wolfiesch

wolfiesch / sf-parking-heatmap

Public

Temporal heatmap of SF metered parking - typical-week occupancy from 206M+ meter transactions, with bike share and isochrone overlays.

sfparking.wolfie.gg dataviz deck-gl maplibre open-data parking
30
3
100% credibility
Found Apr 11, 2026 at 30 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
TypeScript
AI Summary

An interactive visualization of typical metered parking occupancy in San Francisco, aggregated from millions of public parking meter transactions into hourly weekly profiles per city block.

How It Works

1
🗺️ Discover SF Parking Map

You hear about a handy map that shows parking busyness across San Francisco by day and hour, helping you find spots easily.

2
🌐 Try the live map

Visit the website to zoom into neighborhoods and see colors reveal where parking is free, moderate, or tough at any time.

3
🚗 Spot easy parking

Pick rush hour on a weekday and green areas pop up showing blocks with plenty of open spots nearby.

4
💻 Get your own copy

Download the map to your computer to explore offline or tweak it for your favorite spots.

5
📊 Refresh city data

Pull the latest parking info straight from San Francisco's records so your map stays current.

6
▶️ Start your map

Open the map on your screen and scrub through the week or play it to watch demand pulse.

✅ Park like a local

Now you always know the best times and streets for hassle-free parking anywhere in the city.

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Star Growth

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AI-Generated Review

What is sf-parking-heatmap?

This TypeScript app delivers a spatial temporal heatmap of SF metered parking occupancy, crunched from 206M+ meter transactions into typical-week profiles across 28k blocks. Users scrub through 168 time slots (7 days x 24 hours) to watch demand pulse via deck.gl layers on MapLibre—from city heatmaps to neighborhood 3D columns and street paths—plus bike share overlays and isochrone reachability. No API keys needed; data pipelines pull straight from SF Open Data SODA endpoints.

Why is it gaining traction?

It nails multi-scale viz: zoom out for spatio temporal heatmaps, in for block details with enforcement schedules and 311 pressure blends. Time playback, delta comparisons, address search with radius overlays, and optional Valhalla Docker for drive/bike/walk isochrones make it instantly playable. 30 stars reflect niche appeal for real-world mapping demos over toy examples.

Who should use this?

Mapping devs prototyping urban dashboards or parking apps; data viz folks mining open gov datasets; SF cyclists checking bike+parking correlations before rides. Ideal for React/deck.gl stacks needing production-ready temporal heatmap patterns.

Verdict

Grab it for a battle-tested open data viz blueprint—MIT licensed, docker-compose ready, live at sfparking.wolfie.gg—but at 1.0% credibility and 30 stars, treat as inspiration over drop-in. Run the Python pipeline once for fresh data; maturity suits experiments, not enterprise.

(198 words)

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