Leveraging Crowdsourced Data to Assess Spatiotemporal Patterns of Resilience in Diverse Gulf Coast Communities Impacted by Natural Hazards

利用众包数据评估受自然灾害影响的墨西哥湾沿岸不同社区的复原力时空模式

• 批准号: 2053588
• 负责人: Michelle Hummel
• 金额: $ 39.62万
• 依托单位: University of Texas at Arlington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-11-01 至 2024-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2053588&HistoricalAwards=false
• 关键词: Leveraging; Crowdsourced; Data; Assess; Spatiotemporal

The severity and cost of flood events continue to increase in the US, often with disproportionate impacts on vulnerable populations who may have higher sensitivity to the negative effects of flooding and lower capacity to adapt. Understanding what makes communities vulnerable or resilient to flooding is critical to developing mitigation actions that can reduce the negative effects of future hazards. However, existing frameworks for assessing resilience often fall short, as they do not address important dynamic and highly localized factors that influence peoples’ ability to cope with, adapt to, and recover from natural hazards. This Disaster Resilience Research Grants (DRRG) project will develop a bottom-up, community-driven framework for local-level resilience assessment by generating and utilizing high-resolution crowdsourced datasets and leveraging local knowledge and experiences to examine how the factors contributing to resilience (i.e., exposure, sensitivity, and adaptive capacity) vary over space and time. Findings will have implications for more effective resilience building. As part of the project, crowdsourcing efforts using Streetwyze, a community-driven mapping platform, will increase the awareness of flooding and its daily impacts in communities and will encourage diverse voices to participate in the collection of data to support local resilience planning efforts.This project will use a mixed-methods, sociotechnical approach to examine how crowdsourced datasets can be leveraged to (1) improve the spatiotemporal characterization of factors that influence community resilience to flood disasters, (2) develop new metrics that account for the dynamic social and physical nature of resilience, and (3) encourage more equitable capacity-building to reduce the impacts of future floods and enhance disaster resilience across diverse populations. The project focuses on flood hazards in coastal Mississippi and engages with diverse community partners from flood-prone areas in the cities of Gulfport and Biloxi. Two novel crowdsourcing technologies that include passively collected mobility data and actively generated qualitative and imagery data will be used to characterize fine-scale spatial and temporal patterns of exposure, sensitivity, and adaptive capacity. Surveys will evaluate how community members use the crowdsourced data and will assess the role of demographic and socioeconomic factors in influencing participation in the crowdsourcing effort. Geospatial, statistical, and machine learning models will be developed and applied to integrate the crowdsourced datasets with conventional sensor, satellite, and survey data. Model outputs will be used to develop novel measurement approaches that improve assessments of the factors contributing to community resilience.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.

在美国，洪水事件的严重程度和成本继续增加，往往对弱势群体造成不成比例的影响，他们可能对洪水的负面影响更敏感，适应能力更低。了解是什么使社区易受洪水影响或具有抵御能力，对于制定缓解行动以减少未来灾害的负面影响至关重要。然而，评估抗灾能力的现有框架往往不足，因为它们没有处理影响人们科普、适应自然灾害和从自然灾害中恢复的能力的重要动态和高度本地化的因素。这个抗灾能力研究赠款（DRRG）项目将通过生成和利用高分辨率众包数据集并利用当地知识和经验来研究如何影响抗灾能力的因素（即，暴露、敏感性和适应能力）随空间和时间而变化。研究结果将对更有效的复原力建设产生影响。作为该项目的一部分，利用社区驱动的地图平台Streetwyze开展众包工作，将提高人们对洪水及其对社区日常影响的认识，并鼓励不同的声音参与数据收集，以支持当地的抗灾规划工作。社会技术方法，以审查如何利用众包数据集（1）改善影响社区抵御洪水灾害能力的因素的时空特征，（2）制定新的衡量标准，说明复原力的动态社会和物理性质，（3）鼓励更公平的能力建设，以减少未来洪水的影响，提高不同人群的抗灾能力。该项目的重点是密西西比沿海地区的洪水灾害，并与来自格尔夫波特和比洛西两个城市洪水易发地区的各种社区合作伙伴进行合作。两种新的众包技术，包括被动收集的移动数据和主动生成的定性和图像数据将被用来表征曝光，敏感性和适应能力的精细尺度的空间和时间模式。调查将评价社区成员如何使用众包数据，并将评估人口和社会经济因素在影响参与众包努力方面的作用。将开发和应用地理空间、统计和机器学习模型，将众包数据集与传统传感器、卫星和调查数据整合。模型输出将用于开发新的测量方法，以改善对社区恢复力因素的评估。该奖项反映了NSF的法定使命，并被认为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来支持。