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Exploring Spatio-Temporal and Cross-Type Correlations for Crime Prediction
Crime prediction plays an impactful role in enhancing public security and
sustainable development of urban. With recent advances in data collection and
integration technologies, a large amount of urban data with rich crime-related
information and fine-grained spatio-temporal logs has been recorded. Such
helpful information can boost our understandings about the temporal evolution
and spatial factors of urban crimes and can enhance accurate crime prediction.
In this paper, we perform crime prediction exploiting the cross-type and
spatio-temporal correlations of urban crimes. In particular, we verify the
existence of correlations among different types of crime from temporal and
spatial perspectives, and propose a coherent framework to mathematically model
these correlations for crime prediction. The extensive experimental results on
real-world data validate the effectiveness of the proposed framework. Further
experiments have been conducted to understand the importance of different
correlations in crime prediction