Oakmore Highlands Crime Rate Trends — Oakland
Oakmore Highlands is a foothill neighborhood between Park Boulevard and the Mountain Boulevard ridge, organized around the Sausal Creek watershed and Park Boulevard. Single-family housing on sloped, wooded streets, with the Oakmore Pool and Sausal Creek Trail as community anchors.
March 2026 was a quiet month in Oakmore Highlands. No category crossed an anomaly threshold — zero tracked signals across the entire month, which itself is the most useful read on the data: the multi-year declines are now structural enough that nothing broke from trend in either direction.
The 12-month totals show how far volumes have moved. Theft from Vehicle is down 73.8% against the prior year — 11 incidents vs. 42. Robbery fell from 11 to 6, a 45.5% decline. Burglary, Motor Vehicle Theft, and Vandalism are each down between 31.8% and 33.3%. Other Larceny is the lone category running above its prior-year pace, at 58 incidents vs. 54, a 7.4% difference — the only number pointing upward in an otherwise broad property-crime decline.
Notable signals 0
Nothing notable surfaced this month — every category sits within normal range against its baseline.
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 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
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 Oakmore Highlands 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 — 0 below Oakmore Highlands's 58.
Open page →Piedmont Pines
52 incidents over the past 12 months — 6 below Oakmore Highlands's 58.
Open page →Montclair
69 incidents over the past 12 months — 11 above Oakmore Highlands's 58.
Open page →Recurring local terms (last 12 months)
Top terms in incident descriptions for Oakmore Highlands, 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.