Chatsworth Crime Rate Trends — Los Angeles
Chatsworth is the far northwest San Fernando Valley neighborhood at the foot of the Santa Susana Mountains, organized around Devonshire Street and Topanga Canyon Boulevard. Anchored by Stoney Point Park, the Chatsworth Metrolink station, and historic Western film locations in the surrounding hills.
Three categories moved in Chatsworth this March — all three as sustained structural shifts downward, with no spikes, no rare events, and nothing running above trend. The shape is a broad, multi-month property and violent-crime retreat, not a single quiet month.
Burglary leads the structural story: 155 incidents over the current 12 months against 272 in the prior year, down 43.0%. Motor vehicle theft shows a similar arc — 90 current vs. 165 prior, down 45.5%. Vandalism rounds out the three sustained shifts at 195 vs. 289, down 32.5%. Every other tracked category was within its normal range.
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.
- Burglary has reset to a lower baseline.
The trailing 12-month count is 155, down 43% from 272 the year before. If the trend holds another quarter, it will pull the multi-year baseline down.
- Motor Vehicle Theft has reset to a lower baseline.
The trailing 12-month count is 90, down 46% from 165 the year before. If the trend holds another quarter, it will pull the multi-year baseline down.
- Vandalism has reset to a lower baseline.
The trailing 12-month count is 195, down 33% from 289 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
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
How Chatsworth 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.”
Recurring local terms (last 12 months)
Top terms in incident descriptions for Chatsworth, 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.