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  1. Free, publicly-accessible full text available November 1, 2024
  2. Abstract

    Evacuation destination choice modeling is an integral aspect of evacuation planning. Outputs from such models are required to estimate the clearance times on which evacuation orders are based. The number of evacuees arriving at each destination also informs allocation of resources and shelter planning. Despite its importance, evacuee destination modeling has not received as much attention as identifying who evacuates and when. In this study, we present a new approach to identify evacuees and determine where they go and when using privacy-enhanced smartphone location data. We demonstrate the method using data from four recent U.S. hurricanes affecting multiple geographies (Florence 2018, Michael 2018, Dorian 2019, and Ida 2021). We then build on those results to develop a new machine learning model that predicts the number of evacuees that move between pairs of metropolitan statistical areas. The machine learning model incorporates hurricane characteristics, which have not been thoroughly exploited by existing methods. The model’s predictive power is comprehensively evaluated through a tenfold cross validation, holdout validation using Hurricane Ida (2021), and comparison with the traditional gravity model. Results suggest that the new model substantially outperforms the traditional gravity model across all performance indicators. Analysis of feature importance in the machine learning model indicates that in addition to distance and population, hurricane characteristics are important in evacuee destination choices.

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  3. Free, publicly-accessible full text available July 1, 2024
  4. Abstract. Regional hurricane risk is often assessed assuming a static housing inventory, yet a region's housing inventory changes continually. Failing to include changes in the built environment in hurricane risk modeling can substantially underestimate expected losses. This study uses publicly available data and a long short-term memory (LSTM) neural network model to forecast the annual number of housing units for each of 1000 individual counties in the southeastern United States over the next 20 years. When evaluated using testing data, the estimated number of housing units was almost always (97.3 % of the time), no more than 1 percentage point different than the observed number, predictive errors that are acceptable for most practical purposes. Comparisons suggest the LSTM outperforms the autoregressive integrated moving average (ARIMA) and simpler linear trend models. The housing unit projections can help facilitate a quantification of changes in future expected losses and other impacts caused by hurricanes. For example, this study finds that if a hurricane with characteristics similar to Hurricane Harvey were to impact southeastern Texas in 20 years, the residential property and flood losses would be nearly USD 4 billion (38 %) greater due to the expected increase of 1.3 million new housing units (41 %) in the region. 
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  5. null (Ed.)
    Abstract We hypothesize that for disaster risk mitigation, many households, despite being aware of their risk and possible mitigation actions, never seriously consider doing anything about them. In mitigation-focused decisions, since there is no equivalent to warning messages, the decision process is likely to evolve over an extended time. We explore what activates hurricane mitigation protective action decisions through three research questions: (1) to what extent are homeowners unengaged in protective action decision making? (2) What homeowner characteristics are associated with lack of engagement? And (3) to what extent do different life events trigger engagement in the decision-making process? We use the Precaution Adoption Process Model to conceptualize engagement as distinct from decision making; the broader protective action decision-making literature to explore drivers of engagement; and Life Course Theory to examine potential transitions from unengaged to engaged. We use survey data of homeowners in North Carolina to examine these questions empirically. Findings suggest that one-third of respondents had never engaged in protective action decisions, that life experiences differ in their occurrence frequency and effect on households’ mitigation decisions, and that some events, such as renovating, reroofing, or purchasing a home may offer critical moments that could be leveraged to encourage greater engagement in mitigation decision making. 
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