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Assessing the Impact of Street-View Greenery on Fear of Neighborhood Crime in Guangzhou, China

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  • Fengrui Jing

    (School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
    Guangdong Provincial Engineering Research Center for Public Security and Disaster, Guangzhou 510275, China)

  • Lin Liu

    (Department of Geography, University of Cincinnati, Cincinnati, OH 45221-0091, USA
    Center of GeoInformatics for Public Security, School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China)

  • Suhong Zhou

    (School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
    Guangdong Provincial Engineering Research Center for Public Security and Disaster, Guangzhou 510275, China)

  • Jiangyu Song

    (School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China)

  • Linsen Wang

    (School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China)

  • Hanlin Zhou

    (Department of Geography, University of Cincinnati, Cincinnati, OH 45221-0091, USA)

  • Yiwen Wang

    (School of Architecture and Urban Planning, Nanjing University, Nanjing 210093, China)

  • Ruofei Ma

    (School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China)

Abstract

Previous literature has examined the relationship between the amount of green space and perceived safety in urban areas, but little is known about the effect of street-view neighborhood greenery on perceived neighborhood safety. Using a deep learning approach, we derived greenery from a massive set of street view images in central Guangzhou. We further tested the relationships and mechanisms between street-view greenery and fear of crime in the neighborhood. Results demonstrated that a higher level of neighborhood street-view greenery was associated with a lower fear of crime, and its relationship was mediated by perceived physical incivilities. While increasing street greenery of the micro-environment may reduce fear of crime, this paper also suggests that social factors should be considered when designing ameliorative programs.

Suggested Citation

  • Fengrui Jing & Lin Liu & Suhong Zhou & Jiangyu Song & Linsen Wang & Hanlin Zhou & Yiwen Wang & Ruofei Ma, 2021. "Assessing the Impact of Street-View Greenery on Fear of Neighborhood Crime in Guangzhou, China," IJERPH, MDPI, vol. 18(1), pages 1-17, January.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:1:p:311-:d:474291
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    References listed on IDEAS

    as
    1. Richardson, Elizabeth A. & Mitchell, Richard, 2010. "Gender differences in relationships between urban green space and health in the United Kingdom," Social Science & Medicine, Elsevier, vol. 71(3), pages 568-575, August.
    2. Yu Ye & Wei Zeng & Qiaomu Shen & Xiaohu Zhang & Yi Lu, 2019. "The visual quality of streets: A human-centred continuous measurement based on machine learning algorithms and street view images," Environment and Planning B, , vol. 46(8), pages 1439-1457, October.
    3. Scarborough, Brittney K. & Like-Haislip, Toya Z. & Novak, Kenneth J. & Lucas, Wayne L. & Alarid, Leanne F., 2010. "Assessing the relationship between individual characteristics, neighborhood context, and fear of crime," Journal of Criminal Justice, Elsevier, vol. 38(4), pages 819-826, July.
    4. Fengrui Jing & Lin Liu & Suhong Zhou & Guangwen Song, 2020. "Examining the Relationship between Hukou Status, Perceived Neighborhood Conditions, and Fear of Crime in Guangzhou, China," Sustainability, MDPI, vol. 12(22), pages 1-19, November.
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    Cited by:

    1. Liu, Yang & Kwan, Mei-Po & Wong, Man Sing & Yu, Changda, 2023. "Current methods for evaluating people's exposure to green space: A scoping review," Social Science & Medicine, Elsevier, vol. 338(C).
    2. Juntti, Meri & Ozsezer-Kurnuc, Sevda, 2023. "Factors influencing the realisation of the social impact of urban nature in inner-city environments: A systematic review of complex evidence," Ecological Economics, Elsevier, vol. 211(C).
    3. Yiwen Wang & Di Liu & Jiameng Luo, 2022. "Identification and Improvement of Hazard Scenarios in Non-Motorized Transportation Using Multiple Deep Learning and Street View Images," IJERPH, MDPI, vol. 19(21), pages 1-25, October.

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