Spatio-temporal cokriging crime predictions using social media data: a multi-type case study in San Jose, California

Abstract

Crime prevention requires accurate prediction of the spatial and temporal distribution of criminal activities to effectively allocate law enforcement resources. However, many trending crime prediction algorithms lack comprehensive spatio-temporal structures and often consider only single input variables. This study innovatively using in ST-Cokriging method integrated both historical crime records as the primary variable and crime-related geo-tagged Twitter data as the co-variable for crime prediction. The predictive method has been specifically developed to assess crime risk across three major crime types—street crime, property crime, and vehicle crime—and applied in the San Francisco Bay Area (SFBA), California, a region characterized by high development and heightened crime sensitivity, for both prediction and validation. The results indicate that incorporating social media data into a spatio-temporal statistical method improves the associations between predicted and actual crime risk, reduced the Root Mean Squared Error (RMSE), and enhanced the identification of crime risk areas for both weekdays and weekends across three crime types compared to the method without the co-variable. This study presents a new multi-variable approach to more accurately predict crime, enabling law enforcement proactively address crime of varying nature in urban areas.