A Resident-Centric Framework for Postdisaster Infrastructure Recovery: Characterizing Hierarchical Needs and Fulfillment Cycles to Assess Urban Resilience

Allocating resources effectively for the recovery of facilities and services after a disaster is essential to improve the sustainability and well-being of affected residents who face various challenges and pressures. A significant challenge in this process is identifying vulnerabilities and strategically prioritizing resource to address residents’ postdisaster needs. These needs change over time, including both their urgency and the extent to which they are fulfilled, as facilities recover. However, currently, no quantitative computational model characterizes these needs and further guides resilience evaluation and infrastructure rush repair. This study introduces an evaluation framework that monitors residents’ unmet needs by keeping track of the needs they report and addressing feedback gathered from social sensing. Residents’ postdisaster needs are first identified by clustering 336,928 pieces of appeal and feedback data using latent Dirichlet allocation (LDA) topic modeling. These needs are then validated through a Delphi study and modeled into a three-level hierarchy. Then, we characterized residents’ expected recovery curve over time as a benchmark and used the actual recovery based on needs fulfillment to quantify the discrepancy, which served as the proxy for resilience. The proposed framework was validated using empirical data from a regular and an extreme rainstorm that occurred in 2020 and 2023 in Beijing. The findings offer valuable insights into how residents’ most urgent needs and the corresponding types of infrastructure repair needed change across different recovery phases and for different types of rainstorms. These insights help guide policymakers in improving the effectiveness of disaster response and in taking proactive measures for infrastructure maintenance to prevent future disasters.