Harvard Heights Crime Rate Trends — Los Angeles
Harvard Heights is a South LA neighborhood organized around Pico Boulevard and Western Avenue. A historic district of Craftsman bungalows and early-20th-century apartment buildings.
Four categories moved in Harvard Heights this March — one below-trend single-month drop and three sustained structural shifts, all running lower than the prior period. The structural pattern is broadly downward across property crime, with the sustained shifts carrying more weight than any single month's movement.
Motor vehicle theft is the most prominent signal: 75 incidents in the current 12 months against 156 in the prior year, down 51.9%. Theft from vehicle is also in sustained decline — 92 current vs. 151 prior, down 39.1%. Burglary and vandalism follow the same direction, down 31.3% and 33.6% respectively; aggravated assault is the only category that held flat, up just 1.6% year over year.
Notable signals 1
Motor Vehicle Theft
The past 12 months saw 75 incidents — about 49% below the 147 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.
- Motor Vehicle Theft has reset to a lower baseline.
The trailing 12-month count is 75, down 52% from 156 the year before. If the trend holds another quarter, it will pull the multi-year baseline down.
- Theft from Vehicle has reset to a lower baseline.
The trailing 12-month count is 92, down 39% from 151 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 99, down 34% from 149 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 Harvard Heights compares
Peer neighborhoods picked by closest 12-month motor vehicle theft 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 motor vehicle theft levels.”
Lake Balboa
74 incidents over the past 12 months — 1 below Harvard Heights's 75.
Open page →Mar Vista
73 incidents over the past 12 months — 2 below Harvard Heights's 75.
Open page →Hollywood Hills
72 incidents over the past 12 months — 3 below Harvard Heights's 75.
Open page →Recurring local terms (last 12 months)
Top terms in incident descriptions for Harvard Heights, 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.