Against the backdrop of Shenzhen’s high-density urban environment, the multifunctional design of water purification plants offers dual benefits: providing residents with urban green spaces while simultaneously mitigating NIMBY sentiments due to their inherent characteristics. Unlike traditional urban development, Shenzhen’s water purification plants integrate into residents’ daily lives. Therefore, optimizing the built environment and road network structure to enhance residents’ perceptions of proximity benefits while reducing NIMBY (Not In My Backyard effect) sentiments holds significant implications for the city’s sustainable development. To address this question, this study adopted the following three-step mixed-methods approach: (1) It examined the relationships among residents’ YIMBY (Neighboring Benefits Effect) and NIMBY perceptions, perceptions of park spaces atop water purification plants, and perceptions of accessibility through questionnaire surveys and structural equation modeling (SEM), establishing a scoring framework for comprehensive YIMBY and NIMBY perceptions. (2) Random forest models and Shapley Additive Explanations (SHAP) analysis revealed nonlinear relationships between the built environment and composite YIMBY and NIMBY perceptions. (3) Spatial syntax analysis categorized the upgraded road network around the water purification plant into grid-type, radial-type, and fragmented-type structures. Scatter plot fitting methods uncovered relationships between these road network types and resident perceptions. Finally, negative perceptions were mitigated by optimizing path enclosure and reducing visual obstructions around the water purification plant. Enhancing neighborhood benefits—through improved path safety and comfort, increased green spaces and resting areas, optimized path networks, and diversified travel options—optimized the built environment. This approach proposes design strategies to minimize NIMBY perceptions and maximize YIMBY perceptions.

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.

Global climate change and rapid urbanization have intensified extreme precipitation events, making urban pluvial flood risk management particularly urgent. However, existing research exhibits theoretical and methodological limitations in reconciling pluvial flood resilience within high-density urban development, especially lacking a quantitative framework for human mobility resilience based on natural hydrological units. To address this, this study takes 48 natural catchment areas in Shenzhen as research units. By integrating mobile phone signaling data, remote sensing imagery, and multi-dimensional spatial form indicators, it constructs a post-disaster recovery curve based on population dynamics to quantify human mobility resilience. Using Principal Component Analysis (PCA), Ordinary Least Squares (OLS) regression, and the K-means clustering method, this research analyzes the mechanisms through which green infrastructure, road network structure, and three-dimensional building morphology influence resilience. The results show that three-dimensional building morphology is the core driver of post-disaster recovery capacity, with Floor Area Ratio (FAR), Standard Deviation of Building Height (SDBH), and Building Coverage Ratio (BCR) significantly promoting recovery, while Building Shape Coefficient (BSC) exerts an inhibitory effect. A key comparative analysis reveals that the model based on natural catchment areas has significantly better explanatory power than the model using stormwater management units, and the dominant factors differ: building morphology factors are more prominent in natural hydrological units, whereas road network structure factors are more significant in stormwater management units. This study confirms that natural geographic boundaries can more authentically reveal the intrinsic “morphology–resilience” relationship, providing an important theoretical and empirical basis for optimizing the planning and management units of sponge cities in high-density urban areas.