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Exploring associations between streetscape factors and crime behaviors using Google Street View images

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Abstract

Understanding the influencing mechanism of the urban streetscape on crime is fairly important to crime prevention and urban management. Recently, the development of deep learning technology and big data of street view images, makes it possible to quantitatively explore the relationship between streetscape and crime. This study computed eight streetscape indexes of the street built environment using Google Street View images firstly. Then, the association between the eight indexes and recorded crime events was revealed with a poisson regression model and a geographically weighted poisson regression model. An experiment was conducted in downtown and uptown Manhattan, New York. Global regression results show that the influences of Motorization Index on crimes are significant and positive, while the effects of the Light View Index and Green View Index on crimes depend heavily on the socioeconomic factors. From a local perspective, the Pedestrian Space Index, Green View Index, Light View Index and Motorization Index have a significant spatial influence on crimes, while the same visual streetscape factors have different effects on different streets due to the combination differences of socioeconomic, cultural and streetscape elements. The key streetscape elements of a given street that affect a specific criminal activity can be identified according to the strength of the association. The results provide both theoretical and practical implications for crime theories and crime prevention efforts.

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Acknowledgements

The work was supported by the National Natural Science Foundation of China (Grant No. 61872050, No. 62172066), the Chongqing Basic and Frontier Research Program (cstc2018jcyjAX0551), the Fundamental Research Funds for the Central Universities (2018CDJSK03XK01), and the Chongqing Technology Innovation and Application Development Key Project (ctsc2019jscx-gksbX0066).

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Authors and Affiliations

  1. School of Computer Science, Chongqing University, Chongqing, 400044, China

    Mingyu Deng, Chao Chen & Chenxi Liu

  2. School of Management Science and Real Estate, Chongqing University, Chongqing, 400044, China

    Wei Yang

  3. State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing, 400044, China

    Chao Chen

Authors
  1. Mingyu Deng
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  2. Wei Yang
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  3. Chao Chen
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  4. Chenxi Liu
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Corresponding authors

Correspondence to Wei Yang or Chao Chen.

Additional information

Mingyu Deng is currently a master student at College of Computer Science, Chongqing University, China. She obtained her bachelor degree of Computer Science and Technology of Chongqing University of Posts and Telecommunications, China in 2018. Her research interests include smart cities and data visualization.

Wei Yang received the PhD degree in School of Resource and Environmental Science at Wuhan University, China in 2019. He is currently a Lecturer at Chongqing University, China. His research interests include trajectory data mining, human behavior analysis, GIS and smart city.

Chao Chen is a full professor at College of Computer Science, Chongqing University, China. He obtained his PhD degree from Pierre and Marie Curie University and Institut Mines-Télécom/ Télécom SudParis, France in 2014. He received the BSc and MSc degrees in control science and control engineering from Northwestern Polytechnical University, China in 2007 and 2010, respectively. Dr. Chen got published over 80 papers including 20 ACM/IEEE Transactions. His work on taxi trajectory data mining was featured by IEEE Spectrum in 2011 and 2016, respectively. He was also the recipient of the Best Paper Runner-Up Award at MobiQuitous 2011.

In 2009, he worked as a Research Assistant with Hong Kong Polytechnic University, China. His research interests include pervasive computing, mobile computing, urban logistics, data mining from large-scale GPS trajectory data, and big data analytics for smart cities.

Chenxi Liu is currently a master degree candidate at College of Computer Science, Chongqing University, China. He received his bachelor degree from the School of Optical and Electronic Information of Huazhong University of Science and Technology, China in 2017. His research interests include reinforcement learning and ride-sharing platform optimization.

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Deng, M., Yang, W., Chen, C. et al. Exploring associations between streetscape factors and crime behaviors using Google Street View images. Front. Comput. Sci. 16, 164316 (2022). https://doi.org/10.1007/s11704-020-0007-z

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