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

TL;DR

This paper investigates how spatio-temporal and cross-type correlations among urban crimes can be modeled to improve prediction accuracy, utilizing real-world data and a novel mathematical framework.

Contribution

It introduces a new framework that models cross-type and spatio-temporal correlations for more accurate urban crime prediction.

Findings

Correlations exist among different crime types in space and time.

The proposed framework outperforms existing methods in prediction accuracy.

Different correlations have varying importance in crime prediction.

Abstract

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.