Lake Balboa Crime Rate Trends — Los Angeles
Lake Balboa is a central San Fernando Valley neighborhood organized around the 80-acre Lake Balboa and the surrounding Anthony C. Beilenson Park, west of Van Nuys. Predominantly single-family ranch homes on a tight grid, with the Sepulveda Dam recreation area on its eastern edge.
March 2026 was a narrow month in Lake Balboa — one tracked signal, one category, one direction. Theft from vehicle registered as a sustained shift downward, meaning the change is structural across the trailing 12 months, not a single quiet week.
Theft from vehicle is down 36.1% against the prior year, 85 incidents in the current 12-month window against 133 in the 12 before. Every other category in the neighborhood — robbery, aggravated assault, burglary, vandalism, motor vehicle theft, other larceny, sexual assault — ran without triggering a signal this month, all within their normal ranges. The sustained-shift read on theft from vehicle is the one substantive move; the rest of the month held flat.
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.
- Theft from Vehicle has reset to a lower baseline.
The trailing 12-month count is 85, down 36% from 133 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 Lake Balboa compares
Peer neighborhoods picked by closest 12-month theft from vehicle 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 theft from vehicle levels.”
Chesterfield Square
86 incidents over the past 12 months — 1 above Lake Balboa's 85.
Open page →Arlington Heights
82 incidents over the past 12 months — 3 below Lake Balboa's 85.
Open page →El Sereno
82 incidents over the past 12 months — 3 below Lake Balboa's 85.
Open page →Recurring local terms (last 12 months)
Top terms in incident descriptions for Lake Balboa, 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.