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Urban Public Space Safety Perception and the Influence of the Built Environment from a Female Perspective: Combining Street View Data and Deep Learning

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Listed:
  • Shudi Chen

    (School of Urban Design, Wuhan University, Hubei Habitat Environment Research Centre of Engineering and Technology, Wuhan 430072, China)

  • Sainan Lin

    (School of Urban Design, Wuhan University, Hubei Habitat Environment Research Centre of Engineering and Technology, Wuhan 430072, China
    Key Laboratory of Ecology and Energy Saving Study of Dense Habitat (Ministry of Education), Tongji University, Shanghai 200092, China)

  • Yao Yao

    (School of Geography and Information Engineering, China University of Geosciences, Wuhan 430079, China)

  • Xingang Zhou

    (Key Laboratory of Ecology and Energy Saving Study of Dense Habitat (Ministry of Education), Tongji University, Shanghai 200092, China
    College of Architecture and Urban Planning, Tongji University, Shanghai 200092, China)

Abstract

Women face disadvantages in urban public spaces due to their physiological characteristics. However, limited attention has been given to assessing safety perceptions from a female perspective and identifying the factors that influence these perceptions. Despite advancements in machine learning (ML) techniques, efficiently and accurately quantifying safety perceptions remains a challenge. This study, using Wuhan as a case study, proposes a method for ranking street safety perceptions for women by combining RankNet with Gist features. Fully Convolutional Network-8s (FCN-8s) was employed to extract built environment features, while Ordinary Least Squares (OLS) regression and Geographically Weighted Regression (GWR) were used to explore the relationship between these features and women’s safety perceptions. The results reveal the following key findings: (1) The safety perception rankings in Wuhan align with its multi-center urban pattern, with significant differences observed in the central area. (2) Built environment features significantly influence women’s safety perceptions, with the Sky View Factor, Green View Index, and Roadway Visibility identified as the most impactful factors. The Sky View Factor has a positive effect on safety perceptions, whereas the other factors exhibit negative effects. (3) The influence of built environment features on safety perceptions varies spatially, allowing the study area to be classified into three types: sky- and road-dominant, building-dominant, and greenery-dominant regions. Finally, this study proposes targeted strategies for creating safer and more female-friendly urban public spaces.

Suggested Citation

  • Shudi Chen & Sainan Lin & Yao Yao & Xingang Zhou, 2024. "Urban Public Space Safety Perception and the Influence of the Built Environment from a Female Perspective: Combining Street View Data and Deep Learning," Land, MDPI, vol. 13(12), pages 1-22, December.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:12:p:2108-:d:1537607
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    References listed on IDEAS

    as
    1. Kristen Day, 2006. "Being Feared: Masculinity and Race in Public Space," Environment and Planning A, , vol. 38(3), pages 569-586, March.
    2. Kerun Li, 2024. "Research on the Factors Influencing the Spatial Quality of High-Density Urban Streets: A Framework Using Deep Learning, Street Scene Images, and Principal Component Analysis," Land, MDPI, vol. 13(8), pages 1-26, July.
    3. Bivand, Roger & Müller, Werner G. & Reder, Markus, 2009. "Power calculations for global and local Moran's," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 2859-2872, June.
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