DROP · MOTOR VEHICLE THEFTAPRIL 2026 BRIEFINGSAN FRANCISCO · 16.0K residents

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.

MOTOR VEHICLE THEFT · 24-MO COUNT04 2026 · 6
071512-mo avg: 4.0
LONE MOUNTAIN/USFCITYWIDE TREND (RESCALED)-41% 12MO YOY
+100%MoM
-38%12mo YoY
48last 12mo
6this month
01 · TL;DR

Six categories moved in Lone Mountain/USF this April — four ran below trend in the current month, two registered as sustained multi-month structural shifts. The overall shape is broadly downward across property crime, with no spikes or rare events in the mix.

Motor vehicle theft leads the signals: the trailing 12-month total is 48 incidents against a baseline of 116.94, and down 37.7% versus the prior 12-month period of 77. Theft from vehicle and vandalism also ran below trend this month, with theft from vehicle off 43.1% year-over-year (82 current vs. 144 prior) and vandalism down 13.5% (64 vs. 74). The two sustained-shift signals point to structural change, not just a quiet April — the property-crime decline here has been building across multiple periods.

4 drops2 sustained shifts
02 · Notable signals

Notable signals 4

DROP · MOTOR VEHICLE THEFT

Motor Vehicle Theft

The past 12 months saw 48 incidents — about 59% below the 117 average from prior years.

DROP · THEFT FROM VEHICLE

Theft from Vehicle

The past 12 months saw 82 incidents — about 66% below the 240 average from prior years.

DROP · VANDALISM

Vandalism

The past 12 months saw 64 incidents — about 37% below the 102 average from prior years.

DROP · AGGRAVATED ASSAULT

Aggravated Assault

The past 12 months saw 6 incidents — about 74% below the 23 average from prior years.

03 · By category

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.

Homicidebelow threshold
2024-052026-04
Robbery+15%
2024-052026-04
Aggravated Assaultbelow threshold
2024-052026-04
Sexual Assaultbelow threshold
2024-052026-04
Burglary-32%
2024-052026-04
Theft from Vehicle-43%
2024-052026-04
Other Larceny+15%
2024-052026-04
Motor Vehicle Theft-38%
2024-052026-04
Vandalism-14%
2024-052026-04
Arsonbelow threshold
2024-052026-04
05 · Forecast

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

NO FORECAST

Too low-volume per neighborhood for a reliable point forecast — see the rare-event and streak-break signals above instead.

Arson

NO FORECAST

Below the volume threshold for a reliable forecast — too few incidents in recent months to project from.

Burglary

MAY 2026
Most likely 9 next month — likely between 0 and 20.
+46% vs 12-month average (≈6.3)

Homicide

NO FORECAST

Too low-volume per neighborhood for a reliable point forecast — see the rare-event and streak-break signals above instead.

Motor Vehicle Theft

MAY 2026
Most likely 6 next month — likely between 0 and 13.
+54% vs 12-month average (≈4.0)

Other Larceny

MAY 2026
Most likely 17 next month — likely between 8 and 26.
7% vs 12-month average (≈18.8)

Robbery

NO FORECAST

Too low-volume per neighborhood for a reliable point forecast — see the rare-event and streak-break signals above instead.

Sexual Assault

NO FORECAST

Too low-volume per neighborhood for a reliable point forecast — see the rare-event and streak-break signals above instead.

Theft from Vehicle

MAY 2026
Most likely 5 next month — likely between 0 and 17.
33% vs 12-month average (≈6.8)

Vandalism

MAY 2026
Most likely 5 next month — likely between 0 and 11.
+1% vs 12-month average (≈5.3)
06 · Context & comps

How Lone Mountain/USF compares

Peer neighborhoods picked by closest 12-month motor vehicle theft 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 motor vehicle theft levels.”

07 · Patterns

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.

shopliftingfoundadultmissingwarrantunlawfullockedlostsuspiciousunlockedinvestigationphoneforcibleforcefraudulentrecoveredbldgmoneyoccurrenceresidencefalsebuildingpossessionapartmenthouse
When does it happen?

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.

HOUR OF DAY · ALL CATEGORIES
026953812am6am12pm6pm11pm

Hour 0 is mildly inflated by reports without a known time defaulting to midnight — see methodology.

DAY OF WEEK · ALL CATEGORIES
05841,167MonTueWedThuFriSatSun
MONTH OF YEAR · ALL CATEGORIES
0345691JanFebMarAprMayJunJulAugSepOctNovDec
08 · Methodology

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.