This study assesses urban flood resilience at the subdistrict scale in Shenzhen, China, addressing the lack of fine-grained spatial analysis in existing city-level models. A multidimensional framework integrating natural geography, infrastructure, socioeconomics, emergency management, and risk exposure was constructed, with indicator weights derived from a hybrid Analytic Hierarchy Process–Entropy Weight Method. Spatial autocorrelation analysis (Moran’s I = 0.475, p < 0.001) revealed distinct “resilience fault lines,” with high-resilience clusters in central districts and low-resilience clusters in peripheral industrial belts. Geodetector identified economic intensity (q = 0.46), elevation (q = 0.39), and emergency shelter density (q = 0.37) as dominant drivers, with strong interaction effects. These findings highlight significant resilience inequality, emphasizing the need for targeted, multidimensional interventions to enhance adaptive capacity and inform climate adaptation strategies in rapidly urbanizing coastal megacities.
Huang et al. (Sun,) studied this question.