Enhancing infrastructural dynamic responses to critical residents’ needs for urban resilience through machine learning and hypernetwork analysis

Abstract

The increasing frequency of extreme weather events poses ever greater challenges to urban resilience and residents’ quality of life. Despite a growing trend advocating for an anthropocentric approach to urban resilience, there remains an inadequate understanding of the evolving hierarchical needs of residents during post-disaster periods, especially considering the interplay with infrastructure service restoration. This study aims to address the gap by elucidating how emergency governance and urban infrastructure repairs can effectively address critical residents’ needs, providing empirical insights for improved resource allocation in infrastructure rush-repair scenarios. First, we categorize residents’ needs into three layers (safety and health, social livelihood and civic engagement) using Latent Dirichlet Allocation topic modeling. Subsequently, we present an urban resilience assessment framework that traces the recovery of residents’ needs alongside dynamic infrastructural functionality restoration, benchmarking against pre-disaster levels. Additionally, hypernetwork analyses are adopted to identify critical and evolving patterns of residents’ post-disaster needs over time. The robustness of our proposed framework is validated through its application to a dataset comprising 220,567 records from residents’ appeals during three recurrent rainfall events in Beijing. Theoretically, this study models the dynamic interactions between residents’ needs and infrastructure response during urban post-disaster recovery. Practically, the pinpointed critical needs guide efficient infrastructure rush-repairs and proactive disaster prevention in infrastructure maintenance.