A Data-Driven Problem: Exploring Predictive Policing with Random Forest Crime Mapping in Oslo

• In Event: WG-PLACE 1: Research on crime, place and time

Abstract

Technology is advancing rapidly and law enforcement must keep up-to-date with new methods to develop effective crime prevention strategies. Exploring predictive policing with random forest algorithms and crime mapping can inform the development of crime prevention strategies and policies with the aim of reducing violent crime events in Oslo. Furthermore, supervised machine learning models are, to a small degree, empirically tested in Norway, and the interest in their use for predictive policing is increasing. Since the risk of biased assessments based on crime predictions can increase when technologies are not adequately understood or the applied input data quality could be better, expanding our knowledge in this field is crucial. This study tests the prediction performance of random forest models predicting violent crime at three different micro-geographical units of analyses in Oslo. Data from the Norwegian police crime registry and environmental features, including urban and weather data, were used to enhance this analysis. Findings showed that random forests could predict violent crimes with up to 80 per cent accuracy. Here, the location and spatial time lags of violent crimes in Oslo were significant predictors of future crimes, as were environmental features such as minor roads, residential areas, and forests. The study concludes that using random forest algorithms in crime mapping is a highly accurate predictive model for law enforcement. Still, there are some critical challenges that technological advancements may present for the implementation of new policies. Based on the empirical findings and a more comprehensive discussion of the effectiveness, limitations, and ethical implications of the approach, this study hopes to contribute to the current discourse on the responsible and effective adoption of data-driven strategies for crime prevention, acknowledging the need for adaptability and continuous learning in the face of ever-evolving technology.

Author

• Victoria Jerstad Stensland, University of Oslo