From Behavioral Characteristics to Spatiotemporal Structures: Identifying Urban Active-Healthy Walking Support Types and Their Environmental Determinants

From an active-health perspective, regular walking is a key pathway for mitigating chronic disease risks and promoting sustained health benefits. Existing studies have primarily assessed urban walkability using static or aggregated measures of walking intensity, which insufficiently capture the capacity of urban spaces to continuously support walking behavior over time. This study aims to identify urban walking support types by incorporating the temporal structure of walking behavior beyond intensity alone. Crowdsourced walking trajectory data are used to construct a multidimensional behavioral indicator system integrating walking intensity, temporal stability, and rhythmic characteristics over an annual period. An unsupervised clustering framework combining nonlinear dimensionality reduction and density-based clustering is applied to identify distinct walking support types, while interpretable machine-learning models are employed to examine the relative roles of built-environment factors in differentiating these types. The results indicate that urban walking support does not vary continuously along a single intensity dimension but instead forms discrete spatial types shaped by multiple behavioral temporal characteristics. These types exhibit clear differences in temporal walking structures and associated environmental constraints. By emphasizing behavioral temporal stability and rhythm, this study provides a process-oriented understanding of urban walking support and supports typology-based spatial identification beyond intensity-based assessments.