Walkability contour maps from government open data for rental search

Problem

When searching for rental properties, walkability is a key factor but hard to evaluate. You end up cross-referencing Google Maps, public transport timetables, and real estate listings manually. Each suburb requires checking train and tram routes, calculating walk times to stops, and comparing median rents -- a process that takes hours per area and still misses the spatial picture of how transit access radiates outward from stops.

Solution

Step 1: Source government open data

Most state and local governments publish transport and rental data as open APIs or CSV downloads.

# Example: Victorian government open data portal
curl -o gtfs.zip "https://data.ptv.vic.gov.au/downloads/gtfs.zip"
curl -o rental_medians.csv "https://data.vic.gov.au/rental-report-quarterly.csv"

Step 2: Generate walkability contours

Use Turf.js to create isochrone-style contour polygons around each transit stop at 5, 10, and 15-minute walking distances.

import * as turf from "@turf/turf";

function walkabilityContour(stopCoords, minuteRadius) {
  const walkSpeedKmH = 4.8;
  const radiusKm = (walkSpeedKmH * minuteRadius) / 60;
  return turf.circle(stopCoords, radiusKm, { units: "kilometers", steps: 64 });
}

// Generate 5, 10, 15 minute contours for each stop
const contours = stops.flatMap((stop) => [
  { feature: walkabilityContour(stop.coords, 5), minutes: 5 },
  { feature: walkabilityContour(stop.coords, 10), minutes: 10 },
  { feature: walkabilityContour(stop.coords, 15), minutes: 15 },
]);

Step 3: Overlay rental median data

// Color suburbs by median rent, overlay walkability contours
map.addSource("rental-medians", {
  type: "geojson",
  data: rentalGeoJson,
});

map.addLayer({
  id: "rent-heatmap",
  type: "fill",
  source: "rental-medians",
  paint: {
    "fill-color": ["interpolate", ["linear"], ["get", "median_rent"],
      300, "#2ecc71", 500, "#f1c40f", 800, "#e74c3c"],
    "fill-opacity": 0.4,
  },
});

Step 4: Deploy to GitHub Pages

npm run build
git subtree push --prefix dist origin gh-pages

Why It Works

Government open data portals provide authoritative, regularly updated datasets that would be prohibitively expensive to compile independently. Turf.js handles the geospatial math for walking-distance contours without needing a routing API, keeping the app free to host. The visual overlay of walkability zones on top of rental pricing lets you instantly identify high-value corridors where affordable rent meets strong transit access -- something no single real estate site provides.

Context

This pattern was used to build a rental finder deployed to GitHub Pages using Victorian government open data
GTFS (General Transit Feed Specification) is the standard format for public transport data and is available in most cities worldwide
Turf.js runs entirely in the browser so no backend is needed for the geospatial calculations
The same approach works for cycling infrastructure, school zones, or any point-of-interest proximity analysis
Quarterly rental median updates mean the data stays reasonably current without manual maintenance