RAPID/Collaborative Research: Households' Immediate Protective Actions and Trade-Off Processes Between Property Security and Life Safety in Response to 2022 Hurricane Ian

快速/协作研究：应对 2022 年伊恩飓风时家庭的立即保护行动以及财产安全和生命安全之间的权衡过程

• 批准号: 2303578
• 负责人: Xilei Zhao
• 金额: $ 6万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-15 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2303578&HistoricalAwards=false
• 关键词: RAPID; Collaborative; Research; Households; Immediate

In recent years, increased climate change together with rising populations in coastal communities has exposed residents to escalating risks of destructive extreme weather events such as hurricanes. These threats are increasing the urgency of conducting studies on coastal households’ risk perceptions and behavioral responses that could improve policies to protect lives and property. Despite decades of progress, hurricane evacuation studies still lack an adequate understanding of households’ decisions about the trade-offs between property protection and life safety among alternative protective actions. Hurricane Ian was a Category 4 storm with a last-minute intensification and track-change that affected a wide range of communities with diverse socio-demographic characteristics. This hurricane presents a unique opportunity to study how households dealt with conflicting objectives in their protective action decision making and why they delayed evacuating or even refused to leave. This Grant for Rapid Response Research (RAPID) project enhances scientific knowledge of households’ emergency responses to hurricane threats by extending the Protective Action Decision Model (PADM) and gathering critical data in two coastal counties and one inland county in Florida. The outcomes promote teaching, training, and learning; increase the participation of underrepresented groups; and significantly improve emergency management and community resilience across the US coastal areas.This project addresses four current limitations in the evacuation literature: 1) risk communication challenges amid rapid changes in hurricane characteristics; 2) the low accuracy in predicting protective action decisions based on linear statistical approaches; 3) an inadequate understanding of households’ decisions in the context of buildings’ vulnerabilities to extreme environmental conditions (e.g., the actual strength of houses against hurricane-force wind speeds); and 4) ignoring tradeoffs among protective action alternatives during the evacuation decision-making process. To tackle these research challenges, this project gathers ephemeral data on housing damage and empirical data regarding households’ experiences and perceptions. The five tasks include 1) field investigation; 2) household survey; 3) comparison of respondents’ perceptions against risk levels as evaluated by experts; 4) comparison of conventional linear regression analyses and machine-learning-enhanced logistic regression models; and 5) development of a trade-off model of decision making among multiple protective actions. This project advances the state of knowledge about households’ protective action decision making and tradeoffs among protective action PA alternatives in response to a hurricane emergency.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.

近年来，气候变化加剧，加上沿海社区人口增加，使居民面临飓风等破坏性极端天气事件的风险不断增加。这些威胁增加了对沿海家庭的风险认知和行为反应进行研究的紧迫性，这些研究可以改善保护生命和财产的政策。飓风疏散研究虽然取得了几十年的进展，但仍然缺乏对家庭在财产保护和生命安全之间权衡各种替代保护行动的决定的充分了解。飓风伊恩是一场4级风暴，在最后一刻加剧并改变了轨迹，影响了具有不同社会人口特征的广泛社区。这场飓风提供了一个独特的机会，研究家庭如何处理相互冲突的目标，在他们的保护行动决策，以及为什么他们推迟疏散，甚至拒绝离开。该快速反应研究赠款（RAPID）项目通过扩展保护行动决策模型（PADM）和收集佛罗里达两个沿海县和一个内陆县的关键数据，增强了家庭对飓风威胁的紧急反应的科学知识。该项目的成果促进了教学、培训和学习，增加了代表性不足的群体的参与，并显著提高了美国沿海地区的应急管理和社区复原力。该项目解决了目前疏散文献中的四个局限性：1）飓风特征快速变化时的风险沟通挑战; 2）基于线性统计方法预测保护行动决策的准确性低; 3）对住户在建筑物易受极端环境条件影响的情况下的决定了解不足（例如，房屋抵抗飓风强度风速的实际强度）;以及4）在疏散决策过程中忽略了保护措施备选方案之间的权衡。为了应对这些研究挑战，该项目收集了关于住房损坏的短暂数据以及关于家庭经历和看法的经验数据。这五项任务包括：1）实地调查; 2）住户调查; 3）比较受访者对专家评估的风险水平的看法; 4）比较传统的线性回归分析和机器学习增强的逻辑回归模型;以及5）开发多种保护措施之间的决策权衡模型。该项目促进了对家庭的保护行动决策和保护行动PA替代品之间的权衡，以应对飓风紧急情况的知识状态。该奖项反映了NSF的法定使命，并已被认为是值得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的支持。

项目成果

Causality-informed Rapid Post-hurricane Building Damage Detection in Large Scale from InSAR Imagery

• DOI: 10.1145/3615884.3629422
• 发表时间: 2023-10
• 期刊: Proceedings of the 8th ACM SIGSPATIAL International Workshop on Security Response using GIS
• 作者: Chenguang Wang;Yepeng Liu;Xiaojian Zhang;Xuechun Li;Vladimir Paramygin;Arthriya Subgranon;Peter She