A Multi-Criteria Decision-Support Framework for Dual-Use Green-Space Shelter Siting in High-Density Urban Systems: An NRBO–P-Median Approach

High-density cities are complex, tightly coupled systems in which ecological infrastructure, disaster-resilience capacity, and transportation networks interact and constrain one another under intensifying climate-driven hazards and urbanization pressure. Urban green spaces sit at the intersection of these subsystems, offering dual ecological and emergency-shelter value, yet their siting is rarely optimized as a system-level problem: coverage, structural safety, and evacuation accessibility are typically treated in isolation, and classical heuristic solvers struggle to reconcile these interdependent, competing objectives at city scale. This study reframes green-space shelter siting as a socio-technical urban system problem and develops a multi-criteria geospatial decision-support framework that couples a Newton–Raphson-based Optimizer (NRBO) with a P-Median (minimum-weighted-distance) formulation, integrating nine quantitative indicators across the three interacting subsystems above into a single decision-theoretic model. Benchmarked against exhaustive enumeration, the framework reproduces identical system-optimal solutions at a computational cost more than two orders of magnitude lower, demonstrating that intelligent optimization can scale system-level decision-making to city-wide planning problems. By applying this framework to the historic core of Nanjing, this study demonstrates how coordinated, cross-subsystem optimization—rather than single-objective design—can resolve conflicting spatial constraints and yield highly resilient urban outcomes. The empirical findings validate the framework’s capacity to significantly enhance coverage, safety, and accessibility simultaneously. The proposed NRBO–P-Median framework is data-driven, computationally scalable, and structurally transferable to other dense urban systems, offering planners and policymakers a generalizable decision-support tool for embedding disaster-resilience functions into intelligent urban green-infrastructure governance.