Location
University of Nevada Las Vegas, Greenspun Hall
Description
Police typically rely on retrospective hotspot maps to informe prevention strategies aimed at reducing future crime. The current study reviews environmental crime theories that help to identify casual factors associated with rish of auto theft. Map layers are created from data that operationalize these risk factors. These layers are combined using spatial analysis techniques to produce a "risk density" map. Analysis of crime data are used to determing wheter our "risk density" map better predicts subsequetnt theft events than a traditional retrospective hotspot map.
Keywords
Automobile theft – Forecasting; Automobile theft—Prevention; Crime prevention
Disciplines
Criminology and Criminal Justice | Quantitative, Qualitative, Comparative, and Historical Methodologies | Social Control, Law, Crime, and Deviance | Statistics and Probability
Language
English
Included in
Criminology and Criminal Justice Commons, Quantitative, Qualitative, Comparative, and Historical Methodologies Commons, Social Control, Law, Crime, and Deviance Commons, Statistics and Probability Commons
Risk auto theft: Predicting spatial distributions of crime events
University of Nevada Las Vegas, Greenspun Hall
Police typically rely on retrospective hotspot maps to informe prevention strategies aimed at reducing future crime. The current study reviews environmental crime theories that help to identify casual factors associated with rish of auto theft. Map layers are created from data that operationalize these risk factors. These layers are combined using spatial analysis techniques to produce a "risk density" map. Analysis of crime data are used to determing wheter our "risk density" map better predicts subsequetnt theft events than a traditional retrospective hotspot map.