DROP · MOTOR VEHICLE THEFTAPRIL 2026 BRIEFINGDENVER · 8.9K residents

Cheesman Park Crime Rate Trends — Denver

Cheesman Park is the dense residential neighborhood surrounding the eponymous park, just east of Capitol Hill. Pre-war high-rise apartment buildings line the park's edges; the side streets are a mix of Victorians, bungalows, and four-story walk-ups.

MOTOR VEHICLE THEFT · 24-MO COUNT04 2026 · 3
091712-mo avg: 4.8
CHEESMAN PARKCITYWIDE TREND (RESCALED)-34% 12MO YOY
-40%MoM
-37%12mo YoY
58last 12mo
3this month
01 · TL;DR

Two signals surfaced in Cheesman Park this April — one short-term drop and one sustained structural shift, both in the same category. Motor vehicle theft is doing the work this month, accounting for both tracked signals and pointing to a longer-term change rather than a single quiet week.

Motor vehicle theft has fallen 37.0% over the trailing 12 months, from 92 incidents to 58, against a multi-year baseline of 120.56 — the sustained-shift signal reflects that structural gap, not just a one-month dip. The rest of the tracked categories were within range: burglary is down 12.3% year-over-year, vandalism down 13.6%, and robbery down 28.6%, but none of those moves crossed the threshold for a signal this month.

1 drop1 sustained shift
02 · Notable signals

Notable signals 1

DROP · MOTOR VEHICLE THEFT

Motor Vehicle Theft

The past 12 months saw 58 incidents — about 52% below the 121 average from prior years.

03 · By category

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.

Homicidebelow threshold
2024-052026-04
Robbery-29%
2024-052026-04
Aggravated Assault+5%
2024-052026-04
Burglary-12%
2024-052026-04
Theft from Vehicle-3%
2024-052026-04
Other Larceny-7%
2024-052026-04
Motor Vehicle Theft-37%
2024-052026-04
Vandalism-14%
2024-052026-04
Arsonbelow threshold
2024-052026-04
05 · Forecast

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

NO FORECAST

Too low-volume per neighborhood for a reliable point forecast — see the rare-event and streak-break signals above instead.

Arson

NO FORECAST

Below the volume threshold for a reliable forecast — too few incidents in recent months to project from.

Burglary

MAY 2026
Most likely 5 next month — likely between 0 and 9.
22% vs 12-month average (≈5.9)

Homicide

NO FORECAST

Too low-volume per neighborhood for a reliable point forecast — see the rare-event and streak-break signals above instead.

Motor Vehicle Theft

MAY 2026
Most likely 6 next month — likely between 0 and 11.
+16% vs 12-month average (≈4.8)

Other Larceny

MAY 2026
Most likely 9 next month — likely between 4 and 15.
20% vs 12-month average (≈11.5)

Robbery

NO FORECAST

Too low-volume per neighborhood for a reliable point forecast — see the rare-event and streak-break signals above instead.

Theft from Vehicle

MAY 2026
Most likely 11 next month — likely between 5 and 17.
2% vs 12-month average (≈11.3)

Vandalism

MAY 2026
Most likely 10 next month — likely between 3 and 18.
+20% vs 12-month average (≈8.5)
06 · Context & comps

How Cheesman Park 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.”

SPATIAL SPILLOVER · NEW

Do crime spikes here spill over to adjacent neighborhoods?

When Cheesman Park has spiked burglary historically (7 events on record), an adjacent neighborhood spiked the same category within 3 months 100% of the time. The strongest-travelling categories sit at the top of the table.

Cheesman Park historical spike-event spillover by crime category (3-month lookahead, adjacent neighborhoods via shared boundary).
CategorySpike eventsSame-category spillover
Burglary7100%
Other larceny683.3%

Each row shows Cheesman Park's historical spike events for that category, and how often any of its 5 adjacent neighborhoods spiked the same category within the next 3 months. A high same-category rate suggests a shock that travels (e.g. theft crews moving across Denver); a low rate means spikes here tend to be local to the neighborhood. Categories with fewer than 5 historical spike events are listed but their rates are suppressed.

07 · Patterns

Recurring local terms (last 12 months)

Top terms in incident descriptions for Cheesman Park, excluding generic crime taxonomy. Useful as texture — what kinds of specifics show up here that don't show up elsewhere.

itemsforceresidencedrugsimplebldgorderpartsbicycleinjurethreatscrimesweaponposscourtaggravatedshoplifttrespassingdisturbingpeacebusinessfraudparaphernaliaillegaldangerous
When does it happen?

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.

HOUR OF DAY · ALL CATEGORIES
011322612am6am12pm6pm11pm

Hour 0 is mildly inflated by reports without a known time defaulting to midnight — see methodology.

DAY OF WEEK · ALL CATEGORIES
0262524MonTueWedThuFriSatSun
MONTH OF YEAR · ALL CATEGORIES
0167334JanFebMarAprMayJunJulAugSepOctNovDec
08 · Methodology

How we built this page

Data → Anomalies → Forecast → Page

Incident data is pulled from Denver Open Data — DPD's NIBRS-coded crime offenses on ArcGIS Hub — mapped to 9 NIBRS-aligned categories (sexual assault is excluded because DPD redacts victim-bearing rows from the public feed). The feed publishes a 5-year rolling window so the analysis baseline starts at 2021-01. Aggregated to statistical 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.