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

Shudi Chen, Sainan Lin (), Yao Yao and Xingang Zhou ()
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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
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

Land, 2024, vol. 13, issue 12, 1-22

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.

Keywords: female perspective; safety perception; street view image; machine learning; FCN; RankNet; public space; Wuhan (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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