Lone Mountain/USF Crime Rate Trends — San Francisco
Lone Mountain is a small residential district built around the University of San Francisco's hilltop campus and its distinctive St. Ignatius Church spires. The neighborhood mixes student housing with leafy single-family streets at the boundary of the Anza Vista, Inner Richmond, and Western Addition districts.
Four categories moved in Lone Mountain/USF this September, three one-month below-trend signals and one sustained structural shift. The overall shape is broadly downward across property crime, with no spikes or rare events in the mix.
Vandalism is the strongest signal, down 36.7% over the trailing 12 months, 50 incidents against 79 in the prior year. Theft from vehicle fell even further in proportional terms, dropping 35.7% year over year, 72 incidents against 112. Motor vehicle theft added a third below-trend reading, off 15.7% to 43 incidents. Robbery was the one flat category, 18 incidents in both the current and prior 12-month windows, and every other tracked category also ran below its prior-year pace.
Notable signals 3
Vandalism
The past 12 months saw 50 incidents — about 50% below the 101 average from prior years.
Theft from Vehicle
The past 12 months saw 72 incidents — about 69% below the 233 average from prior years.
Motor Vehicle Theft
The past 12 months saw 43 incidents — about 62% below the 113 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 72, down 36% from 112 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 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 Lone Mountain/USF 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.”
Inner Sunset
56 incidents over the past 12 months — 6 above Lone Mountain/USF's 50.
Open page →Noe Valley
64 incidents over the past 12 months — 14 above Lone Mountain/USF's 50.
Open page →Presidio Heights
34 incidents over the past 12 months — 16 below Lone Mountain/USF's 50.
Open page →Recurring local terms (last 12 months)
Top terms in incident descriptions for Lone Mountain/USF, 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.