As urbanization accelerates, the growing needs of children have led to a significant imbalance between supply and demand in urban spaces. Creating child-friendly environments is crucial for enhancing urban resilience and promoting sustainable development. However, there is currently a lack of sufficient quantitative methods to assess child-friendliness and analyze the complex interactions between children’s perceptions and spatial factors. This study uses the central area of Xiamen as a case study to explore how different street environment characteristics influence perceptions of child-friendliness. This study integrates empathy-based stories (MEBS), street scene image analysis, XGBoost machine learning, and GeoSHapley spatial analysis to explore children’s perceptions of urban spaces. The study reveals that: (1) The child-friendly resources in the central urban area of Xiamen are concentrated in the northeastern and Huli districts, while a supply–demand mismatch exists in Siming District, which has a higher population density; (2) Greenness and pavement coverage are critical in shaping child-friendliness, with greenness having the greatest positive impact; (3) Some areas with child-friendly renovations have a lower child-friendliness index, whereas regions like Guanyinshan, which did not undergo renovations, scored higher; (4) The interaction between greenness and openness positively influences perceptions, while enclosure and visual complexity have a negative effect. Building on the need for child-friendly environments, this study develops a spatial analysis framework to quantify the alignment of child-friendly supply and demand in Xiamen’s central urban area, identify regions with mismatched supply and demand, and offer spatial decision support to improve urban environmental quality and promote sustainable development.
Su et al. (2025) studied this question.