SPIKE · OTHER LARCENYAUGUST 2026 BRIEFINGDENVER · 7.2K residents

University Hills Crime Rate Trends — Denver

University Hills is a south Denver neighborhood between Yale Avenue and Hampden Avenue, organized around University Hills Plaza and South Colorado Boulevard. Predominantly mid-century single-family residential with the namesake shopping center and apartment-building corridors along Colorado.

OTHER LARCENY · 24-MO COUNT08 2026 · 17
0408112-mo avg: 45.7
UNIVERSITY HILLSCITYWIDE TREND (RESCALED)+5% 12MO YOY
-55%MoM
+31%12mo YoY
548last 12mo
17this month
01 · TL;DR

August 2026 brought two distinct signals in University Hills, both concentrated in the same category. Other larceny produced the month's strongest single-month reading and also registers as a sustained structural shift, meaning the elevated volume is not just a one-month outlier but a pattern building across the trailing 12 months. Every other tracked category was within normal range.

Other larceny stands at 548 incidents over the current 12 months, up 30.5% against the prior year's 420. The sustained-shift signal reinforces that this is not noise: the volume has been running above the prior period for long enough to constitute a trend, not a single spike. Aggravated assault and motor vehicle theft both moved in the other direction over the same window, down 66.7% and 48.6% respectively, which makes the other-larceny climb the defining story of this neighborhood's August briefing.

1 spike1 sustained shift1 zero-event
02 · Notable signals

Notable signals 1

SPIKE · OTHER LARCENY

Other Larceny

The past 12 months saw 548 incidents — about 211% above the 176 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
2024-092026-08
Robberybelow threshold
2024-092026-08
Aggravated Assaultbelow threshold
2024-092026-08
Burglary+11%
2024-092026-08
Theft from Vehicle+6%
2024-092026-08
Other Larceny+31%
2024-092026-08
Motor Vehicle Theft-49%
2024-092026-08
Vandalism-3%
2024-092026-08
Arsonbelow threshold
2024-092026-08
05 · Forecast

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

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

SEPTEMBER 2026
Most likely 5 next month — likely between 1 and 8.
+45% vs 12-month average (≈3.3)

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

SEPTEMBER 2026
Most likely 1 next month — likely between 0 and 5.

Other Larceny

SEPTEMBER 2026
Most likely 45 next month — likely between 26 and 62.
1% vs 12-month average (≈45.7)

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

SEPTEMBER 2026
Most likely 5 next month — likely between 0 and 11.
19% vs 12-month average (≈6.1)

Vandalism

SEPTEMBER 2026
Most likely 4 next month — likely between 0 and 10.
16% vs 12-month average (≈5.1)
06 · Context & comps

How University Hills 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.”

SPATIAL SPILLOVER · NEW

Do crime spikes here spill over to adjacent neighborhoods?

University Hillsdoesn't have enough spike history in any single category for a stable spillover rate yet (we want at least 5 events). The table below lists what we have.

University Hills historical spike-event spillover by crime category (3-month lookahead, adjacent neighborhoods via shared boundary).
CategorySpike eventsSame-category spillover
Other larceny1— too few

Each row shows University Hills's historical spike events for that category, and how often any of its 8 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 University Hills, excluding generic crime taxonomy. Useful as texture — what kinds of specifics show up here that don't show up elsewhere.

shopliftitemsdrugbusinessforcebldgsimplepartsfraudtrespassingbicycleinjurethreatsdisturbingorderpeaceselltelephonecomputerderivgraffitiopiumaggravatedcourtharassment
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
013827512am6am12pm6pm11pm

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

DAY OF WEEK · ALL CATEGORIES
0267534MonTueWedThuFriSatSun
MONTH OF YEAR · ALL CATEGORIES
0184369JanFebMarAprMayJunJulAugSepOctNovDec
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.