Inner Richmond Crime Rate Trends — San Francisco
The Inner Richmond is a residential neighborhood north of Golden Gate Park, anchored by the Geary Boulevard and Clement Street commercial corridors. Long known for its dim sum restaurants, neighborhood bakeries, and a steady mix of long-established small businesses, it has a fog-belt climate and a lived-in pace.
Inner Richmond had a restrained June 2026 — two tracked signals across the neighborhood, split between a one-month below-trend move and a structural multi-month shift. The shape is broadly downward on property crime, with no spikes and no rare-event clusters.
Vandalism registered a below-trend signal this month: the trailing 12-month total stands at 74 incidents against a baseline of 124.33. Burglary's sustained-shift signal reflects something longer in the making — 59 incidents over the current 12 months versus 103 in the prior year, a 42.7% reduction. Theft from vehicle is also well below prior-year levels at 55 versus 81 (down 32.1%), though it didn't cross a signal threshold this month. Aggravated assault is the one category moving against that grain — up 150.0% year over year, though off a small base (20 current vs. 8 prior).
Notable signals 1
Vandalism
The past 12 months saw 74 incidents — about 40% below the 124 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.
- Burglary has reset to a lower baseline.
The trailing 12-month count is 59, down 43% from 103 the year before. If the trend holds another quarter, it will pull the multi-year baseline down.
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
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 Inner Richmond compares
Peer neighborhoods picked by closest 12-month vandalism 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 vandalism levels.”
Noe Valley
73 incidents over the past 12 months — 1 below Inner Richmond's 74.
Open page →Visitacion Valley
73 incidents over the past 12 months — 1 below Inner Richmond's 74.
Open page →Portola
80 incidents over the past 12 months — 6 above Inner Richmond's 74.
Open page →Recurring local terms (last 12 months)
Top terms in incident descriptions for Inner Richmond, 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.