Street safety through children's eyes: integrating Photovoice and machine learning to uncover disparities in environmental safety perception between children and adults

Abstract

The relationship between the built environment and human safety perception has been widely studied, but existing research lacks a child-friendly perspective in exploring the impact mechanisms of street environmental elements on children's safety perception and their intergenerational differences with adults. The study employed "Photovoice" method to assess children's and adults' perceptions of urban street safety. By integrating dual-perspective street-view images with deep learning techniques, a large-scale evaluation of street safety perception was conducted. Additionally, random forest model was used to quantify the differences in the impact of various elements on children's and adults' safety perception. Results indicate that children generally perceive lower environmental safety compared with adults, with significant differences observed in spatial preferences, attention patterns, emotional response models, and the perception of environmental elements. The study finds that vegetation, water bodies, and sidewalks positively influence children's safety perception, whereas traffic-related elements such as motor vehicles and certain complex artificial structures evoke negative reactions. Children's safety perception shows a steady trend, while adults' perception is more complex. This study provides methodological innovations and practical pathways for child-friendly urban development, emphasizing the need to consider children's unique perceptual needs and promoting a transition toward age-inclusive urban spaces.

Keywords: Photovoice; deep learning; environmental safety perception; intergenerational differences; street view images.