How urban environments structure running behaviour in Beijing during winter, spring, and summer 2024: spatiotemporal patterns and configuration-specific interactions from running trajectory data.

BACKGROUND

Understanding how urban environments stimulate routine physical activity is a central issue in public health. Running, as a low-threshold and widely accessible exercise, serves as a sensitive indicator of environmental influence. Yet, few studies have used methods capable of capturing the non-linear and context-dependent interactions through which multiple built-environment factors jointly shape running. This study investigates the spatiotemporal patterns of running and identifies how combinations of environmental features across different urban scenarios affect behavioural activation.

METHODS

We analysed 83,302 GPS-tracked running trajectories from Beijing. Built-environment indicators were integrated across five dimensions and examined using Light Gradient Boosting Machine with SHapley Additive exPlanations (SHAP). To enhance behavioural interpretability, variables were grouped into scenario-informed contexts representing commuting, restorative, and training environments. Temporal and spatial analyses were also conducted to identify diurnal, weekly, and spatial clustering patterns of running activity.

RESULTS

Running exhibited clear morning-evening peaks, an inverted-U weekly rhythm, and a concentric spatial structure concentrated in central areas and along continuous spaces such as waterfronts and forest sports parks. SHAP interaction analysis further shows that non-linear built-environment effects are organised through configuration-specific interaction structures rather than marginal feature responses. Three dominant configurations are identified: (i) visual–landscape structures combining vegetation texture, sky openness, and water proximity; (ii) urban density configurations linking residential intensity, building form, accessibility, and nightlight intensity; and (iii) training configurations integrating facility density, route continuity, surface flatness, and shading.

CONCLUSIONS

Running behaviour is jointly shaped by temporal rhythms, spatial clustering, and structured interactions among built-environment features. The findings demonstrate that physical activity patterns reflect the conditional contribution of co-occurring spatial attributes, providing evidence for designing more health-supportive and activity-friendly urban environments.