SPIKE · OTHER LARCENYSEPTEMBER 2026 BRIEFINGDENVER · 13.1K residents

Mar Lee Crime Rate Trends — Denver

Mar Lee is a small southwest neighborhood between Federal Boulevard and Sheridan, just south of Mississippi Avenue. Predominantly mid-century single-family residential with Mar Lee Park as its namesake green space.

OTHER LARCENY · 24-MO COUNT09 2026 · 12
0132612-mo avg: 16.2
MAR LEECITYWIDE TREND (RESCALED)+5% 12MO YOY
+50%MoM
+100%12mo YoY
194last 12mo
12this month
01 · TL;DR

Three categories moved in Mar Lee this September: one spike, one single-month drop, and one sustained structural shift. The shape of the month is dominated by other larceny, which registers both as a one-month spike and as a multi-year structural increase, two separate signals pointing in the same direction. Motor vehicle theft ran below trend, the lone category moving the other way.

Other larceny stands out most sharply in the 12-month totals: 194 incidents in the current period against 97 in the year before, a 100.0% increase, with the sustained-shift signal confirming this is not a one-month anomaly but a structural change that has been building across the trailing window. Motor vehicle theft moved the opposite direction, 63 incidents this year against 83 prior, down 24.1%. Theft from vehicle also posted a large 12-month decline, down 33.7% to 55 from 83, though it did not cross the signal threshold this month. Everything else, aggravated assault, burglary, robbery, and vandalism, remained within range or showed more modest 12-month movement.

1 spike1 drop1 sustained shift
02 · Notable signals

Notable signals 2

SPIKE · OTHER LARCENY

Other Larceny

The past 12 months saw 194 incidents — about 200% above the 65 average from prior years.

DROP · MOTOR VEHICLE THEFT

Motor Vehicle Theft

The past 12 months saw 63 incidents — about 48% 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
2024-102026-09
Robbery+100%
2024-102026-09
Aggravated Assault-8%
2024-102026-09
Burglary-14%
2024-102026-09
Theft from Vehicle-34%
2024-102026-09
Other Larceny+100%
2024-102026-09
Motor Vehicle Theft-24%
2024-102026-09
Vandalism0%
2024-102026-09
Arsonbelow threshold
2024-102026-09
05 · Forecast

What next month likely looks like

Forecasts trained through September 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

OCTOBER 2026
Most likely 2 next month — likely between 0 and 5.
−26% vs 12-month average (≈2.7)

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

OCTOBER 2026
Most likely 6 next month — likely between 0 and 12.
+16% vs 12-month average (≈5.3)

Other Larceny

OCTOBER 2026
Most likely 12 next month — likely between 4 and 21.
−24% vs 12-month average (≈16.2)

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

OCTOBER 2026
Most likely 5 next month — likely between 0 and 10.
+9% vs 12-month average (≈4.6)

Vandalism

OCTOBER 2026
Most likely 8 next month — likely between 3 and 13.
+30% vs 12-month average (≈6.1)
06 · Context & comps

How Mar Lee 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?

Mar Leedoesn'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.

Mar Lee historical spike-event spillover by crime category (3-month lookahead, adjacent neighborhoods via shared boundary).
CategorySpike eventsSame-category spillover
Other larceny2— too few

Each row shows Mar Lee'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 Mar Lee, excluding generic crime taxonomy. Useful as texture — what kinds of specifics show up here that don't show up elsewhere.

shopliftweaponorderdischargeunlawfuldrugsimplecourtforcepartsitemsinjurethreatstrespassingresidencepossfraudpeaceaggravatedmenacingpoliceweapdisturbingrestrainingparaphernalia
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
09017912am6am12pm6pm11pm

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

DAY OF WEEK · ALL CATEGORIES
0223446MonTueWedThuFriSatSun
MONTH OF YEAR · ALL CATEGORIES
0139277JanFebMarAprMayJunJulAugSepOctNovDec
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.