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Dynamic road crime risk prediction with urban open data

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Abstract

Crime risk prediction is helpful for urban safety and citizens’ life quality. However, existing crime studies focused on coarse-grained prediction, and usually failed to capture the dynamics of urban crimes. The key challenge is data sparsity, since that 1) not all crimes have been recorded, and 2) crimes usually occur with low frequency. In this paper, we propose an effective framework to predict fine-grained and dynamic crime risks in each road using heterogeneous urban data. First, to address the issue of unreported crimes, we propose a cross-aggregation soft-impute (CASI) method to deal with possible unreported crimes. Then, we use a novel crime risk measurement to capture the crime dynamics from the perspective of influence propagation, taking into consideration of both time-varying and location-varying risk propagation. Based on the dynamically calculated crime risks, we design contextual features (i.e., POI distributions, taxi mobility, demographic features) from various urban data sources, and propose a zero-inflated negative binomial regression (ZINBR) model to predict future crime risks in roads. The experiments using the real-world data from New York City show that our framework can accurately predict road crime risks, and outperform other baseline methods.

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Acknowledgements

This work was partly supported by the National Natural Science Foundation of China (Grant No. 61772460) and Ten Thousand Talent Program of Zhejiang Province (2018R52039).

Author information

Authors and Affiliations

  1. College of Computer Science, Zhejiang University, Hangzhou, 310027, China

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

  2. Fujian Key Laboratory of Sensing and Computing for Smart Cities, School of Information Science and Engineering, Xiamen University, Xiamen, 361005, China

    Longbiao Chen

Authors
  1. Binbin Zhou
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  2. Longbiao Chen
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  3. Fangxun Zhou
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  4. Shijian Li
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  5. Sha Zhao
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  6. Gang Pan
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Corresponding author

Correspondence to Gang Pan.

Additional information

Binbin Zhou received her MPhil degree in computer science from Hongkong Polytechnic University, China in 2011. She is currently pursuing the PhD degree in the Department of Computer Science, Zhejiang University, China. Her research interests include urban computing, spatio-temporal data analysis and intelligent transportation.

Longbiao Chen is an assistant professor with Fujian Key Laboratory of Sensing and Computing for Smart City, Xiamen University, China. He obtained his PhD degree in computer science from Sorbonne University, France in 2018 and Zhejiang University, China in 2016. His research interests include ubiquitous computing, urban computing, and big data analytics.

Fangxun Zhou received the BSc Degree in digital media technology from Zhejiang University, China in 2018. He is currently pursuing the Master degree in computer science and technology at Zhejiang University, China.

Shijian Li received the PhD degree from Zhejiang University, China in 2006. He is currently a professor with the College of Computer Science and Technology, Zhejiang University, China. His research interests include sensor networks, ubiquitous computing, and social computing. Professor Li serves as an editor of the International Journal of Distributed Sensor Networks and as Reviewer or PC Member of more than ten conferences.

Sha Zhao received her BSc degree in computer science from Jinan University, China in 2011, and PhD degree in computer science from Zhejiang University, China in 2016. She is currently a post-doc with college of computer science and technology, Zhejiang University, China. Her research interests include pervasive computing, mobile sensing, and machine learning. Dr. Zhao has served as a reviewer or member of several conferences.

Gang Pan received BSc and PhD degrees in computer science from Zhejiang University, China in 1998 and 2004, respectively. He is currently a professor with the College of Computer Science and Technology, Zhejiang University, China. His research interests include pervasive computing, computer vision, and pattern recognition. Prof. Pan has served as a Program Committee Member for more than ten prestigious international conferences, such as ICCV and CVPR.

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Zhou, B., Chen, L., Zhou, F. et al. Dynamic road crime risk prediction with urban open data. Front. Comput. Sci. 16, 161609 (2022). https://doi.org/10.1007/s11704-021-0136-z

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