With the growing emphasis on running in urban health initiatives, understanding the spatiotemporal dynamics of running behavior has become essential for smart city development. This study harnesses multi-source big data—including running trajectories, points of interest (POIs), and remote sensing data—to systematically analyze factors influencing running space selection. Through stepwise regression analysis, we identify 16 significant variables encompassing accessibility, diversity, and comfort dimensions. The Geographical and Temporally Weighted Regression (GTWR) model is then employed to uncover distinct spatiotemporal heterogeneity patterns, demonstrating how these factors variably influence running activities across different urban zones and time periods. The methodology and findings contribute to geospatial analysis in urban health studies while providing practical guidance for creating more inclusive, runner-friendly urban environments.