2020","title":"Heading North: Population Growth in Southcentral Alaska, 1990–2020","thumbnail":"2054e3f7c79a30db740ab38898a5063ba7a700cbbf10f368a1b2a6d5087c1100","access_level":"public","create_time":"$D2026-10-18T19:27:54.955Z","update_time":"$D2026-10-21T17:20:52.777Z","view_time":null,"first_public_time":"$D2026-10-18T22:39:18.952Z","has_unpublished_edits":null,"fork":false,"stars":1,"starred":false,"owner":{"type":"team","id":"6892435a406ffad9","login":"thomas-butcher","name":"Thomas Butcher","avatar_url":"https://avatars-next.observableusercontent.com/29c8bc8df0eb2be218365ecfea2a967467e030183be3eb4ad91ce9ce087ae457","tier":"starter_2024","bio":"","followed_by_you":false,"follows_you":false},"version":242,"publish_version":242,"role":null,"authored":false,"authors":[{"type":"individual","id":"5279211d076af583","login":"thomas-butcher","name":"Thomas Butcher","avatar_url":"https://avatars-next.observableusercontent.com/29c8bc8df0eb2be218365ecfea2a967467e030183be3eb4ad91ce9ce087ae457","tier":null,"bio":"","followed_by_you":false,"follows_you":false}],"members":[{"type":"team","id":"6892435a406ffad9","login":"thomas-butcher","name":"Thomas Butcher","avatar_url":"https://avatars-next.observableusercontent.com/29c8bc8df0eb2be218365ecfea2a967467e030183be3eb4ad91ce9ce087ae457","tier":"starter_2024","role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"titling":"auto","title":"Heading North: Population Growth in Southcentral Alaska, 1990–2020","cells":[{"id":1,"value":"# Heading North: Population Growth in Southcentral Alaska, 1990–2020","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2,"value":"$3c","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":17,"value":"# Change in Population in Anchorage and the Matanuska-Susitna Valley since 1990.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":7,"value":"$3d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":16,"value":"Anchorage still has the tallest bars, but Mat-Su's growth is spread along a long band of squares. By 2020 Mat-Su held about 41% of the two areas' growth since 1990, from a 16% starting share.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":19,"value":"# Population gained per 1km cell in Anchorage and the Mat-su Valley","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":8,"value":"$3e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":18,"value":"Anchorage's gains sit in a dense core, while Mat-Su's are spread across the Wasilla and Palmer area. The median Mat-Su square grew about 130% by 2020, compared with about 36% in Anchorage.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":35,"value":"## Here is the population mountains plot with simulated protanopia.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":34,"value":"await FileAttachment(\"Screenshot 2026-10-18 211736.png\").image()","pinned":false,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":37,"value":"## Here is the population raster plot with simulated protanopia.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":39,"value":"await FileAttachment(\"Screenshot 2026-10-18 211756.png\").image()","pinned":false,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":21,"value":"# Revisions","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":22,"value":"{\n const m = cubes(data.filter(d => d.pop2020 > 1), \"pop2020\");\n const div = document.createElement(\"div\");\n div.style.cssText = \"width:100%;height:650px\";\n Plotly.newPlot(div, [{\n type: \"mesh3d\", ...m, colorscale: \"Jet\", flatshading: true,\n colorbar: {title: \"People\"}\n }], {\n title: \"Anchorage and Mat-Su population, 2020 (Revision 1)\",\n scene: {aspectmode: \"manual\", aspectratio: {x: 1.6, y: 1, z: 0.5}}\n });\n return div;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":29,"value":"**Revision 1: total population, rainbow colors.**\n- Height and color both show population, so color adds nothing (Tufte's data-ink).\n- It shows size, not growth, so it can't test the hypothesis.\n- Rainbow colors are hard for color-blind readers, and Munzner ranks length above hue.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":24,"value":"{\n const m = cubes(data.filter(d => d.gain2020_vs1990 > 5), \"gain2020_vs1990\");\n const div = document.createElement(\"div\");\n div.style.cssText = \"width:100%;height:650px\";\n Plotly.newPlot(div, [{\n type: \"mesh3d\", ...m, colorscale: \"Cividis\", flatshading: true,\n colorbar: {title: \"People gained\"}\n }], {\n title: \"People gained, 1990 to 2020 (Revision 2)\",\n scene: {aspectmode: \"manual\", aspectratio: {x: 1.6, y: 1, z: 0.5},\n zaxis: {title: \"People gained per 1 km²\"}}\n });\n return div;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":30,"value":"**Revision 2: people gained since 1990.