Dynamics of Post-disaster Recovery in Behavior-dependent Business Networks
<|reference_start|>Dynamics of Post-disaster Recovery in Behavior-dependent Business Networks: The recovery of businesses after a disaster is vital to community economic resilience, yet the network dynamics influencing the speed and spillover effects of recovery remain poorly understood. Understanding these dynamics is essential for characterizing economic resilience and informing more effective recovery policies. This study explores the extent to which post-disaster business recovery is shaped by network diffusion processes within pre-disaster business dependency networks, driven by visitation behaviors among business points of interest (POIs). We developed a network diffusion model to simulate recovery across businesses in the Louisiana Gulf Coast following Hurricane Ida (2021) and assessed its performance using empirical data. Our analysis focuses on four key areas: (1) the presence of a diffusion process influencing recovery across the business network; (2) variations in how different business types depend on others for recovery; (3) identification of recovery multiplier businesses that accelerate regional recovery; and (4) differences in recovery multipliers across high- and low-income areas. The findings reveal that business recovery is governed by diffusion dynamics in these behavior-based networks, with recovery speed closely linked to pre-disaster visitation patterns. Retail and service businesses are identified as key recovery multipliers whose rapid recovery accelerates the broader business network's recovery, enhancing economic resilience. Additionally, recovery multipliers vary between high- and low-income areas. This study enhances our understanding of network mechanisms in post-disaster recovery and offers valuable insights for improving recovery policies.<|reference_end|>
@article{liu12024dynamics,
    title={Dynamics of Post-disaster Recovery in Behavior-dependent Business
  Networks},
    author={Chia-Fu Liu1, Chia-Wei Hsu, and Ali Mostafavi},
    journal={arXiv preprint arXiv:2409.12357},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.12357},
    primaryClass={cs.SI physics.soc-ph}
}