RAPID: Improving Predictions of Evacuation Decisions

RAPID：改进疏散决策的预测

• 批准号: 1849598
• 负责人: Susan Weller
• 金额: $ 1.97万
• 依托单位: University of Texas Medical Branch at Galveston
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-15 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1849598&HistoricalAwards=false
• 关键词: RAPID; Improving; Predictions; Evacuation; Decisions

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.

该项目将使用人类学方法来改善对飓风疏散决定的测量和理解，并最终改善对疏散的预测。人口调查试图主要从撤离者和未撤离者的社会人口特征的角度来理解撤离。过去20年来收集的关于疏散估计的数据表明，疏散区约有三分之一的居民未能疏散。尽管如此，没有一套明确或标准化的疏散原理，因此很难在研究和地区之间进行概括。该项目将探索一套评估疏散决策的新方法是否可以在不同地区和风暴中复制，以便更好地预测疏散决策。调查结果将分发给探索和管理灾难管理和恢复的原因，后果和复杂性的组织。该RAPID奖项支持收集有关佛罗里达飓风伊尔玛强制疏散的时间敏感数据。为了得到可靠的报告，关于疏散的动机和信念，访谈必须进行的背景下，受访者可以形成适当的联想认知状态相关的人为事件所必需的自由列表练习。这也需要在另一场风暴发生之前进行。研究人员将使用人类学的访谈技术来收集疏散/不疏散的原因。将在较高和较低风暴风险区（20对）与邻居对（一人撤离，一人未撤离）进行访谈，以了解撤离或不撤离的原因。邻居匹配对于控制可能影响疏散的外部因素（例如财富或距水的距离）非常重要。最近的研究表明，自由列表面试比标准的开放式问题更有成效。研究人员之前的工作，成功地使用自由列表，以获得一套全面的疏散/非疏散的理由飓风艾克，在加尔维斯顿，得克萨斯州。本项目收集的数据将提高这些方法和模型在疏散环境中的鲁棒性、可靠性和可复制性。如果数据是可复制的，该项目将构成一个方法论的创新，特别是人类学，并为灾害相关的科学更普遍。这个奖项反映了NSF的法定使命，并已被认为是值得支持的评估使用基金会的知识价值和更广泛的影响审查标准。