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Spatial-temporal meta-path guided explainable crime prediction

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Abstract

Exposure to crime and violence can harm individuals’ quality of life and the economic growth of communities. In light of the rapid development in machine learning, there is a rise in the need to explore automated solutions to crime prevention. The increasing availability of both fine-grained urban and public service data has driven a recent surge in fusing such cross-domain information to facilitate crime prediction. By capturing the information about social structure, environment, and crime trends, existing machine learning predictive models have explored the dynamic crime patterns from different views. However, these approaches mostly convert such multi-source knowledge into implicit and latent representations (e.g., learned embeddings of districts), making it still a challenge to investigate the impacts of explicit factors for the occurrences of crimes behind the scenes. In this paper, we present a Spatial-Temporal Meta-path guided Explainable Crime prediction (STMEC) framework to capture dynamic patterns of crime behaviours and explicitly characterize how the environmental and social factors mutually interact to produce the forecasts. Extensive experiments show the superiority of STMEC compared with other advanced spatial-temporal models, especially in predicting felonies (e.g., robberies and assaults with dangerous weapons).

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Data Availability

All datasets used for supporting the conclusions of this article are available from the public data repository at the website of data.cityofnewyork.us and www.kaggle.com.

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Acknowledgements

We gratefully thank Dr. Thomas Taimre (the University of Queensland) and Dr. Radislav Vaisman (the University of Queensland) for valuable discussions on this research and helpful comments on the manuscript. This work is supported by Australian Research Council Future Fellowship (Grant No. FT210100624) and Discovery Project (Grant No.DP190101985).

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  1. School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia

    Yuting Sun, Tong Chen & Hongzhi Yin

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Contributions

Yuting Sun implemented the methodology, performed data curation, wrote the original draft, and conducted the experiment. Tong Chen proposed the conceptualization and methodology, prepared the figures, and performed review and editing. Hongzhi Yin proposed the research problem and methodology, supervised the whole research work, wrote the introduction and abstract parts, and performed review and editing.

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Correspondence to Hongzhi Yin.

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Sun, Y., Chen, T. & Yin, H. Spatial-temporal meta-path guided explainable crime prediction. World Wide Web 26, 2237–2263 (2023). https://doi.org/10.1007/s11280-023-01137-3

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