US Crime Offenses
Every U.S. state (plus DC and a national 'US' series) across the 10 UCR offense classes - violent crime, homicide, rape, robbery, aggravated assault, property crime, burglary, larceny, motor-vehicle theft and arson - as one tidy table: monthly offenses known to law enforcement, offenses cleared, the crime and clearance rate per 100,000, the covered population and reporting-coverage percentage, straight from the FBI's Crime Data Explorer (UCR/SRS). Decades-deep, nationally comparable, keyed on the 2-letter state code. Aggregate counts only, no victim/offender/arrestee records.
- Source
- FBICDE
- Licence
- Redistributable open data
- Quality
- QC clean · 0 failures
- Format
- Apache Parquet + dictionary
Safe to train on
Built only from a named, redistributable official source under a documented open licence - not scraped web data. No copyright grey zone, no personal data. Ships machine-readable Croissant metadata (ML Commons - loads in Hugging Face / Kaggle / Google), an AI training-licence manifest documenting source, licence and provenance for your model's data governance, and a machine-readable data dictionary (drop it into an agent / RAG prompt so the model knows every column) - all inspectable before you buy.
What's included
- Every state + DC + a national series x 10 UCR offense classes, one clean table
- Monthly offenses known to police AND offenses cleared - counts, not just rates
- Crime & clearance rate per 100k, covered population and reporting-coverage %
- Decades of monthly history; keyed on the 2-letter state code for easy joins
- Aggregate counts only - no victim/offender/arrestee records, no PII
- Source: FBI Uniform Crime Reporting (UCR) Program - Crime Data Explorer (public domain / CC0)
Schema
| Column | Type | Description | Filled | Distinct |
|---|---|---|---|---|
| state | str | USPS 2-letter state code ('US' = national total) | 100.0% | 51 |
| state_name | str | State / national name | 100.0% | 51 |
| offense | str | UCR offense class slug (e.g. violent-crime, homicide, burglary) | 100.0% | 10 |
| year | int | Calendar year | 100.0% | 42 |
| month | int | Calendar month (1-12) | 100.0% | 12 |
| period | str | YYYY-MM period | 100.0% | 499 |
| offense_count | int | Offenses known to law enforcement (SRS estimate) | 100.0% | 24,597 |
| clearance_count | int | Offenses cleared by arrest or exceptional means | 100.0% | 8,847 |
| offense_rate_per_100k | float | Offense rate per 100,000 population | 100.0% | 36,261 |
| clearance_rate_per_100k | float | Clearance rate per 100,000 population | 100.0% | 10,291 |
| population | int | Covered population | 100.0% | 2,039 |
| participated_population | int | Population of reporting agencies | 100.0% | 15,467 |
| coverage_pct | float | Percent of population covered by reporting agencies | 100.0% | 3,339 |
Sample & preview
Every purchase ships as Apache Parquet with a data dictionary and the full QC report. A free sample (first rows + schema) is downloadable here - confirm fit before you buy. Source: FBICDE (redistributable open data; attribution passes through - see our Licence).
Don't trust screenshots - drop the sample into your notebook right now.
import pandas as pd df = pd.read_parquet("us-crime-offenses_sample.parquet") df.info() # typed columns, gap-aware, ready to join⬇ Download free Parquet sample or CSV for Excel
Related datasets
EU Business Formation Dynamics
Net business creation across Europe: enterprise births, deaths, active-population, birth/death/churn rates, net population growth, jobs created & lost, and 1/3/5-year survival rates - by country x NACE sector x year, 2009-2023. Sourced from Eurostat Business Demography (bd_size), cleaned to one tidy Parquet panel. Aggregate flows only, no company-level data.
European Power Plant Registry
Every conventional power plant in Europe in one clean table: name, operator, country, net capacity, fuel/technology, commissioning year and location - assembled and de-duplicated from Open Power System Data. The asset reference table that joins to generation, capacity and carbon analytics by country and fuel.
Global Cities Gazetteer (GeoNames, pop ≥ 1,000)
Every populated place on Earth with population ≥ 1,000 - ~150k cities and towns - in one clean, geocoded table. GeoNames ships it as a header-less tab-delimited dump; we parse it to a tidy row per place: name (+ ASCII), country, admin-1/2 codes, population, elevation, timezone and lat/lon. The universal populated-place join key (name ⋈ country ⋈ coordinates) that geocodes or enriches any geo dataset. CC-BY 4.0.