Howard University Crime Rate Trends — Washington DC
The Howard University cluster covers the historically Black university campus and the Le Droit Park rowhouse district to its south, plus the Cardozo and Shaw fringes that connect it to U Street. Le Droit Park's late-19th-century blocks form one of the city's earliest planned suburbs, now folded into the urban grid.
Three categories moved in Howard University this month — all three are sustained downward shifts, not single-month anomalies. The structural direction across property and violent crime is broadly lower, with no spikes or rare events in the mix.
Motor vehicle theft leads the sustained moves, down 60.1% against the prior 12 months (61 incidents vs. 153). Theft from vehicle follows at -44.8% (214 vs. 388), and robbery has fallen 46.3% over the same window (73 vs. 136). Every other tracked category either ran within normal range or posted smaller moves — the breadth here is property-focused, and the declines are multi-year in character, not one quiet 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.
- Theft from Vehicle has reset to a lower baseline.
The trailing 12-month count is 214, down 45% from 388 the year before. If the trend holds another quarter, it will pull the multi-year baseline down.
- Motor Vehicle Theft has reset to a lower baseline.
The trailing 12-month count is 61, down 60% from 153 the year before. If the trend holds another quarter, it will pull the multi-year baseline down.
- Robbery has reset to a lower baseline.
The trailing 12-month count is 73, down 46% from 136 the year before. If the trend holds another quarter, it will pull the multi-year baseline down.
What next month likely looks like
Forecasts trained through June 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.
Burglary
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.
Theft from Vehicle
How Howard University 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.”
Shaw
58 incidents over the past 12 months — 3 below Howard University's 61.
Open page →North Michigan Park
55 incidents over the past 12 months — 6 below Howard University's 61.
Open page →Douglas
54 incidents over the past 12 months — 7 below Howard University's 61.
Open page →Recurring local terms (last 12 months)
Top terms in incident descriptions for Howard University, 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 DC Open Data — MPD's per-year Crime Incidents layers on the DCGIS ArcGIS Hub — mapped to 8 UCR Part 1 categories (vandalism and arson are not exposed in MPD's public feed and are excluded). The feed covers 2018-current and updates daily. Aggregated to neighborhood cluster × category × month, with each cluster page identified by its colloquial lead constituent (Adams Morgan, Petworth, Capitol Hill, etc.) rather than the numbered 'Cluster N' identifier.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.