Mount Greenwood Crime Rate Trends — Chicago
Mount Greenwood is a Far South Side neighborhood on the Worth and Evergreen Park borders, organized around 111th Street and Pulaski Road. Predominantly single-family ranch homes, with Mount Greenwood Park and the Daniel Wright Woods forest preserve as community anchors.
April 2026 was a quiet month in Mount Greenwood. No tracked category crossed an anomaly threshold — zero signals across all flag types — making this one of the calmest briefings the neighborhood has produced.
The 12-month picture is more varied. Burglary stands out structurally: 17 incidents over the current 12 months against 7 in the prior year, a 142.9% increase that predates this month and reflects a longer accumulation. Vandalism moved in the opposite direction, down 22.4% year-over-year (45 vs. 58). Other Larceny, the neighborhood's highest-volume category at 105 incidents, edged down 6.2%. Everything else — robbery, aggravated assault, motor vehicle theft — stayed within a few percentage points of prior-year levels.
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 April 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
Below the volume threshold for a reliable forecast — too few incidents in recent months to project from.
Homicide
Too low-volume per neighborhood for a reliable point forecast — see the rare-event and streak-break signals above instead.
Motor Vehicle Theft
Below the volume threshold for a reliable forecast — too few incidents in recent months to project from.
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.
Vandalism
How Mount Greenwood 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.”
Forest Glen
110 incidents over the past 12 months — 5 above Mount Greenwood's 105.
Open page →Fuller Park
98 incidents over the past 12 months — 7 below Mount Greenwood's 105.
Open page →Riverdale
114 incidents over the past 12 months — 9 above Mount Greenwood's 105.
Open page →Recurring local terms (last 12 months)
Top terms in incident descriptions for Mount Greenwood, 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 CPD's open dataset on the City of Chicago Open Data portal — IUCR-coded and mapped to 9 UCR-aligned categories (theft from vehicle isn't reliably separable in the public feed and rolls into other larceny). Aggregated to community area × 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.