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Crime prediction plays an impactful role in enhancing public security andsustainable development of urban. With recent advances in data collection andintegration technologies, a large amount of urban data with rich crime-relatedinformation and fine-grained spatio-temporal logs has been recorded. Suchhelpful information can boost our understandings about the temporal evolutionand spatial factors of urban crimes and can enhance accurate crime prediction.In this paper, we perform crime prediction exploiting the cross-type andspatio-temporal correlations of urban crimes. In particular, we verify theexistence of correlations among different types of crime from temporal andspatial perspectives, and propose a coherent framework to mathematically modelthese correlations for crime prediction. The extensive experimental results onreal-world data validate the effectiveness of the proposed framework. Furtherexperiments have been conducted to understand the importance of differentcorrelations in crime prediction.

Exploring Spatio-Temporal and Cross-Type Correlations for Crime Prediction