DROP · ROBBERYMARCH 2026 BRIEFINGOAKLAND · 14.4K residents

Temescal Crime Rate Trends — Oakland

Temescal is a North Oakland commercial-and-residential neighborhood organized around the Telegraph Avenue and 51st Street commercial corridor, just south of Berkeley. Anchored by Temescal Alley, weekend street markets, and a cluster of food destinations.

ROBBERY · 24-MO COUNT03 2026 · 2
05912-mo avg: 1.6
TEMESCALCITYWIDE TREND (RESCALED)-39% 12MO YOY
0%MoM
-60%12mo YoY
19last 12mo
2this month
01 · TL;DR

Eight categories moved in Temescal this March — three registered as one-month below-trend signals and five as sustained structural shifts. The dominant pattern is broad, multi-year decline across violent and property crime, not a single quiet month.

Robbery, burglary, and motor vehicle theft all ran below trend, with robbery the sharpest: 19 incidents over the current 12 months against a baseline of 52.77 and a prior-year total of 47, down 59.6%. Burglary fell 50.4% year-over-year (59 vs. 119), and motor vehicle theft dropped 41.4% (116 vs. 198). Theft from vehicle is the one category moving against that grain — up 42.3% over the prior 12 months, 969 incidents vs. 681 — and stands out as the clearest counter-trend in an otherwise broadly contracting picture.

3 drops5 sustained shifts
02 · Notable signals

Notable signals 3

DROP · ROBBERY

Robbery

The past 12 months saw 19 incidents — about 64% below the 53 average from prior years.

DROP · BURGLARY

Burglary

The past 12 months saw 59 incidents — about 53% below the 125 average from prior years.

DROP · MOTOR VEHICLE THEFT

Motor Vehicle Theft

The past 12 months saw 116 incidents — about 55% below the 258 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.

Homicide-52%
2024-042026-03
Robbery-60%
2024-042026-03
Aggravated Assault+13%
2024-042026-03
Sexual Assaultbelow threshold
2024-042026-03
Burglary-50%
2024-042026-03
Theft from Vehicle+42%
2024-042026-03
Other Larceny-33%
2024-042026-03
Motor Vehicle Theft-41%
2024-042026-03
Vandalism-30%
2024-042026-03
Arsonbelow threshold
2024-042026-03
05 · Forecast

What next month likely looks like

Forecasts trained through March 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

APRIL 2026
Most likely 6 next month — likely between 0 and 14.
+22% vs 12-month average (≈4.9)

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

APRIL 2026
Most likely 13 next month — likely between 0 and 27.
+31% vs 12-month average (≈9.7)

Other Larceny

APRIL 2026
Most likely 27 next month — likely between 10 and 41.
+37% vs 12-month average (≈19.6)

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

APRIL 2026
Most likely 89 next month — likely between 31 and 148.
+10% vs 12-month average (≈80.8)

Vandalism

APRIL 2026
Most likely 7 next month — likely between 0 and 31.
49% vs 12-month average (≈13.1)
06 · Context & comps

How Temescal compares

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

SPATIAL SPILLOVER · NEW

Do crime spikes here spill over to adjacent neighborhoods?

Temescaldoesn'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.

Temescal historical spike-event spillover by crime category (3-month lookahead, adjacent neighborhoods via shared boundary).
CategorySpike eventsSame-category spillover
Homicide3— too few

Each row shows Temescal's historical spike events for that category, and how often any of its 7 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 Oakland); 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 Temescal, excluding generic crime taxonomy. Useful as texture — what kinds of specifics show up here that don't show up elsewhere.

grandforcespousemailfirearmweaponforcibleannoyingcallsdisturbpeaceterrorizeinflictinjurycreditdeathaccesscardcourtdangerousanotherpersonalunexplaineddateidentification
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
05961,19212am6am12pm6pm11pm

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

DAY OF WEEK · ALL CATEGORIES
09001,801MonTueWedThuFriSatSun
MONTH OF YEAR · ALL CATEGORIES
05071,013JanFebMarAprMayJunJulAugSepOctNovDec
08 · Methodology

How we built this page

Data → Anomalies → Forecast → Page

Incident data is pulled from OPD's CrimeWatch feed on Oakland Open Data, 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.