Spatio-temporal modeling is widely recognized as a promising means for predicting crime patterns. Despite their enormous potential, the available methods are still in their infancy. A lot of research focuses on crime hotspot detection and geographic crime clusters, while a systematic approach to include the temporal component of the underlying crime distributions is still under-researched. In this paper, we gain further insight in predictive crime modeling by including a spatio-temporal interaction component in the prediction of residential burglaries. Based on an extensive dataset, we show that including additive space-time interactions leads to significantly better predictions.

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The 6th International Conference on Data Analytics, Data Analytics 2017

Mahfoud, M., Bhulai, S., & van der Mei, R. (2017). Spatio-temporal modeling for residential burglary. In Proceedings of the 6th international conference on Data Analytics (Barcelona, January 2018) (pp. 59–64).