Supporting Post-disaster Recovery with Agent-based Modeling in Multilayer Socio-physical Networks

TL;DR

This paper introduces PostDisasterSim, an agent-based model that simulates individual and system-wide recovery processes in socio-physical networks post-disaster, providing insights into recovery heterogeneity and policy impacts.

Contribution

It presents a novel ABM framework for analyzing post-disaster recovery in multilayer socio-physical networks, integrating diverse data sources and revealing detailed recovery dynamics.

Findings

Heterogeneity in recovery based on agent and location types

Positive effects of infrastructure improvement plans on recovery

Quantitative analysis of recovery scenarios

Abstract

The examination of post-disaster recovery (PDR) in a socio-physical system enables us to elucidate the complex relationships between humans and infrastructures. Although existing studies have identified many patterns in the PDR process, they fall short of describing how individual recoveries contribute to the overall recovery of the system. To enhance the understanding of individual return behavior and the recovery of point-of-interests (POIs), we propose an agent-based model (ABM), called PostDisasterSim. We apply the model to analyze the recovery of five counties in Texas following Hurricane Harvey in 2017. Specifically, we construct a three-layer network comprising the human layer, the social infrastructure layer, and the physical infrastructure layer, using mobile phone location data and POI data. Based on prior studies and a household survey, we develop the ABM to simulate how evacuated individuals return to their homes, and social and physical infrastructures recover. By implementing the ABM, we unveil the heterogeneity in recovery dynamics in terms of agent types, housing types, household income levels, and geographical locations. Moreover, simulation results across nine scenarios quantitatively demonstrate the positive effects of social and physical infrastructure improvement plans. This study can assist disaster scientists in uncovering nuanced recovery patterns and policymakers in translating policies like resource allocation into practice.