Resilience Characterization of Physical Activity: Investigating Blue Landscape Patterns and Urban Morphological Factors in Shenzhen’s Stormwater Management Units

Rapid urbanization-induced extreme rainstorms severely disrupt social functions. Previous research often focused on “de-densification” strategies, which are difficult to adapt to high-density Sponge City Stormwater Management Units (SMUs) that carry core development functions. This study uses Shenzhen as a case study, utilizing Keep movement big data as a “social sensor” for system function perception and introducing the Socio-Ecological-Technological Systems (SETS) theory to construct a “recovery (RCN)–resistance (MI)” binary assessment framework. Through systematic clustering and hierarchical regression models, the driving mechanisms of blue landscape patterns, topography, road networks, and the built environment on social behavioral resilience are systematically parsed. The results show: (1) Road network morphology dominates resistance, while multi-dimensional elements collaborate for recovery. Resistance (MI) is primarily dominated by macro road network detour resistance (TPD2000, β = 0.956), while recovery depends on the synergistic support of blue space interspersion (Blue_IJI), topography, and micro-circulation road networks. (2) Green infrastructure fails in the model due to efficiency bottlenecks, empirical evidence of weakened regulation caused by green space fragmentation in ultra-high-density environments. (3) Low-density, eco-centric built environments provide dual synergistic gains for resilience. Based on this, a “Bidirectional Socio-Ecological Resilience Needs Pyramid” model is constructed, identifying four governance types such as the “Synergistic Balanced Type”. This study provides a quantitative basis for the transition from administrative control to precise morphological governance in high-density cities.