Hegewisch Crime Rate Trends — Chicago
Hegewisch is a Far Southeast Side neighborhood on the Indiana border, founded in 1883 as a planned company town for the U.S. Rolling Stock Company. Bordered by the Wolf Lake forest preserve and the Calumet River industrial corridor; predominantly single-family homes with a small commercial strip on Baltimore Avenue.
August 2026 produced a single notable signal in Hegewisch: a sustained structural shift in vandalism, not a one-month spike but a multi-month pattern that has persisted long enough to register as a change in the neighborhood's baseline behavior. Every other tracked category was within its normal range.
Vandalism is up 78.9% over the trailing 12 months against the prior 12, 161 incidents vs. 90. That structural climb is the defining story for Hegewisch this briefing. Elsewhere, robbery fell 31.2% year-over-year (11 incidents vs. 16) and other larceny is down 22.3% (136 vs. 175), both moving in the opposite direction from vandalism. Aggravated assault and burglary both rose over the same period, but neither crossed the threshold for a tracked signal this month.
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.
- Vandalism is climbing.
The trailing 12-month count is 161, up 79% from 90 the year before. If the trend holds another quarter, it will pull the multi-year baseline up.
What next month likely looks like
Forecasts trained through August 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
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 Hegewisch compares
Peer neighborhoods picked by closest 12-month vandalism 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 vandalism levels.”
Lincoln Square
160 incidents over the past 12 months — 1 below Hegewisch's 161.
Open page →Dunning
158 incidents over the past 12 months — 3 below Hegewisch's 161.
Open page →Calumet Heights
157 incidents over the past 12 months — 4 below Hegewisch's 161.
Open page →Recurring local terms (last 12 months)
Top terms in incident descriptions for Hegewisch, 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.