Every ZIP within 16 miles of the school in Boston, mapped by rent, transit commute, reported crime, nightlife and who lives there. Built from public data; set a budget and see what fits.
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生活風格Python · JavaScriptGitHub @stephs-repos上線中營運者未確認
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一分鐘完成驗證Where to live while at North Bennet Street School: ZIP-level map of rent, commute, crime and neighbourhood life from public data
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取自儲存庫 README
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檢視完整 README ↗Where to live while at North Bennet Street School (150 North St, Boston). A single HTML page maps every ZIP code within about 16 miles of the school by rent, transit commute, reported crime, nearby venues and amenities, and who lives there. It also has an adjustable best-fit score.
uv run build.py # reuses anything already downloaded into data/raw
uv run build.py --refresh # downloads every source again
uv run build.py --deploy # build, then publish to GitHub Pages
The live map is at https://stephs-repos.github.io/boston-housing/. --deploy pushes out/site/ (the page, its LinkedIn/Open Graph preview image web/og.png, and .nojekyll) as the only commit on the gh-pages branch, which GitHub Pages serves.
The output is out/site/index.html, one self-contained page (plus out/boston-housing-map.html, the same page without the document wrapper, for hosts that add their own). A first run downloads about 300 MB and takes 2–10 minutes; most of that is waiting on the public OpenStreetMap (Overpass) servers. The steps after the download take about 10 seconds.
| Step | Script | What it does |
|---|---|---|
| fetch | pipeline/fetch.py | Downloads every source into data/raw/ and skips files already there |
| base | pipeline/base.py | ZIP shapes within 26 km of the school, HUD and Zillow rents, MBTA lines and stations → data/build/base.json |
| commute | pipeline/commute.py | Weekday-morning minutes from every MBTA stop to the school, from the GTFS timetable (subway and bus) → stop_minutes.json |
| layers | pipeline/layers.py | Adds commute, crime, Census and amenity values per ZIP → layers.json |
| page | build.py | Puts web/head.html, web/body.html and web/app.js together with the data into the page |
Every build fetches a fresh USD/CAD rate (the one download that is never reused), so rebuilding and republishing is how the C$ figures stay current. The page can't fetch it while someone is viewing it, because published pages can't call outside services.
Data years are set in pipeline/config.py. Change them there when a source publishes a new year: HUD in October, FBI in late summer, Census ACS in December.