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.
Five categories moved in Lone Mountain/USF this June — four ran below trend in the current month, plus one sustained structural shift. The pattern is broadly downward across property crime, with no spikes or rare events in the mix.
Vandalism, Motor Vehicle Theft, and Theft from Vehicle all registered below-trend signals, with the 12-month picture reinforcing the direction: vandalism is down 27.8% year-over-year (57 incidents vs. 79), theft from vehicle is down 32.0% (87 vs. 128), and motor vehicle theft is down 25.4% (47 vs. 63). The one category moving in the opposite direction is robbery, up 29.4% over the trailing 12 months — 22 incidents against 17 the prior year — though at low absolute volume.
Notable signals 4
Vandalism
The past 12 months saw 57 incidents — about 44% below the 101 average from prior years.
Motor Vehicle Theft
The past 12 months saw 47 incidents — about 59% below the 116 average from prior years.
Theft from Vehicle
The past 12 months saw 87 incidents — about 63% below the 237 average from prior years.
Aggravated Assault
The past 12 months saw 7 incidents — about 70% below the 23 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 87, down 32% from 128 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 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
60 incidents over the past 12 months — 3 above Lone Mountain/USF's 57.
Open page →Oceanview/Merced/Ingleside
60 incidents over the past 12 months — 3 above Lone Mountain/USF's 57.
Open page →Presidio Heights
44 incidents over the past 12 months — 13 below Lone Mountain/USF's 57.
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.