Spatiotemporal Evolution, Constraints, and Configurational Driving Paths of District-Level Urban Resilience: A Case Study of Xi’an, China

Addressing meso-scale sensing voids and resource misallocations, this study constructs an integrated “Performance Sensing–Bottleneck Diagnosis–Configuration Identification” framework to evaluate the spatiotemporal evolution of resilience across Xi’an’s districts (2018–2023). This research operationalizes a diagnostic-driven analytical pipeline coupling multi-source parameters with the CRITIC method to complement static stock accounting with dynamic performance sensing. This logic integrates Dagum Gini decomposition to pinpoint spatiotemporal bottlenecks and fuzzy-set QCA (fsQCA) to uncover driving pathways, utilizing an “Obstacle–Correlation” matrix to provide an objective basis for antecedent selection. The results show the following: (1) A “V-shaped” spatiotemporal trajectory and 2020 “resilience inversion” (dipping to 0.364) highlight the sensitivity of dynamic performance sensing in exposing latent vulnerabilities. (2) Persistent “center-periphery” gradients exist, with administrative siphoning driving 66.7% of inequality; diagnosis identifies distinct spatiotemporal pathologies: rigid spatial constraints in urban cores versus service imbalances in expansion zones. (3) Three equifinal pathways and an “asymmetric cancellation” effect prove that resilience hinges on configurational fit rather than linear stacking, where extreme single-dimension shortfalls neutralize collective gains. By bridging situational pathologies and governance pathways, this framework provides a robust empirical basis for the refined allocation of resources in complex environments.