Urban Public Space Safety Perception and the Influence of the Built Environment from a Female Perspective: Combining Street View Data and Deep Learning
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)...
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MDPI AG
2024-12-01
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| Online Access: | https://www.mdpi.com/2073-445X/13/12/2108 |
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| author | Shudi Chen Sainan Lin Yao Yao Xingang Zhou |
| author_facet | Shudi Chen Sainan Lin Yao Yao Xingang Zhou |
| author_sort | Shudi Chen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-599f8a82460048c6a00bdd9166b677bb |
| institution | DOAJ |
| issn | 2073-445X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj-art-599f8a82460048c6a00bdd9166b677bb2025-08-20T02:56:52ZengMDPI AGLand2073-445X2024-12-011312210810.3390/land13122108Urban Public Space Safety Perception and the Influence of the Built Environment from a Female Perspective: Combining Street View Data and Deep LearningShudi Chen0Sainan Lin1Yao Yao2Xingang Zhou3School of Urban Design, Wuhan University, Hubei Habitat Environment Research Centre of Engineering and Technology, Wuhan 430072, ChinaSchool of Urban Design, Wuhan University, Hubei Habitat Environment Research Centre of Engineering and Technology, Wuhan 430072, ChinaSchool of Geography and Information Engineering, China University of Geosciences, Wuhan 430079, ChinaKey Laboratory of Ecology and Energy Saving Study of Dense Habitat (Ministry of Education), Tongji University, Shanghai 200092, ChinaWomen 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.https://www.mdpi.com/2073-445X/13/12/2108female perspectivesafety perceptionstreet view imagemachine learningFCNRankNet |
| spellingShingle | Shudi Chen Sainan Lin Yao Yao Xingang Zhou Urban Public Space Safety Perception and the Influence of the Built Environment from a Female Perspective: Combining Street View Data and Deep Learning Land female perspective safety perception street view image machine learning FCN RankNet |
| title | Urban Public Space Safety Perception and the Influence of the Built Environment from a Female Perspective: Combining Street View Data and Deep Learning |
| title_full | Urban Public Space Safety Perception and the Influence of the Built Environment from a Female Perspective: Combining Street View Data and Deep Learning |
| title_fullStr | Urban Public Space Safety Perception and the Influence of the Built Environment from a Female Perspective: Combining Street View Data and Deep Learning |
| title_full_unstemmed | Urban Public Space Safety Perception and the Influence of the Built Environment from a Female Perspective: Combining Street View Data and Deep Learning |
| title_short | Urban Public Space Safety Perception and the Influence of the Built Environment from a Female Perspective: Combining Street View Data and Deep Learning |
| title_sort | urban public space safety perception and the influence of the built environment from a female perspective combining street view data and deep learning |
| topic | female perspective safety perception street view image machine learning FCN RankNet |
| url | https://www.mdpi.com/2073-445X/13/12/2108 |
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