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Abstract This study investigates how different risk predictors influenced households’ evacuation decisions during a dual‐threat event (Hurricane Laura and COVID‐19 pandemic). The Protective Action Decision Model (PADM) literature indicates that perceived threat variables are the most influential variables that drive evacuation decisions. This study applies the PADM to investigate a dual‐threat disaster that has conflicting protective action recommendations. Given the novelty, scale, span, impact, and messaging around COVID‐19, it is crucial to see how hurricanes along the Gulf Coast—a hazard addressed seasonally by residents with mostly consistent protective action messaging—produce different reactions in residents in this pandemic context. Household survey data were collected during early 2021 using a disproportionate stratified sampling procedure to include households located in mandatory and voluntary evacuation areas across the coastal counties in Texas and parishes in Louisiana that were affected by Hurricane Laura. Structural equation modeling was used to identify the relationships between perceived threats and evacuation decisions. The findings suggest affective risk perceptions strongly affected cognitive risk perceptions (CRPs). Notably, hurricane and COVID‐19 CRPs are significant predictors of hurricane evacuation decisions in different ways. Hurricane CRPs encourage evacuation, but COVID‐19 CRPs hinder evacuation decisions.
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Predicting hurricane evacuation for local neighborhoods across a metropolitan region
To mitigate the devastating impacts of hurricanes on people’s lives, communities, and societal infrastructures, disaster management would benefit considerably from a detailed understanding of evacuation, including the socio-demographics of the populations that evacuate, or remain, down to disaggregated geographic levels such as local neighborhoods. A detailed household evacuation prediction model for local neighborhoods requires both a robust household evacuation decision model and individual household data for small geographic units. This paper utilizes a recently pub- lished statistical meta-analysis for the first requirement and then conducts a rigorous population synthesis procedure for the second. Our model produces predicted non-evacuation rates for all US Census block groups for the Tampa-St. Petersburg-Clearwater Metropolitan Statistical Area for a Hurricane Irma-like storm along with their socio-demographic and hurricane impact risk profiles. Our model predictions indicate that non- evacuation rates are likely to vary considerably, even across neighboring block groups, driven by the variability in evacuation risk profiles. Our results also demonstrate how different predictors may come to the fore in influencing non-evacuation in different block groups, and that predictors which may have an outsize impact on individual household evacuation decisions, such as Race, are not necessarily associated with the greatest differentials in non-evacuation rates when we aggregate households to block group level and above. Our research is intended to provide a framework for the design of hurricane evacuation prediction tools that could be used in disaster management.
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- Award ID(s):
- 2319551
- PAR ID:
- 10511378
- Publisher / Repository:
- International Journal of Disaster Risk Reduction
- Date Published:
- Journal Name:
- International Journal of Disaster Risk Reduction
- Volume:
- 99
- Issue:
- C
- ISSN:
- 2212-4209
- Page Range / eLocation ID:
- 104117
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.ijdrr.2023.104117
