Multi-Step Occurrence Prediction of Urban Crimes with Enhanced GRU-ODE-Bayes Model

Abstract

Crime occurrence prediction is an important task for public security and urban governance. In real world, some crimes are quite harmful, thus it’s necessary to predict these crimes in the next few days (weeks). However, these harmful crime events may have relatively lower frequency, and their records are discontinuous and sporadic, making the multistep occurrence prediction difficult. Due to the sparsity and irregularity of the crime sequences, most of the existing crime prediction techniques do not distinguish crime types and have difficulties to predict low-frequency crimes’ occurrences. In addition, they cannot accurately predict the occurrence of crimes over multiple recent time slots due to sporadic crime records. In this paper, we propose Multi-Time-Slot Crime Occurrence Prediction (MCOP) model, which aims to (1) predict crime types separately and (2) predict crime occurrences in the next multiple time slots. Specifically, MCOP uses a GRU-ODE-Bayes model to handle the discontinuous and sporadic crime events sequences. MCOP is enhanced by a data augmentation to address the data sparsity problem, and exploits the near repeat phenomenon to enable continuous crime predictions. We evaluated our model using real-world urban data, and the results showed our model outperforms the baseline techniques.