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Spatio-temporal analysis of urban crime leveraging multisource crowdsensed data

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Abstract

Crime analysis is important for social security management. With the advance of crowd sensing techniques, abundant multisource crowd sensed data could be used for crime analysis. The occurrence of crimes usually has some patterns in terms of temporal and spatial aspects. Investigating the spatio-temporal correlation of crimes could provide more useful cues for crime analysis and help discover underlying crime patterns. In this paper, we conduct a spatio-temporal study to understand urban crimes leveraging multisource crowd sensed data, including crime data, meteorological data, POI distribution, and taxi trips. Specifically, we first present monthly temporal trends and spatial distribution of crimes. We then investigate the spatio-temporal correlation using meteorological data (e.g., weather conditions and air temperature) and POI distribution and taxi trips. It is found that taxi trips and air temperature have a strong correlation with the crime, and some POI categories have a valuable correlation with the crime, e.g., College & University. We also find that Overcast days would witness more crime than other weather conditions.

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Funding

This work is supported by Ten Thousand Talent Program of Zhejiang Province (Grant No. 2018R52039).

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Authors and Affiliations

  1. Zhejiang University, Hangzhou, China

    Binbin Zhou, Sha Zhao, Fangxun Zhou, Shijian Li & Gang Pan

  2. Xiamen University, Xiamen, China

    Longbiao Chen

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  1. Binbin Zhou
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  2. Longbiao Chen
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  3. Sha Zhao
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  4. Fangxun Zhou
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  5. Shijian Li
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  6. Gang Pan
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Correspondence to Gang Pan.

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Zhou, B., Chen, L., Zhao, S. et al. Spatio-temporal analysis of urban crime leveraging multisource crowdsensed data. Pers Ubiquit Comput 27, 599–612 (2023). https://doi.org/10.1007/s00779-020-01456-6

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  • DOI: https://doi.org/10.1007/s00779-020-01456-6

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