The Differential Impact Mechanisms of the Built Environment on Running-Space Selection: A Case Study of Suzhou’s Gusu District and Industrial Park District

Guided by the “Healthy China” initiative, understanding the impact of the built environment on running behavior is essential for encouraging regular physical activity and advancing public health. This study addresses a critical gap in healthy city research by examining the spatial heterogeneity in how urban environmental contexts affect residents’ running preferences. Focusing on two contrasting areas of Suzhou, namely the historic Gusu District and the modern Industrial Park District, we developed a 5Ds-based analytical framework (density, accessibility, diversity, design, and visual) that incorporates Suzhou’s unique water networks and street features. Methodologically, we used Strava heatmap data and multi-source environmental indicators to quantify built-environment attributes and examined their relationships with running-space selection. We applied linear regression and interpretable machine learning to reveal overall associations, while geographically weighted regression (GWR) was used to capture spatial variations. Results reveal significant spatial heterogeneity in how the built environment influences running-space selection. While the two districts differ in their urban form, runners in Gusu District prefer dense and compact street networks, whereas those in Industrial Park District favor open, natural spaces with higher levels of human vibrancy. Despite these differences, both districts show consistent preferences for spaces with a more intense land use mix, stronger transportation accessibility, and larger parks and green spaces. The multi-dimensional planning strategies derived from this study can improve the urban running environment and promote the health and well-being of residents.