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 August briefing, three as one-month below-trend signals and one as a multi-month sustained shift. The shape is broadly downward across property crime, with no spikes or rare events in the mix.
Vandalism leads the signals: the trailing 12-month total of 56 incidents is down 30.0% against the prior year's 80. Theft from vehicle shows the sharpest year-over-year gap, 77 incidents in the current 12 months against 119 in the year before, a drop of 35.3%. Motor vehicle theft also ran below trend, 47 incidents versus 57 in the prior period. Everything else, including burglary and other larceny, came in within a narrower range of its prior-year pace.
Notable signals 3
Vandalism
The past 12 months saw 56 incidents — about 44% below the 101 average from prior years.
Theft from Vehicle
The past 12 months saw 77 incidents — about 67% below the 234 average from prior years.
Motor Vehicle Theft
The past 12 months saw 47 incidents — about 59% below the 114 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 77, down 35% from 119 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 August 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
57 incidents over the past 12 months — 1 above Lone Mountain/USF's 56.
Open page →Oceanview/Merced/Ingleside
61 incidents over the past 12 months — 5 above Lone Mountain/USF's 56.
Open page →Inner Richmond
66 incidents over the past 12 months — 10 above Lone Mountain/USF's 56.
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.