Abstract:
The retrospective predictive policing techniques are atheoretical and therefore remain incapable of sensing the changing crime risk across the streets. In this study, we aim to develop a dynamic predictive policing system that capitalizes on theory-based risk indicators. The sample includes all the theft and robbery incidents in Chicago between 2014-2019. In the first step, pipelining bivariate network K analysis and segmented regression, we introduce novel distance-aware risk functions that operationalize spatiotemporal crime risk around the selected urban features (i.e., bus stop, fast food restaurant, gas station, grocery store, pub). In the second step, we develop various network-based predictive policing methods using graph-based deep learning algorithms (i.e., GraphWavenet, Spatiotemporal Graph Convolutional Networks). These methods generate weekly and intraday hotspot predictions. We complement these methods with various theory-based risk indicators including a risk score devised from the novel risk functions, 311 calls, park events, and cooccurring crime incidents. The results showcase that crime risk around urban features varies across space, time, and crime types. Furthermore, this risk is found to be significantly correlated with the regional socioeconomic characteristics. Another important result shows that incorporating theory based indicators improved the performance of the retrospective methods up to 68%. Amongst the algorithms, GraphWavenet is found to outperform its counterparts in the majority of the prediction models with an accuracy as high as 80%. The proposed system helps law enforcement agents in planning their operations efficiently by pinpointing the micro geographical units with relatively higher risks in the next time step.
