Incorporating Land Cover within Bayesian Journey-to-crime Estimation Models

Abstract

Crime occurs within asymmetrical landscapes that are occupied by physical and cultural structures that influence a criminal's behavior in space. These structures manipulate the distribution of available targets and bias the offender's perceptions of opportunity and target attractiveness. A recent study demonstrated that criminal geographic profiles can be enhanced to accommodate such ecological characteristics by using land cover classifications as a proxy for these structures. This study expands on these earlier findings by incorporating land cover classes within a Bayesian probability framework. Seven traditional and land cover enhanced geographic profile models for fifty-two burglary, robbery, and larceny serial offenses were compared. Overall, land cover enhanced models performed significantly better than non-enhanced techniques for measures of search costs andprobability estimation. Tests measuring a profile's error distance were mixed and failed to confirm significance between paired comparisons.