https://doi.org/10.1140/epjds/s13688-026-00621-w
Research
Evaluating perceived built environment factors influencing children’s walking to school: an XGBoost–SHAP approach
1
School of Architecture, Chang’an University, Chang’an Road, 710064, Xi’an, China
2
Qingdao Urban Planning & Design Research Institute, Hong Kong West Road, Qingdao, China
a
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Received:
6
September
2025
Accepted:
12
January
2026
Published online:
28
May
2026
Abstract
Walking to school promotes children’s health and sustainable mobility, serving as a key metric for child-friendly cities. However, the share of children walking to school has continued to decline worldwide. The understanding of child-environment interactions is limited by previous studies’ reliance on linear assumptions and single data sources, which obscures their inherent complexity. To address this, we developed a multi-scale analytical framework that integrates street view imagery, perceptual surveys, and geospatial data from a sample of 906 children. We used XGBoost and SHAP to disentangle the complex associations between the built environment, objective street features, subjective perceptions, and walking frequency. The model demonstrated robust performance (
). The results show three main findings: (1) building density is the primary predictor; (2) subjective perceptions and objective street features outperform traditional road network morphology indicators in explanatory power; and (3) most environmental factors exhibit nonlinear relationships, manifesting as threshold effects or diminishing marginal returns. This study underscores that children’s walking to school depends not merely on physical access or safety, but is significantly associated with subjective perceptions and objective street features once basic physical thresholds are met. These findings shift the focus of child-friendly planning from traditional infrastructural metrics to perceptual and experiential dimensions.
Key words: Children / Walking / Built environment / Objective street features / Subjective street perceptions
Handling Editor: Luca Aiello
© The Author(s) 2026
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