RAPID: Improving Predictions of Evacuation Decisions

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This project will use anthropological approaches to improve measurement and understanding of hurricane evacuation decisions, and ultimately improve prediction of evacuations. Population surveys have attempted to understand evacuation principally in terms of the socio-demographic characteristics of those who do and do not evacuate. Data collected on evacuation estimates over the last two decades indicate that about one-third of residents in evacuation zones fail to evacuate. Despite this, there is no clear or standardized set of evacuation rationales, making it difficult to generalize across studies and regions. This project will explore whether a new set of methods for evaluating evacuation decisions can be replicated across regions and storms, in order to better predict evacuation decisions. Findings will be disseminated to organizations that explore and manage the causes, consequences, and complexities of disaster management and recovery.This RAPID award supports the collection of time-sensitive data concerning mandatory evacuation for Hurricane Irma in Florida. In order to get reliable reports concerning motives and beliefs about evacuation, interviews must be conducted in a context where respondents can form the appropriate associative cognitive state related to anthropogenic events necessary for free-listing exercises. This also needs to be conducted before another storm occurs. The researchers will use interviewing techniques from anthropology to collect reasons for evacuation/non-evacuation. Interviews will be conducted with neighbor-pairs (one who did and one did not evacuate) in both higher and lower storm risk zones (20 pairs) to elicit reasons for evacuating or not evacuating. Neighbor-matching is important to control for extraneous factors (such as wealth or distance from water) that might influence evacuation. Recent research suggests that free-list interviews are far more productive than standard open-ended questions. The researchers' previous work, successfully used free-listing to obtain a comprehensive set of evacuation/non-evacuation rationales on Hurricane Ike, in Galveston, Texas. The data collected in this project would improve the robustness, reliability, and replicability of these methodologies and models across evacuation contexts. If the data is replicable, the project would constitute a methodological innovation for anthropology specifically, and for disaster-related science more generally.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.