Walkability Perceptions and Gender Differences in Urban Fringe New Towns: A Case Study of Shanghai

Abstract

Urban fringe areas, characterized by relatively larger community sizes and lower population densities compared to central areas, may lead to variations in walkability as well as gender differences, such as safety perception. While objective measurements have received considerable attention, further research is needed to comprehensively assess subjective perceptions of walking in the urban periphery. As a case study, we evaluated survey responses of community perceptions of “Imageability”, “Enclosure”, “Human scale”, “Complexity” and “Safety” of Shanghai’s five new towns, comparing these with responses from the central area in terms of gender difference, and analyzed influencing factors and prediction performance of machine learning (ML) models. We developed a TrueSkill-based rating system to dynamically collect audits of street view images (SVIs) from professional students and used the result to integrate with Geographic Information Systems (GIS), Computer Vision (CV), Clustering analysis, and ML algorithm for further investigation. Results show that most of the new towns’ communities are perceived as moderately walkable or higher, with the city center’s community exhibiting the best walkability perceptions in general. Male and female perceptions of the “Human scale” and the factors that affect it differ little, but there are significant disparities in the other four perceptions. The best-performing ML models were effective at variable explanations and generalizations, with Random Forest Regression (RFR) performing better on more perception predictions. Responses also suggest that certain street design factors, such as street openness, can positively influence walkability perceptions of women and could be prioritized in new town development and urban renewal for more inclusive and walkable cities.