RAPID: Collection of Perishable Data on Households Affected by Hurricane Sandy to Better Understand Variables Affecting Collective Post Disaster Housing Recovery

这项快速反应研究拨款（Rapid）将收集受飓风桑迪影响家庭的数据。飓风桑迪是美国历史上损失第二大的飓风，估计造成600亿美元的损失，113人死亡。数据收集和分析将在纽约州史坦顿岛和新泽西州大西洋城进行，因为这两个地区都涵盖了广泛的人口和社会经济属性，如年龄、种族和收入，并受到飓风桑迪的严重影响。收集的数据将包括家庭内部变量，如年龄、性别、种族、残疾、职业状况、收入、教育程度、保险范围、社会网络和心理健康，以及外部变量，如提供的临时住房、获得的补助金和贷款、承诺和收到的保险报销、关键基础设施的恢复、邻居的重建。家庭的恢复决策：重建、等待或搬迁是基于上述内部和外部变量的汇合，其中一些变量往往随着时间的推移而变化很大，因此在以后的日期很难收集，或者在家庭搬迁时几乎不可能收集。随着清理和恢复活动的开始和加速，建筑物损坏数据也可能在接下来的几周（如果不是几天）内丢失。此外，个人的心理状态和他们对未来的期望是极其脆弱的。因此，本研究旨在捕捉所有易逝性数据对家庭恢复决策的影响，并调查这些恢复决策最终将如何影响整个社区的恢复。数据收集将采用面对面访谈和电话访谈相结合的方式，收集农户的初始恢复决策。收集到的数据集将与受影响住宅的地理标记视频相关联，以反映家庭造成的损失成本。此外，将在大约6个月后进行一次后续电话调查，以比较这些家庭最初和最终的恢复决定。最近的灾难，包括飓风桑迪、飓风卡特里娜和乔普林龙卷风，以及许多其他灾害，揭示了受影响社区在恢复过程中迅速恢复基础设施、住宅物业和商业活动所面临的巨大挑战。预计未来极端事件将会增加，加上易受灾地区的人口不断增加，因此迫切需要深入了解灾后恢复过程，并采取更有效的战略来加强这一过程。虽然进行了许多研究以确定设计缺陷或检查某些措施和政策的综合效应，但很少（如果有的话）集中于模拟家庭如何在灾难后做出恢复决策，以及这些决策如何共同促进整个社区的恢复。由于家庭恢复是社区恢复的关键，因此通过了解不同家庭变量对其最终住房恢复决策的影响来弥合这一知识差距至关重要。这项研究的广泛影响将是双重的。首先，它将提供一个家庭及其相关变量的综合数据库，研究人员可以利用该数据库研究恢复动态的不同方面；其次，它可以作为当局的决策工具，通过优先考虑和优化他们在受影响地区的投资来增强整体社区恢复。

This Rapid Response Research Grant (RAPID) will collect data regarding households affected by hurricane Sandy, the second costliest hurricane in the history of the US with an estimated loss of $60 billion and 113 fatalities. The data collection and analysis will be performed in Staten Island, NY and Atlantic City, NJ as both cover a wide range of demographic and socioeconomic attributes such as age, race and income and were severely impacted by hurricane Sandy. The data to be collected will include households' internal variables such as age, gender, race, disabilities, job status, income, education, insurance coverage, social networks and psychological wellbeing as well as external variables including temporary housing provided, grants and loans received, insurance reimbursements promised and received, restoration of critical infrastructure, and reconstruction of the neighbors. Households' recovery decisions: reconstruct, wait, or relocate are based on a confluence of aforementioned internal and external variables among which some tend to vary greatly with time and as such would be either difficult to collect at the later date or almost impossible as households relocate. Building damage data can also be lost in the next few weeks, if not days, as cleanup and recovery activities start and accelerate. In addition, the psychological state of individuals and their expectation for the future are extremely perishable. Therefore this research is aimed at capturing the confluence of all perishable data on households' recovery decision and investigating how these recovery decisions will eventually affect the recovery of the whole community. The data collection will combine face-to-face and phone interviews to collect the initial recovery decision of the households. The collected dataset will be linked with geo-tagged videos of the affected residences to reflect households' imposed damage costs. Additionally a follow-up phone survey will be employed approximately 6 months after, to compare the initial and final recovery decisions of the households.Recent disasters including Hurricane Sandy, Hurricane Katrina, and Joplin Tornado coupled with many others, revealed huge challenges faced by affected communities to promptly restore infrastructures, residential properties and commercial activities during the recovery process. Anticipated increase in future extreme events coupled with growing population in disaster-prone regions has created an urgent need for deep understanding of the process of post-disaster recovery and more effective strategies to enhance it. While there are many studies conducted to identify design deficiencies or to examine aggregated effects of certain measures and policies, very few, if any, have focused on modeling how households make recovery decisions following a disaster, and how these decisions collectively contribute to the community-wide recovery. As household recovery is the key to community recovery, it is crucial to bridge this knowledge gap through understanding the effect of different households' variables on their final housing recovery decisions. The broader impacts of this research would be twofold. Firstly, it will provide a comprehensive database of households and their associated variables which can be utilized by researches to study the different aspect of recovery dynamics and secondly it can be used as a decision-making tool by authorities to enhance the overall community recovery by prioritizing and optimizing their investments in the affected areas.