DROP · VANDALISMAPRIL 2026 BRIEFINGSAN FRANCISCO · 37.1K residents

Excelsior Crime Rate Trends — San Francisco

Excelsior is a working-class residential neighborhood in southeastern San Francisco, defined by the commercial spine of Mission Street and rows of single-family homes climbing the surrounding hills. It has a quieter, suburban feel relative to the city center, with deep neighborhood roots along the Mission Street corridor.

VANDALISM · 24-MO COUNT04 2026 · 11
0122312-mo avg: 9.2
EXCELSIORCITYWIDE TREND (RESCALED)-21% 12MO YOY
0%MoM
-26%12mo YoY
110last 12mo
11this month
01 · TL;DR

Eight categories moved in Excelsior this April — four ran below trend in the month and four registered as sustained structural shifts. The overall shape is a broad property and violent crime decline, not a single outlier pulling the average down.

Vandalism leads the top signals: the trailing 12-month total of 110 incidents is well below the 148 recorded in the prior year, a 25.7% decline. Robbery and theft from vehicle both also ran below trend — robbery is down 36.6% over the same window (52 incidents vs. 82), and theft from vehicle is down 54.9% (106 vs. 235). The four sustained-shift signals indicate these moves aren't just a quiet April; the structural pattern has been building across multiple months.

4 drops4 sustained shifts
02 · Notable signals

Notable signals 4

DROP · VANDALISM

Vandalism

The past 12 months saw 110 incidents — about 38% below the 176 average from prior years.

DROP · ROBBERY

Robbery

The past 12 months saw 52 incidents — about 43% below the 92 average from prior years.

DROP · THEFT FROM VEHICLE

Theft from Vehicle

The past 12 months saw 106 incidents — about 66% below the 315 average from prior years.

DROP · BURGLARY

Burglary

The past 12 months saw 40 incidents — about 55% below the 90 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-37%
2024-052026-04
Aggravated Assault-32%
2024-052026-04
Sexual Assaultbelow threshold
2024-052026-04
Burglary-58%
2024-052026-04
Theft from Vehicle-55%
2024-052026-04
Other Larceny+2%
2024-052026-04
Motor Vehicle Theft-42%
2024-052026-04
Vandalism-26%
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 5 next month — likely between 0 and 12.
+39% vs 12-month average (≈3.3)

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 22 next month — likely between 6 and 38.
+110% vs 12-month average (≈10.4)

Other Larceny

MAY 2026
Most likely 16 next month — likely between 4 and 27.
+17% vs 12-month average (≈13.8)

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 13 next month — likely between 0 and 27.
+51% vs 12-month average (≈8.8)

Vandalism

MAY 2026
Most likely 15 next month — likely between 7 and 24.
+60% vs 12-month average (≈9.2)
06 · Context & comps

How Excelsior 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.”

07 · Patterns

Recurring local terms (last 12 months)

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

licensewarrantrecoveredshopliftingplateforceaggravatedfraudulentdrivinginvestigationlostadultlockedfalsemoneyfoundpossessiontrafficmissingofficerweaponordersuspiciousrestrainingpersonation
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
038777312am6am12pm6pm11pm

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

DAY OF WEEK · ALL CATEGORIES
08171,634MonTueWedThuFriSatSun
MONTH OF YEAR · ALL CATEGORIES
0442883JanFebMarAprMayJunJulAugSepOctNovDec
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.