Estimating Disaster Resilience of Hurricane Helene on Florida Counties
<|reference_start|>Estimating Disaster Resilience of Hurricane Helene on Florida Counties: This paper presents a rapid approach to assessing disaster resilience in Florida, particularly regarding Hurricane Helene (2024). This category four storm made landfall on Florida's Gulf Coast in September 2024. Using the Disaster Resilience Index (DRI) developed in this paper, the preparedness and adaptive capacities of communities across counties in Florida are evaluated, identifying the most resilient areas based on three key variables: population size, average per-person income, and the Social Vulnerability Index (SVI). While the Social Vulnerability Index (SVI) accounts for factors like socioeconomic status, household composition, minority status, and housing conditions-key elements in determining a community's resilience to disasters-incorporating a county's population and per person income provides additional insight. A county's total population is directly linked to the number of individuals impacted by a disaster, while personal income reflects a household's capacity to recover. Spatial analysis was performed on the index to compare the vulnerability and resilience levels across thirty-four counties vulnerable to Hurricane Helene's projected path. The results highlight that counties with high income and lower population densities, such as Monroe and Collier, exhibit greater resilience. In contrast, areas with larger populations and higher social vulnerabilities are at greater risk of damage. This study contributes to disaster management planning by providing a rapid yet comprehensive and reassuring socioeconomic impact assessment, offering actionable insights for anticipatory measures and resource allocation.<|reference_end|>
@article{basu2024estimating,
    title={Estimating Disaster Resilience of Hurricane Helene on Florida Counties},
    author={Reetwika Basu, Siddharth Chaudhary, Chinmay Deval, Alqamah Sayeed,
  Kelsey Herndon, Robert Griffin},
    journal={arXiv preprint arXiv:2410.02071},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.02071},
    primaryClass={cs.SI}
}