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.
One category moved in Piedmont Pines this month: burglary registered a below-trend signal against an otherwise flat backdrop. The month's shape is minimal, a single drop signal with every other tracked category within its normal range.
Burglary's 12-month total stands at 3 incidents, down from 19 in the prior 12 months, an 84.2% reduction year-over-year. Theft from vehicle and motor vehicle theft also show steep multi-year declines, 65.2% and 55.6% respectively, pointing to a sustained structural shift in property crime across the neighborhood rather than a single month's noise. Vandalism followed the same direction, down 60.0% against the prior year.
Notable signals 1
Burglary
The past 12 months saw 3 incidents — about 91% below the 33 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.
No sustained shifts surfaced this month.
What next month likely looks like
Forecasts trained through July 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 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.”
Jingletown
9 incidents over the past 12 months — 6 above Piedmont Pines's 3.
Open page →Clinton
12 incidents over the past 12 months — 9 above Piedmont Pines's 3.
Open page →Seminary Park
13 incidents over the past 12 months — 10 above Piedmont Pines's 3.
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 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.