Portola Crime Rate Trends — San Francisco
Portola is a working-class southeastern neighborhood, once known as the city's 'Garden District' for its 19th-century commercial nurseries. Today it is a quiet residential area along San Bruno Avenue, with a character distinct from the busier districts to the north.
Six categories moved in Portola this September, split evenly between one-month below-trend signals and longer-term structural shifts. The dominant shape is downward: three categories registered single-month drops and three others reflect multi-month sustained declines, meaning the broad retreat in property and violent crime here is not a one-month anomaly.
Burglary is the strongest signal, down 36.4% over the trailing 12 months, 21 incidents against 33 in the prior year. Theft from vehicle and vandalism followed the same direction, theft from vehicle down 51.2% (63 vs. 129) and vandalism down 20.0% (72 vs. 90). Other larceny is the one counter-move, up 40.7% over the same window, 159 incidents against 113, and stands apart from an otherwise broad and consistent decline across the neighborhood.
Notable signals 3
Burglary
The past 12 months saw 21 incidents — about 67% below the 64 average from prior years.
Theft from Vehicle
The past 12 months saw 63 incidents — about 73% below the 233 average from prior years.
Vandalism
The past 12 months saw 72 incidents — about 38% below the 115 average from prior years.
All categories, last 24 months
Each panel: recent monthly count vs. trailing 12-month context. MoM is the most recent month vs. the one before; 12mo YoY compares the trailing year to the year before that.
What's been quietly true for a year
Spikes get attention. Sustained shifts shape policy. These are multi-quarter patterns where the past 12-month total differs meaningfully from the year before — they often precede the baseline resetting.
- Theft from Vehicle has reset to a lower baseline.
The trailing 12-month count is 63, down 51% from 129 the year before. If the trend holds another quarter, it will pull the multi-year baseline down.
- Motor Vehicle Theft has reset to a lower baseline.
The trailing 12-month count is 62, down 47% from 117 the year before. If the trend holds another quarter, it will pull the multi-year baseline down.
- Other Larceny is climbing.
The trailing 12-month count is 159, up 41% from 113 the year before. If the trend holds another quarter, it will pull the multi-year baseline up.
What next month likely looks like
Forecasts trained through September 2026, with a likely range we're 95% confident the actual count will fall inside. Categories with too little recent volume — or violent categories at the neighborhood level — show no forecast and are surfaced through signals above instead. See the methodology page for the gating rules.
Aggravated Assault
Too low-volume per neighborhood for a reliable point forecast — see the rare-event and streak-break signals above instead.
Arson
Below the volume threshold for a reliable forecast — too few incidents in recent months to project from.
Burglary
Homicide
Too low-volume per neighborhood for a reliable point forecast — see the rare-event and streak-break signals above instead.
Motor Vehicle Theft
Other Larceny
Robbery
Too low-volume per neighborhood for a reliable point forecast — see the rare-event and streak-break signals above instead.
Sexual Assault
Too low-volume per neighborhood for a reliable point forecast — see the rare-event and streak-break signals above instead.
Theft from Vehicle
Vandalism
How Portola compares
Peer neighborhoods picked by closest 12-month burglary volume — a pragmatic v1 of peer matching. Demographic / housing-stock peer matching isn't built yet (we deliberately don't ingest income or race data alongside crime). Volume similarity has the right intuition: “neighborhoods experiencing comparable burglary levels.”
Recurring local terms (last 12 months)
Top terms in incident descriptions for Portola, excluding generic crime taxonomy. Useful as texture — what kinds of specifics show up here that don't show up elsewhere.
Hour-of-day, day-of-week, and seasonality
Distribution of bucketed incidents in this neighborhood across the full analysis window. Useful for routine context — shopping-strip thefts vs. late-night assaults read very differently when you can see when each typically happens.
How we built this page
Data → Anomalies → Forecast → Page
Incident data is pulled from SFPD's open dataset on DataSF, mapped to 10 NIBRS-aligned categories, and aggregated to neighborhood × category × month.Anomalies are surfaced using strict thresholds (~p < 0.01). Forecasts are Prophet with low-count gating; violent categories at the neighborhood level skip the forecast and show rare-event / streak signals instead.
Spike rule: 12-mo total > baseline mean + 2.5σ AND ≥ 20 incidents AND 6-mo confirms. Drop rule: 12-mo total < baseline mean − 2.5σ AND baseline mean ≥ 20. Rare event: any incident in the last 90 days, no prior comparable in ≥ 5 years.