SPIKE · OTHER LARCENYSEPTEMBER 2026 BRIEFINGSAN FRANCISCO · 12.0K residents

Lakeshore Crime Rate Trends — San Francisco

Lakeshore is a quiet southwestern neighborhood organized around freshwater Lake Merced, the Stonestown Galleria shopping center, and the SF State campus on its eastern edge. It has a more suburban character than most of the city, with single-family homes, golf courses, and easy access to the Pacific coastline.

OTHER LARCENY · 24-MO COUNT09 2026 · 37
0357012-mo avg: 45.2
LAKESHORECITYWIDE TREND (RESCALED)-6% 12MO YOY
-39%MoM
+41%12mo YoY
542last 12mo
37this month
01 · TL;DR

Three signals shaped September 2026 in Lakeshore: one spike and two sustained structural shifts. The dominant move is in other larceny, which registered both a one-month spike and a multi-month structural shift upward, an unusual combination that points to a durable change rather than a single noisy month. Theft from vehicle, by contrast, has settled into a sustained decline. The rest of the tracked categories, robbery, aggravated assault, burglary, motor vehicle theft, and vandalism, all came in within normal range.

Other larceny now stands at 542 incidents over the trailing 12 months, up 41.1% against the prior 12-month total of 384. Theft from vehicle tells the opposite story: 78 incidents in the current 12 months against 128 in the year before, a 39.1% reduction that has held long enough to qualify as a structural shift rather than a monthly fluctuation. Those two categories are pulling in opposite directions, and the net picture for Lakeshore is one of mixed movement concentrated in larceny, not a broad neighborhood-wide trend.

1 spike2 sustained shifts
02 · Notable signals

Notable signals 1

SPIKE · OTHER LARCENY

Other Larceny

The past 12 months saw 542 incidents — about 70% above the 319 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
2024-102026-09
Robbery+11%
2024-102026-09
Aggravated Assault0%
2024-102026-09
Sexual Assaultbelow threshold
2024-102026-09
Burglary-21%
2024-102026-09
Theft from Vehicle-39%
2024-102026-09
Other Larceny+41%
2024-102026-09
Motor Vehicle Theft-28%
2024-102026-09
Vandalism+2%
2024-102026-09
Arsonbelow threshold
2024-102026-09
05 · Forecast

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

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

OCTOBER 2026
Most likely 3 next month — likely between 0 and 8.
−13% vs 12-month average (≈3.8)

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

OCTOBER 2026
Most likely 8 next month — likely between 0 and 16.
+57% vs 12-month average (≈5.3)

Other Larceny

OCTOBER 2026
Most likely 40 next month — likely between 21 and 59.
−12% vs 12-month average (≈45.2)

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

OCTOBER 2026
Most likely 10 next month — likely between 0 and 35.
+59% vs 12-month average (≈6.5)

Vandalism

OCTOBER 2026
Most likely 7 next month — likely between 0 and 14.
−4% vs 12-month average (≈7.5)
06 · Context & comps

How Lakeshore compares

Peer neighborhoods picked by closest 12-month other larceny 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 other larceny levels.”

SPATIAL SPILLOVER · NEW

Do crime spikes here spill over to adjacent neighborhoods?

Lakeshoredoesn't have enough spike history in any single category for a stable spillover rate yet (we want at least 5 events). The table below lists what we have.

Lakeshore historical spike-event spillover by crime category (3-month lookahead, adjacent neighborhoods via shared boundary).
CategorySpike eventsSame-category spillover
Other larceny2— too few

Each row shows Lakeshore's historical spike events for that category, and how often any of its 3 adjacent neighborhoods spiked the same category within the next 3 months. A high same-category rate suggests a shock that travels (e.g. theft crews moving across San Francisco); a low rate means spikes here tend to be local to the neighborhood. Categories with fewer than 5 historical spike events are listed but their rates are suppressed.

07 · Patterns

Recurring local terms (last 12 months)

Top terms in incident descriptions for Lakeshore, excluding generic crime taxonomy. Useful as texture — what kinds of specifics show up here that don't show up elsewhere.

shopliftingwarrantlostbuildingpettyrecoveredfraudulentinvestigationphonepossessionsuspiciousfoundlicenseunlawfulconvictionlockedprioraggravatedcardplatecreditfalseoccurrenceofficeraccess
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
038176112am6am12pm6pm11pm

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

DAY OF WEEK · ALL CATEGORIES
07101,419MonTueWedThuFriSatSun
MONTH OF YEAR · ALL CATEGORIES
0439878JanFebMarAprMayJunJulAugSepOctNovDec
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.