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1. Protection or Peril of Following the Crowd in a Pandemic-Concurrent Flood Evacuation

   Elisa Borowski, Amanda Stathopoulos ( January 2023 , Natural hazards review)

   The decisions of whether and how to evacuate during a climate disaster are influenced by a wide range of factors, including emergency messaging, social influences, and sociodemographics. Further complexity is introduced when multiple hazards occur simultaneously, such as a flood evacuation taking place amid a viral pandemic that requires physical distancing. Such multihazard events can necessitate a nuanced navigation of competing decision-making strategies wherein a desire to follow peers is weighed against contagion risks. To better understand these trade-offs, we distributed an online survey during a COVID-19 pandemic surge in July 2020 to 600 individuals in three midwestern and three southern states in the United States with high risk of flooding. In this paper, we estimate a random parameter discrete choice model in both preference space and willingness-to-pay space. The results of our model show that the directionality and magnitude of the influence of peers’ choices of whether and how to evacuate vary widely across respondents. Overall, the decision of whether to evacuate is positively impacted by peer behavior, while the decision of how to evacuate (i.e., ride-type selection) is negatively impacted by peer influence. Furthermore, an increase in flood threat level lessens the magnitude of peer impacts. In terms of the COVID-19 pandemic impacts, respondents who perceive it to be a major health risk are more reluctant to evacuate, but this effect is mitigated by increased flood threat level. These findings have important implications for the design of tailored emergency messaging strategies and the role of shared rides in multihazard evacuations. Specifically, emphasizing or deemphasizing the severity of each threat in a multihazard scenario may assist in: (1) encouraging a reprioritization of competing risk perceptions; and (2) magnifying or neutralizing the impacts of social influence, thereby (3) nudging evacuation decision-making toward a desired outcome. 

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2. Location intelligence reveals the extent, timing, and spatial variation of hurricane preparedness

   https://doi.org/10.1038/s41598-022-20571-3  

   Li, Bo ; Mostafavi, Ali ( December 2022 , Scientific Reports)

   Abstract Hurricanes are one of the most catastrophic natural hazards faced by residents of the United States. Improving the public’s hurricane preparedness is essential to reduce the impact and disruption of hurricanes on households. Inherent in traditional methods for quantifying and monitoring hurricane preparedness are significant lags, which hinder effective monitoring of residents’ preparedness in advance of an impending hurricane. This study establishes a methodological framework to quantify the extent, timing, and spatial variation of hurricane preparedness at the census block group level using high-resolution location intelligence data. Anonymized cell phone data on visits to points-of-interest for each census block group in Harris County before 2017 Hurricane Harvey were used to examine residents’ hurricane preparedness. Four categories of points-of-interest, grocery stores, gas stations, pharmacies and home improvement stores, were identified as they have close relationship with hurricane preparedness, and the daily number of visits from each CBG to these four categories of POIs were calculated during preparation period. Two metrics, extent of preparedness and proactivity, were calculated based on the daily visit percentage change compared to the baseline period. The results show that peak visits to pharmacies often occurred in the early stage of preparation, whereas the peak of visits to gas stations happened closer to hurricane landfall. The spatial and temporal patterns of visits to grocery stores and home improvement stores were quite similar. However, correlation analysis demonstrates that extent of preparedness and proactivity are independent of each other. Combined with synchronous evacuation data, CBGs in Harris County were divided into four clusters in terms of extent of preparedness and evacuation rate. The clusters with low preparedness and low evacuation rate were identified as hotspots of vulnerability for shelter-in-place households that would need urgent attention during response. Hence, the research findings provide a new data-driven approach to quantify and monitor the extent, timing, and spatial variations of hurricane preparedness. Accordingly, the study advances data-driven understanding of human protective actions during disasters. The study outcomes also provide emergency response managers and public officials with novel data-driven insights to more proactively monitor residents’ disaster preparedness, making it possible to identify under-prepared areas and better allocate resources in a timely manner. 

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3. Evacuate or social distance? Modeling the influence of threat perceptions on hurricane evacuation in a dual‐threat environment

   https://doi.org/10.1111/risa.14202  

   Wu, Hao‐Che ; Murphy, Haley ; Greer, Alex ; Clay, Lauren ( August 2023 , Risk Analysis)

   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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4. Planning for Special Needs Shelters: A Hurricane Track Uncertainty-Based Approach Integrating Coastal Inundation and Accessibility

   Yang, J. ( January 2023 , Transportation Research Board Conference)

   Storm surge and evacuation traffic under the observed track of Hurricane Michael (2018) showed clear accessibility and evacuation challenges for Panama City, Florida although the city was not hit directly. Since a possible Hurricane Michael track within National Hurricane Center (NHC)'s forecasted hurricane cone was Panama City, this paper tries to answer the following questions: What if Hurricane Michael hit Panama City directly? How would the special needs populations and their accessibility to Special Needs Shelters (SpNS) be impacted, and what could have been done to alleviate this impact? A previously validated storm surge model was used to predict storm surge inundations under this different hurricane track. Based on the impact of these coastal inundations, a GIS-based optimization methodology was developed to evaluate the accessibility and siting of special needs shelters. Results indicate that if Hurricane Michael had shifted to Panama City in 2018, most of the coastal region of Panama City would have been inundated, compelling residents to evacuate. The possible landfall of Michael in this simulation would also lead to a maximum storm surge of 5 to 6 m on the coast, which is above FEMA's 100-year flood elevation. In addition, the only evacuation route out of Panama City area, when the bridges with their access roads were flooded, was US 231. This would have been life-threatening since there is only one SpNS in the north of the city accessible by this roadway. The proposed analysis studies the accessibility of this SpNS shelter and provides a reasonable approach for SpNS shelter siting or repurposing regular shelters for this purpose based on the hypothesized travel time most likely to be experienced on roadway networks based on the impact of Hurricane Michael. Emergency plans can be updated by the results of this optimization model, which can locate additional sites or shelter locations while minimizing the travel costs and integrating the impact of storm surge modeling and transportation accessibility analysis. 

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5. Characterizing Social Community Structures in Emergency Shelter Planning

   Pamukcu, D. ; Zobel, C.W. ; Arnette, A. ( June 2020 , Proceedings of the 17th International Conference on Information Systems for Crisis Response and Management - ISCRAM 2020)

   During emergencies, it is often necessary to evacuate vulnerable people to safer places to reduce loss of lives and cope with human suffering. Shelters are publically available places to evacuate, especially for people who do not have any other choices. This paper overviews emergency shelter planning in disaster mitigation and preparation and discusses the need for better responding to people who need to evacuate during emergencies. Recent evacuation studies pay attention to integrating social factors into evacuation modeling for better prediction of evacuation decisions. Our goal is to address the impact of social behavior on the sheltering choices of evacuees and to explore the potential contributions of including social network characteristics in the decision-making process of authorities. We present the shelter utilization problem in South Carolina during Hurricane Florence and discuss an agent-based modeling approach that considers social community structures in modeling the shelter choice behavior of socially connected individuals 

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