**\n- Height now shows growth, which is what the hypothesis is about (Munzner: start from the task).\n- Problem: only 2020 is shown, and bars still hide each other.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":25,"value":"$3f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":31,"value":"**Revision 3: year slider with fixed scales.**\n- A slider shows change over time. The scales stay fixed, so heights mean the same in every year.\n- Problem: the default camera lets tall bars hide the ones behind them.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":26,"value":"$40","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":32,"value":"**Revision 4: southwest camera, trimmed edge, place labels.**\n- The camera now looks from the southwest so Anchorage doesn't block Mat-Su.\n- Anchorage, Wasilla and Palmer are labeled on the chart.\n- Problem: nothing separates the two boroughs, and color still repeats height.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":27,"value":"$41","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":33,"value":"**Revision 5: flat map of change.**\n- A flat map shows every square with no hiding.\n- Problem: the color bar shows a square root of people gained, so a reader can't tell real counts.\n- It also doesn't show how big the gain is compared with what was already there.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3,"value":"data = FileAttachment(\"observable_cells_1km.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":5,"value":"Plotly = (await import(\"https://esm.sh/plotly.js-gl3d-dist-min@2.35.2\")).default","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":6,"value":"function cubes(rows, key) {\n const x = [], y = [], z = [], i = [], j = [], k = [], intensity = [];\n const s = 0.5; // half of a 1 km cell, in km\n const tri = [[0,1,2],[0,2,3],[4,6,5],[4,7,6],[0,4,5],[0,5,1],\n [1,5,6],[1,6,2],[2,6,7],[2,7,3],[3,7,4],[3,4,0]];\n for (const r of rows) {\n const h = r[key];\n if (!(h > 0)) continue; // skip empty cells and losses\n const b = x.length;\n for (const [dx, dy, dz] of [[-s,-s,0],[s,-s,0],[s,s,0],[-s,s,0],\n [-s,-s,h],[s,-s,h],[s,s,h],[-s,s,h]]) {\n x.push(r.x_km + dx); y.push(r.y_km + dy); z.push(dz); intensity.push(h);\n }\n for (const t of tri) { i.push(b + t[0]); j.push(b + t[1]); k.push(b + t[2]); }\n }\n return {x, y, z, i, j, k, intensity};\n}\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":10,"value":"function peakNear(lat, lon, key, km = 6) {\n let best = null;\n for (const d of data) {\n const dy = (d.lat - lat) * 111.2, dx = (d.lon - lon) * 111.2 * Math.cos(lat * Math.PI / 180);\n if (dx * dx + dy * dy <= km * km && (!best || d[key] > best[key])) best = d;\n }\n return best;\n}","pinned":false,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":13,"value":"shade = {\n const hex = h => [1, 3, 5].map(i => parseInt(h.slice(i, i + 2), 16));\n const ramps = {\n \"Anchorage\": [hex(\"#bfe3ff\"), hex(\"#0a5a9c\")], // sky blue, light to dark\n \"Mat-Su\": [hex(\"#f0a8a0\"), hex(\"#6e0a14\")] // dark red, light to dark\n };\n return (borough, t) => {\n const [a, b] = ramps[borough];\n return `rgb(${a.map((v, i) => Math.round(v + (b[i] - v) * t)).join(\",\")})`;\n };\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":14,"value":"function cubesBorough(rows, key, popKey, popMax) {\n const x = [], y = [], z = [], i = [], j = [], k = [], vertexcolor = [], text = [];\n const s = 0.5;\n const tri = [[0,1,2],[0,2,3],[4,6,5],[4,7,6],[0,4,5],[0,5,1],\n [1,5,6],[1,6,2],[2,6,7],[2,7,3],[3,7,4],[3,4,0]];\n for (const r of rows) {\n const h = r[key];\n if (!(h > 0)) continue;\n const b = x.length;\n const col = shade(r.borough, Math.sqrt(Math.min(1, r[popKey] / popMax)));\n const label = `${r.borough}
gained: ${Math.round(h)}
population: ${Math.round(r[popKey])}`;\n for (const [dx, dy, dz] of [[-s,-s,0],[s,-s,0],[s,s,0],[-s,s,0],\n [-s,-s,h],[s,-s,h],[s,s,h],[-s,s,h]]) {\n x.push(r.x_km + dx); y.push(r.y_km + dy); z.push(dz);\n vertexcolor.push(col); text.push(label);\n }\n for (const t of tri) { i.push(b + t[0]); j.push(b + t[1]); k.push(b + t[2]); }\n }\n return {x, y, z, i, j, k, vertexcolor, text};\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W42","stdlib":"2"}} 33:null