Seacliff Crime Rate Trends — San Francisco
Seacliff is a small, affluent residential district at San Francisco's northwestern coast, set on streets that step down toward the Pacific along Sea Cliff Avenue. Its quiet, fog-prone character, panoramic views from the bluffs, and pocket of beach access at Baker Beach to the east set it apart from the rest of the Richmond District.
Seacliff's June 2026 briefing is defined by absence rather than activity. Three categories registered zero events over the tracking window — and with no spikes, drops, or sustained-shift signals anywhere in the mix, the month produced no anomalies in the traditional sense. The structural picture across property crime has shifted considerably over the past year, with volumes down sharply across multiple categories.
Theft from Vehicle fell 50.0% year-over-year (9 incidents in the current 12 months vs. 18 in the prior year), Motor Vehicle Theft is down 71.4% (4 vs. 14), and Vandalism dropped 54.5% (5 vs. 11). Other Larceny followed the same direction, off 45.5%. Burglary is the one category moving the other way — up 11.1%, though on a small base of 10 incidents over the trailing 12 months. Everything else ran within or below its recent range.
Notable signals 0
Nothing notable surfaced this month — every category sits within normal range against its baseline.
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.
No sustained shifts surfaced this month.
What next month likely looks like
Forecasts trained through June 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
Below the volume threshold for a reliable forecast — too few incidents in recent months to project from.
Homicide
Too low-volume per neighborhood for a reliable point forecast — see the rare-event and streak-break signals above instead.
Motor Vehicle Theft
Below the volume threshold for a reliable forecast — too few incidents in recent months to project from.
Other Larceny
Below the volume threshold for a reliable forecast — too few incidents in recent months to project from.
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
Below the volume threshold for a reliable forecast — too few incidents in recent months to project from.
Vandalism
Below the volume threshold for a reliable forecast — too few incidents in recent months to project from.
How Seacliff compares
Peer neighborhoods picked by closest 12-month arson 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 arson levels.”
Recurring local terms (last 12 months)
Top terms in incident descriptions for Seacliff, 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.