Green Meadows Crime Rate Trends — Los Angeles
Green Meadows is a South LA residential neighborhood around 92nd Street and Avalon Boulevard, between Florence and Watts. Predominantly single-family bungalows on a tight grid, anchored by Green Meadows Recreation Center and the 92nd Street Elementary corridor.
Five categories moved in Green Meadows this March — three one-month below-trend signals and two sustained multi-month structural shifts. The dominant shape is downward across both violent and property crime, with no spikes or rare events in the mix.
Motor vehicle theft leads the signals: the trailing 12-month total is 216 incidents against a prior-year count of 435, a 50.3% reduction year-over-year. Aggravated assault and theft from vehicle both ran below trend as well — aggravated assault is down 16.9% over the same window (250 vs. 301), and theft from vehicle is down 21.8% (140 vs. 179). The two sustained-shift signals point to structural change, not just a quiet month.
Notable signals 3
Motor Vehicle Theft
The past 12 months saw 216 incidents — about 50% below the 432 average from prior years.
Aggravated Assault
The past 12 months saw 250 incidents — about 27% below the 343 average from prior years.
Theft from Vehicle
The past 12 months saw 140 incidents — about 38% below the 226 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 216, down 50% from 435 the year before. If the trend holds another quarter, it will pull the multi-year baseline down.
- Burglary has reset to a lower baseline.
The trailing 12-month count is 47, down 44% from 84 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 Green Meadows 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.”
Hyde Park
219 incidents over the past 12 months — 3 above Green Meadows's 216.
Open page →Central-Alameda
220 incidents over the past 12 months — 4 above Green Meadows's 216.
Open page →Vermont Knolls
226 incidents over the past 12 months — 10 above Green Meadows's 216.
Open page →Recurring local terms (last 12 months)
Top terms in incident descriptions for Green Meadows, 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.