Census-tract-level observations of CCTV / surveillance-camera counts paired
with sociodemographic and crime indicators from ten U.S. cities. Continuous
covariates are city-centred and scaled. Used in glmOJ's vignette to
demonstrate count regression with an exposure offset
(offset(log_road_length)) and high-cardinality factor predictors.
Format
A data frame with 11,620 rows and 19 columns:
- city
Factor with 10 U.S. city levels.
- GEOID
Census tract GEOID.
- cam_count
Integer count of street cameras in the tract (the response used in the vignette).
- image_count
Integer count of street-view images sampled.
- pnhwht
Percent non-Hispanic white population (city-scaled).
- pnhblk
Percent non-Hispanic Black population (city-scaled).
- pasian
Percent Asian population.
- phisp
Percent Hispanic population.
- entropy
Race-ethnicity diversity entropy (city-scaled).
- entropy_rank
Within-city percentile rank of entropy.
- total_crime_rate
Total crime rate (city-scaled).
- viol_veh_crime_rate
Violent and vehicle crime rate.
- pop
Tract population (city-scaled).
- hinc
Median household income (city-scaled).
- pvac
Vacancy rate (city-scaled).
- mhmval
Median home value (city-scaled).
- modal_zone
Factor: dominant land-use zoning (
commercial,industrial,mixed,public,residential,roads).- road_length_km
Tract road length in kilometres.
- log_road_length
Natural log of
road_length_km, used as an exposure offset.