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supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

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id caadria2024_272
authors Liu, Jiaxin, Huang, Xiaoran and Yan, Hongming
year 2024
title Exploring Visual Factors Influencing Women’s Perceived Insecurity in Metro Stations and Adjacent Built Environments: A Case Study of Milan, Italy
doi https://doi.org/10.52842/conf.caadria.2024.2.385
source Nicole Gardner, Christiane M. Herr, Likai Wang, Hirano Toshiki, Sumbul Ahmad Khan (eds.), ACCELERATED DESIGN - Proceedings of the 29th CAADRIA Conference, Singapore, 20-26 April 2024, Volume 2, pp. 385–394
summary Prior research has established a direct correlation between women's perceived insecurity in public spaces and the design of the surrounding built environment. However, limited attention has been given to investigating how the built environment influences women's security perceptions within metro systems. This study introduces a novel methodology to analyze the impact of visual factors on the entire walking experience within commuter cores, covering both the station and surrounding areas in Milan Metro Line 1. Both Street View Imagery (SVI) and manual photography are employed for semantic segmentation analysis, followed by expert auditing and machine learning for evaluation. Finally, the study constructs regression models to analyze the relationship between the area ratio of visual factors and women’s perceptions. The results demonstrate that certain factors, such as wider platforms and sidewalks, can positively influence women's safety perceptions in the surrounding and interior spaces, respectively. The models could be used to dissect Milan's other metro stations and their surroundings, offering insights applicable to other metro lines. Moreover, the methodology presents serves as a framework for investigating analogous concerns in diverse cities and delving into the experiences of other marginal groups.
keywords metro station, perceived insecurity, women perception, Google Street View, built environment, deep learning, inclusive city
series CAADRIA
email xiaoran.huang@ncut.edu.cn
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