Spatial Prediction of Annual Burglaries in Los Angeles: A Poisson Point Process and Kernel Intensity Mixed Modeling Approach

SUMMARY: Burglary location, poverty, education, income, and population density data were collected from the Los Angeles Police Department, the U.S. Census and U.S. Internal Revenue Service. Prior burglary point data and socio-demographic spatial covariates were used to construct annual kernel-intensity and Poisson point process hybrid models to predict the burglary rates of the following year. To test the utility of the spatial covariates over kernel-intensity only methods, two models were constructed: A baseline model using only kernel-intensity data, and an expanded model using kernel-intensity data and additional spatial covariates. Analysis-of deviance test revealed a significant difference of 116.64 with nine degrees-of-freedom (p-value < 0.001). Voronoi residuals used for cross-validation indicated a 2.608% improvement in the area adjusted-root-mean-square error