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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.
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.
Road innovation is transforming civil construction by introducing technologies and approaches that improve the efficiency, safety and sustainability of transportation infrastructure. These traffic management systems and real-time monitoring sensors optimize vehicular flow and minimize congestion. The objective of the research is to analyze research trends related to road innovation in civil construction of public spaces. The research approach is mixed, combining qualitative and quantitative methods. A descriptive and retrospective bibliometric analysis was carried out, which was complemented with a documentary review, developed in the Google academic database, Scielo and SCOPUS. The behavior of the researches was decreasing, characterized by a polynomial function with a maximum peak in 2020 of 27 researches. Research in environmental sciences in China predominated. Three lines of scientific research and their main thematic nuclei were identified and explored as key categories and systematized in order to recognize the trends of road innovation in the civil construction of public spaces. The main results were introduced in China with analysis of impacts on the population, use of geographic information systems, environmental pollution studies, among others. Despite international efforts to develop sustainable strategies for the construction and management of public spaces, there are still significant challenges to be addressed to ensure innovation and sustainability in this area.
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