RAPID: Disaster Recovery Decision Making in Remote Tourism-dependent Communities

当建筑环境受到重大破坏和破坏时，飓风会破坏社区的活力，特别是在偏远的、依赖旅游业的沿海社区。在房屋、企业和基础设施受到影响后，旅游企业主和员工开始做出影响沿海社区恢复能力的恢复决策。该快速反应研究（RAPID）项目研究了影响北卡罗来纳州奥克拉科克和哈特拉斯恢复决策的因素和信息网络，这些因素和信息网络受到飓风多利安（2019年9月）的严重影响。这些社区地处偏远，特别容易受到伤害，因为其交通网络建立在单一的州公路和/或渡轮服务上，一旦遭到破坏，可能影响获得恢复资源。他们对旅游业的依赖也使他们在经济上容易受到旅游业利益的影响（例如，就业、税收）依赖于交通便利和住宿供应。由于风暴强度和频率可能随着海平面上升而增加，类似的灾害影响在其他依赖旅游业的沿海社区可能越来越普遍。通过确定影响早期恢复决策的因素和网络，该项目有助于NSF的使命，以促进国家福利和促进科学的进步。具体而言，这项研究增加了对旅游部门内个别灾后恢复决策的了解，这些决策会累积影响社区重建其建筑环境和经济基础的能力。本研究还确定了沟通途径，使更多的知情近期恢复决策，这可以在未来的灾害响应，旅游危机管理，社区弹性建设effortes.The我们的项目的目标是比较的因素和信息网络，影响信息处理和恢复决策在两个偏远的灾后旅游依赖社区。该项目有四个目标：（1）记录影响旅游业利益攸关方恢复决定的各种因素;（2）确定旅游业利益攸关方为了解恢复决定而使用的信息网络;（3）评估通过这些网络激活的恢复信息的处理程度;（4）了解影响风险认知和预期恢复决定的决策途径。这些目标将通过将风险信息寻求和处理（RISP）模型与社会网络分析（SNA）相结合来实现，以分析在与旅游企业主，员工和社区领导人以及国家应急响应官员进行深入访谈时产生的数据。将对影响恢复决策的因素和RISP模型预测因子的数据进行专题分析，并将使用SNA绘制信息网络，以确定恢复决策的影响途径。此外，将比较社区一级的数据，以确定不同的基础设施损坏和恢复资源的获取是否以及如何影响决策，这将提高我们的调查结果的可转移性。在近期灾后背景下收集这些数据至关重要，因为旅游目的地的状态（例如，回收资金的可用性、其他人的回收决定）影响未来决策选择的可用性。调查结果将揭示影响企业和家庭恢复决策的协同检查中的灾难恢复决策的因素和信息途径。更具体地说，调查结果将确定当前和以往经验的信息处理方式，对劳动力能力和建筑环境恢复的看法，网络支持的可用性和预期的旅游需求影响恢复决策，以及暴露短期内维持旅游业的能力需求，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Hurricanes disrupt community vitality when significant damage and destruction occurs to the built environment, particularly in remote, tourism-dependent coastal communities. Immediately following the impacts to homes, businesses, and infrastructure, tourism business owners and employees begin to make recovery decisions that affect the resilience of coastal communities. This Rapid Response Research (RAPID) project examines the factors and information networks that influence recovery decisions in Ocracoke and Hatteras, North Carolina, which were substantially impacted by Hurricane Dorian (September 2019). The remote nature of these communities makes them particularly vulnerable, as their transportation network is built upon a single state highway and/or ferry service that, when damaged, can affect access to recovery resources. Their dependence on tourism also makes them economically vulnerable as tourism benefits (e.g., jobs, tax revenues) rely on transportation access and lodging availability. Similar disaster-related impacts may become increasingly common in other coastal tourism-dependent communities due to potential increases in storm intensity and frequency in conjunction with sea-level rise. By identifying the factors and networks that influence early recovery decisions, this project contributes to NSF’s mission to advance national welfare and promote the progress of science. Specifically, this study increases the understanding of individual disaster recovery decisions within the tourism sector, which cumulatively influence a community’s ability to reestablish its built environment and economic base. This study also identifies the communication pathways that enable more informed near-term recovery decisions, which can be activated in future disaster response, tourism crisis management, and community resiliency building efforts.The goal of our project is to compare the factors and information networks that influence information processing and recovery decision making in two remote post-disaster tourism-dependent communities. This project has four objectives: (1) documenting various factors that influence tourism stakeholders’ recovery decisions; (2) identifying information networks accessed by tourism stakeholders to inform recovery decisions; (3) evaluating the extent to which recovery information activated through those networks is processed; and (4) understanding decision making pathways that influence risk perceptions and intended recovery decisions. These objectives will be accomplished by integrating the Risk Information Seeking and Processing (RISP) model with Social Network Analysis (SNA) in the analysis of data generated during in-depth interviews with tourism business owners, employees, and community leaders, as well as state emergency response officials. Data will be thematically analyzed for factors and RISP model predictors that influence recovery decisions, and information networks will be mapped using SNA to identify influential pathways for recovery decision making. Additionally, community-level data will be compared to identify if, and how, varying infrastructure damage and access to recovery resources impact decision making, which will enhance the transferability of our findings. Collecting these data in a near-term post-disaster context is critical, as the state of a tourism destination (e.g., availability of recovery funds, others’ recovery decisions) impacts the availability of future decision options. The findings will reveal the factors and information pathways that influence disaster recovery decisions in a synergistic examination of business and household recovery decisions. More specifically, the findings will identify the ways in which information processing of current and prior experiences, perceptions of workforce capacity and recovery of the built environment, network support availability, and anticipated tourism demand influence recovery decision making, as well as expose capacity needs to maintain the tourism industry in near-term, post-disaster contexts.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.