Related papers: Exploring Spatio-Temporal and Cross-Type Correlations for Crime Prediction

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

URL: http://arxiv.org/abs/2001.06923v2
Date: Wed, 22 Jan 2020 02:20:36 GMT
Title: Exploring Spatio-Temporal and Cross-Type Correlations for Crime Prediction
Authors: Xiangyu Zhao and Jiliang Tang
Abstract summary: We perform crime prediction exploiting the cross-type and-temporal correlations of urban crimes. We propose a coherent framework to mathematically model these correlations for crime prediction. Further experiments have been conducted to understand the importance of different correlations in crime prediction.
Score: 48.1813701535167
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.

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