CUF Crime Rate Trends — Cincinnati
CUF stands for Clifton Heights, University Heights, and Fairview, the dense neighborhood immediately south and west of the University of Cincinnati's main campus. Mixed student housing, hillside residential streets, and the McMillan Street and Calhoun Street commercial strips that serve the campus.
Three categories moved in CUF this April — one single-month spike and two sustained structural shifts. The mix points in opposite directions: property crime is splitting, with burglary and theft from vehicle running well above prior-year levels even as other larceny has fallen sharply over the trailing 12 months.
Aggravated assault registered a one-month spike against its multi-year baseline; the trailing 12-month total is 26 incidents, up 8.3% against the prior year. Burglary is the more structural concern: 131 incidents over the past 12 months vs. 92 in the year before, a 42.4% increase that now qualifies as a sustained shift rather than a single noisy month. Other larceny moved the other way — 224 incidents vs. 305 in the prior year, down 26.6% — the one category showing a clear multi-year decline.
Notable signals 1
Aggravated Assault
The past 12 months saw 26 incidents — about 87% above the 14 average from prior years.
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.
- Other Larceny has reset to a lower baseline.
The trailing 12-month count is 224, down 27% from 305 the year before. If the trend holds another quarter, it will pull the multi-year baseline down.
- Burglary is climbing.
The trailing 12-month count is 131, up 42% from 92 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 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
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 CUF compares
Peer neighborhoods picked by closest 12-month aggravated assault 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 aggravated assault levels.”
Mt. Airy
26 incidents over the past 12 months — 0 below CUF's 26.
Open page →Corryville
25 incidents over the past 12 months — 1 below CUF's 26.
Open page →Walnut Hills
25 incidents over the past 12 months — 1 below CUF's 26.
Open page →Do crime spikes here spill over to adjacent neighborhoods?
When CUF has spiked other larceny historically (11 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.
| Category | Spike events | Same-category spillover |
|---|---|---|
| Other larceny | 11 | 100% |
| Aggravated assault | 10 | 60% |
| Burglary | 9 | 100% |
| Robbery | 3 | — too few |
Each row shows CUF's historical spike events for that category, and how often any of its 7 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 Cincinnati); 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.
Recurring local terms (last 12 months)
Top terms in incident descriptions for CUF, 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 Cincinnati Open Data — STARS Category Offenses post-2024-06-03 plus PDI Crime Incidents back to 2020 — mapped to 8 UCR-aligned categories (vandalism and arson aren't recoverable across the STARS migration boundary). Aggregated to Statistical Neighborhood Approximation × 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.