DROP · BURGLARYAPRIL 2026 BRIEFINGSAN FRANCISCO · 12.1K residents

Chinatown Crime Rate Trends — San Francisco

San Francisco's Chinatown is the oldest Chinatown in North America, a dense residential and commercial enclave packed into roughly 24 city blocks around Grant and Stockton. Beyond its famous Dragon Gate and tourist storefronts, it remains a tightly-knit residential district of active markets, alleys, and family associations dating to the 19th century.

BURGLARY · 24-MO COUNT04 2026 · 4
0102012-mo avg: 4.8
CHINATOWNCITYWIDE TREND (RESCALED)-30% 12MO YOY
-33%MoM
-53%12mo YoY
57last 12mo
4this month
01 · TL;DR

Four tracked signals shaped April 2026 in Chinatown — one single-month below-trend signal and three sustained structural shifts. The structural pattern is mixed: burglary is down sharply over the trailing year, while aggravated assault has moved in the opposite direction as a multi-month trend, not a one-off.

Burglary is the clearest mover: the current 12-month total is 57 incidents against a baseline mean of 110, and 57 against 121 in the prior year — a 52.9% year-over-year drop that registers both as a single-month signal and a sustained structural decline. Aggravated assault is the counterweight, up 81.8% year-over-year (60 incidents vs. 33), a sustained shift that has been building across multiple months. Theft from vehicle and motor vehicle theft both ran lower on a 12-month basis — down 32.5% and 29.8% respectively — but neither crossed the anomaly threshold this period.

1 drop3 sustained shifts
02 · Notable signals

Notable signals 1

DROP · BURGLARY

Burglary

The past 12 months saw 57 incidents — about 48% below the 110 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-17%
2024-052026-04
Aggravated Assault+82%
2024-052026-04
Sexual Assaultbelow threshold
2024-052026-04
Burglary-53%
2024-052026-04
Theft from Vehicle-33%
2024-052026-04
Other Larceny0%
2024-052026-04
Motor Vehicle Theft-30%
2024-052026-04
Vandalism-8%
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 1 and 16.
+84% vs 12-month average (≈4.8)

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

MAY 2026
Most likely 3 next month — likely between 0 and 7.
3% vs 12-month average (≈2.8)

Other Larceny

MAY 2026
Most likely 20 next month — likely between 4 and 35.
12% vs 12-month average (≈22.4)

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 13 next month — likely between 0 and 36.
+16% vs 12-month average (≈11.4)

Vandalism

MAY 2026
Most likely 12 next month — likely between 2 and 21.
+9% vs 12-month average (≈11.3)
06 · Context & comps

How Chinatown compares

Peer neighborhoods picked by closest 12-month burglary 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 burglary levels.”

SPATIAL SPILLOVER · NEW

Do crime spikes here spill over to adjacent neighborhoods?

Chinatowndoesn'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.

Chinatown historical spike-event spillover by crime category (3-month lookahead, adjacent neighborhoods via shared boundary).
CategorySpike eventsSame-category spillover
Aggravated assault2— too few

Each row shows Chinatown's historical spike events for that category, and how often any of its 4 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 Chinatown, excluding generic crime taxonomy. Useful as texture — what kinds of specifics show up here that don't show up elsewhere.

lostfoundlockedpickpocketwarrantsuspiciousoccurrenceaggravatedadultfraudulentforceshopliftingmissingmoneycourtesyfalseweaponinvestigationcardforciblepersonationcreditphonebuildingterrorist
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
034268312am6am12pm6pm11pm

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

DAY OF WEEK · ALL CATEGORIES
07791,558MonTueWedThuFriSatSun
MONTH OF YEAR · ALL CATEGORIES
0450900JanFebMarAprMayJunJulAugSepOctNovDec
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.