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.
August 2026 produced a single tracked signal in Piedmont Pines: burglary ran below trend, and it was the only category that moved enough to register. The rest of the tracked categories, theft from vehicle, other larceny, motor vehicle theft, and vandalism, were all within range this month, though the 12-month picture across each of them is also running well below prior-year levels.
Burglary is the sharpest story in the 12-month data: 2 incidents in the current trailing year against 20 in the year before, a 90.0% reduction. Theft from vehicle is down 52.6% (18 vs. 38) and vandalism down 57.9% (8 vs. 19) over the same window. Motor vehicle theft fell 38.7% year over year. The structural direction across property crime in Piedmont Pines is broadly downward, and August's single signal fits that pattern rather than breaking from it.
Notable signals 1
Burglary
The past 12 months saw 2 incidents — about 94% 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 August 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
8 incidents over the past 12 months — 6 above Piedmont Pines's 2.
Open page →Clinton
11 incidents over the past 12 months — 9 above Piedmont Pines's 2.
Open page →Seminary Park
11 incidents over the past 12 months — 9 above Piedmont Pines's 2.
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.