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pop_by_shelter = Plot.plot(
(() => {
const n = 3; // number of facet columns
const keys = Array.from(d3.union(housing_counts.map((d) => d.location)));
const index = new Map(keys.map((key, i) => [key, i]));
const fx = (key) => index.get(key) % n;
const fy = (key) => Math.floor(index.get(key) / n);
return {
fx: { axis: null },
fy: { axis: null },
marks: [
Plot.line(housing_counts, {
x: "date",
y: "count",
fx: (d) => fx(d.location),
fy: (d) => fy(d.location)
}),
Plot.text(keys, { fx, fy, frameAnchor: "top-left", dx: 6, dy: 6 }),
Plot.frame()
]
};
})()
)
housing_counts
Type Table, then Shift-Enter. Ctrl-space for more options.
housing_counts = expanded_housing_counts.filter(
(d) =>
![
"SHELTER",
"SHELTER TOTAL",
"STAGING AREA TOTAL",
"Minors Currently in Shelters",
"Minors Currently in Staging Area"
].includes(d.location)
)
expanded_housing_counts = {
const transposedData = [];
let housing_type = "staging";
raw_housing_census.forEach(function (d) {
for (var key in d) {
var obj = {};
if (key !== "STAGING AREA") {
if (d["STAGING AREA"] === "SHELTER") {
housing_type = "shelter";
}
obj.identifier = key.substring(0, 10);
obj.date = new Date(key.substring(0, 10));
obj.count = d[key];
obj.location = d["STAGING AREA"];
obj.type = housing_type;
transposedData.push(obj);
}
}
});
return transposedData
.filter((d) => d.count)
.sort((a, b) => a.date.getTime() - b.date.getTime());
}
raw_housing_census = await d3.csv(
"https://raw.githubusercontent.com/fgregg/asylum-dashboard-data/main/data/housing_census.csv",
d3.autoType
)
movement_to_date = {
const transposedData = [];
raw_movement_to_date.forEach(function (d) {
for (var key in d) {
var obj = {};
if (key !== "") {
obj.identifier = key;
obj.date = new Date(key);
obj.count = d[key];
obj.type = d["* = potential data entry error"];
transposedData.push(obj);
}
}
});
return transposedData;
}
raw_movement_to_date = await d3.csv(
"https://raw.githubusercontent.com/fgregg/asylum-dashboard-data/main/data/movement_to_date.csv",
d3.autoType
)
grand_exit_rate = d3.mean(exit_rates.map((d) => d.rate))
last_date = shelter_entrances[shelter_entrances.length - 1].end
shelter_entrances
age_pyramid = shelter_entrances.map((d) => ({
days_in_system: (last_date.getTime() - d.end.getTime()) / (1000 * 3600 * 24),
count:
d.count *
Math.exp(
(-grand_exit_rate * (last_date.getTime() - d.end.getTime())) /
(1000 * 3600 * 24)
)
}))
Plot.plot({
y: { label: "people per days in system" },
color: { legend: true },
marks: [
Plot.line(age_pyramid, {
curve: "step-before",
x: "days_in_system",
y: "count"
})
]
})
d3.sum(age_pyramid.filter((d) => d.days_in_system > 60).map((d) => d.count)) /
d3.sum(age_pyramid.map((d) => d.count))
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.
