Piedmont Pines Crime Rate Trends — Oakland
Piedmont Pines is a hills neighborhood along Skyline Boulevard near Joaquin Miller and Redwood Regional parks. Predominantly mid-century single-family housing on heavily wooded, sloped streets at the city's eastern edge.
Four categories moved in Piedmont Pines this March — one drop, one spike, one sustained shift, and one streak break. The mix is split: the longer-term picture across property crime is broadly downward, but other larceny is pulling in the opposite direction.
Burglary is the clearest structural story, down 79.2% over the trailing 12 months (5 incidents vs. 24 in the prior year), with theft from vehicle off 54.2% and vandalism off 61.5% over the same window. Against that backdrop, other larceny stands out — 52 incidents in the current 12 months against a baseline of 39.73, and March registered a single-month spike. Aggravated assault also returned after a long absence, a streak break worth watching to see whether it repeats.
Notable signals 3
Other Larceny
The past 12 months saw 52 incidents — about 31% above the 40 average from prior years.
Aggravated Assault
First incident since February 2024 — a 2-year gap ended this month.
Burglary
The past 12 months saw 5 incidents — about 85% below the 34 average from prior years.
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.
What's been quietly true for a year
Spikes get attention. Sustained shifts shape policy. These are multi-quarter patterns where the past 12-month total differs meaningfully from the year before — they often precede the baseline resetting.
- Theft from Vehicle has reset to a lower baseline.
The trailing 12-month count is 22, down 54% from 48 the year before. If the trend holds another quarter, it will pull the multi-year baseline down.
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
Too low-volume per neighborhood for a reliable point forecast — see the rare-event and streak-break signals above instead.
Arson
Below the volume threshold for a reliable forecast — too few incidents in recent months to project from.
Burglary
Below the volume threshold for a reliable forecast — too few incidents in recent months to project from.
Homicide
Too low-volume per neighborhood for a reliable point forecast — see the rare-event and streak-break signals above instead.
Motor Vehicle Theft
Other Larceny
Robbery
Too low-volume per neighborhood for a reliable point forecast — see the rare-event and streak-break signals above instead.
Sexual Assault
Too low-volume per neighborhood for a reliable point forecast — see the rare-event and streak-break signals above instead.
Theft from Vehicle
Vandalism
Below the volume threshold for a reliable forecast — too few incidents in recent months to project from.
How Piedmont Pines 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.”
Clinton
58 incidents over the past 12 months — 6 above Piedmont Pines's 52.
Open page →Oakmore Highlands
58 incidents over the past 12 months — 6 above Piedmont Pines's 52.
Open page →Jingletown
35 incidents over the past 12 months — 17 below Piedmont Pines's 52.
Open page →Recurring local terms (last 12 months)
Top terms in incident descriptions for Piedmont Pines, excluding generic crime taxonomy. Useful as texture — what kinds of specifics show up here that don't show up elsewhere.
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.
How we built this page
Data → Anomalies → Forecast → Page
Incident data is pulled from SFPD's open dataset, 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.