Green-space design and recreational running, walking and cycling: an ecological analysis of fitness-tracking trajectories in Nanchang, China

Background Crowdsourced fitness-tracking trajectories show where recreational physical activity occurs at fine spatial resolution, but few studies have used green spaces themselves as the analytical unit. We examined how internal design, landscape configuration and surrounding urban context are associated with recreational running, walking and cycling across the same set of green spaces. Methods In this ecological cross-sectional study, we linked recreational activity trajectories recorded on three fitness platforms to 50 urban green-space polygons in Nanchang, China, yielding 729,670 site-level person-times. Site activity was expressed as mean daily person-times by activity type and compared with field counts (Spearman ρ = 0.78). We derived 97 candidate indicators from site geometry, path networks, Sentinel-2 imagery, landscape metrics, panoramic imagery and points of interest within a 1-km buffer. Partial least squares structural equation models related three formative constructs (internal spatial characteristics, landscape configuration and surrounding urban context) to activity. An exploratory model added a site-level Positive Response Index (PRI) derived from geotagged facial-expression images. Robustness checks included permutation tests with false-discovery-rate control, Moran’s I and spatial error models. Results Internal spatial characteristics were positively associated with running (β = 0.37), walking (β = 0.44) and cycling (β = 0.38). Landscape configuration was associated with running (β = 0.25) and walking (β = 0.21) but not cycling. Surrounding urban context was associated only with walking (β = 0.26). The models explained 32%, 19% and 14% of between-site variance in running, walking and cycling, respectively. In the exploratory model, PRI was associated with walking, and the indirect products through PRI were 0.10 for internal spatial characteristics and 0.13 for surrounding urban context. Neither activity outcomes nor model residuals showed statistically supported spatial autocorrelation. Conclusions Within the same set of green spaces, design and contextual attributes showed activity-specific association patterns: internal route and site characteristics were associated with all three activities, whereas the surrounding urban context was associated only with walking. Site-level trajectory analysis provides a practical basis for activity-specific green-space assessment; longitudinal and individual-level studies can test whether these associations reflect effects of design.