How Parking SF builds the map.

Parking SF is based on public San Francisco parking meter and mobility datasets. It does not rely on a private live parking API and it does not invent real-time availability.

1. Metered blocks become the core geography

The pipeline starts with SF Open Data parking meter locations and groups meters into metered street blocks. Each block gets a centroid, a simplified path, and meter counts for map rendering.

2. Transactions become a typical week

Historical transaction counts are aggregated into 168 slots, covering every day of week and hour of day. Slot indexing is kept consistent as dow * 24 + hour, where Monday starts at 0.

3. Enforcement schedules control trust

During enforced meter hours, Parking SF uses meter-driven occupancy estimates. Outside enforcement windows, it avoids implying empty streets and instead blends in off-hours parking pressure.

4. 311 pressure fills the blind spots

Parking-related 311 activity provides a signal for where off-hours parking still feels contested, even when meters are off.

5. Bay Wheels adds mobility context

Bike-share demand is optional, but useful. It helps show whether neighborhood movement patterns line up with parking pressure changes.

6. Isochrones stay in the backend

Parking SF uses Apple Maps for the native iPhone map, but reachability polygons are a backend concern. When a local Valhalla instance is available, the backend computes isochrones and the app overlays them on top of the map.

Data sources and citations

Parking SF cites the same upstream datasets that the backend pipeline uses. These are the authoritative sources behind the public methodology.

• SF Open Data: Parking Meters: Active meter locations, street-block grouping inputs, and neighborhood context.
• SF Open Data: Parking Meter Operating Schedules: Meter operating schedules used to determine which hours should rely on metered demand.
• SF Open Data: 311 Cases: Parking enforcement complaints used to estimate off-hours parking pressure.
• SF Open Data: Parking Supply - Street Segment Data: Street-segment parking supply reference used as a stable context layer for block estimates.
• Bay Wheels GBFS Station Information: Station metadata used for the Bay Wheels overlay and station matching.
• Bay Wheels Monthly Trip Data: Recent monthly trip files used to build a typical-week bike-share demand overlay.

Freshness, cadence, and coverage

The core model is designed around a rolling 90-day lookback for parking meter activity and 311 parking complaints whenever the dataset is rebuilt. The Bay Wheels overlay uses the most recent 3 monthly trip files available at build time.

Coverage is strongest on San Francisco metered street blocks because that is where public source data is consistent. Private garages, private lots, and true live curb availability are outside the scope of the product.

Ownership and accountability

The methodology, product site, and app-level behavior are maintained within Laqen LLC. That keeps the public explanation, support content, and software behavior aligned instead of splitting the product story across unrelated operators.

How to interpret the map

During enforced meter hours, block estimates are driven by meter activity. Outside those windows, Parking SF shifts to off-hours parking pressure instead of implying that the street is empty. That keeps the map honest even when the city is not actively metering demand.