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.
April 2026 was a structurally quiet month for Seacliff — no tracked category crossed an anomaly threshold, and three categories registered zero events over the period. The dominant story is a broad, multi-year property crime decline across the neighborhood rather than any single-month move.
Theft from Vehicle is down 47.6% against the prior 12 months (11 incidents vs. 21), Vandalism is down 53.8% (6 vs. 13), and Motor Vehicle Theft is down 81.2% (3 vs. 16). Burglary and Other Larceny also ran lower year-over-year. Every tracked category in this briefing is below its prior-year level; nothing moved in the opposite direction.
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 April 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.