Urban flooding threatens service continuity, livelihoods, and social well-being in coastal megacities. We propose and test a framework that integrates high-resolution, Geographic Information System-based exposure mapping (inundation hotspots, drainage proximity, land cover, and critical facilities) with community-level capacity indicators (preparedness, adaptive capacity, and recovery) derived from a mixed-methods design. Using routinely available spatial data and rapid community assessment, we generate a composite index of flood resilience, identify chronic ponding clusters, and prioritize low-regret interventions. Results reveal spatially coherent hotspots that coincide with high built-up density and limited adaptive capacity; neighborhoods with weaker social preparedness also report longer recovery times. A short list of interventions, including micro-drain maintenance, targeted blue–green retrofits near drainage bottlenecks, and neighborhood-level early-warning and risk communication, offers achievable near-term impact within realistic municipal resource constraints. Sensitivity checks indicate index stability with respect to weighting choices. The approach is transferable to other flood-prone cities by coupling GIS layers with community metrics, providing a transparent basis for investment sequencing and policy design. Findings highlight that combining exposure and capacity yields more actionable prioritization than either dimension alone and aligns with global disaster risk reduction principles.
Widodo et al. (Sun,) studied this question.