The Creeping Disaster along the Coast: Built Environment, Coastal Communities and Population Vulnerability to Sea Level Rise

该项目通过将Zillow提供的详细空间住房数据与社会脆弱性，海平面上升风险和历史洪水的措施相结合，探索人类和建筑环境对海平面上升影响的脆弱性。这个综合数据集允许对建筑环境在缓慢移动，持续或蔓延的灾害背景下调解传统人类-自然系统相互作用的作用进行理论探索。根据最近受飓风影响的沿海社区的数据，创新的统计技术被用来了解脆弱性的驱动因素和自然灾害对社区复原力的潜在影响。该项目中创建的开放、免费的漏洞数据集将成为未来灾难准备、规划和灾后恢复的基础。这一科学研究的贡献，从而支持NSF的使命，以促进科学的进步和提高我们的国家福利。在这种情况下，其好处将是提高沿海灾害准备的见解，这将在未来的事件中挽救生命和经济损失。该项目推进了脆弱性分析的方法基础，以更好地描述沿海社区的人口和建筑环境。这些进步是由几项数据创新驱动的。使用来自Zillow数据的结构特定特征，将以精细的粒度和不同的时间点创建新的定居点数据层，以获得建筑环境脆弱性指数。将通过统计制图技术将定居点层与人口普查数据结合起来，以建立空间上精确的人口分布，并最终计算海平面上升区内精细的社会脆弱性订正措施。修订后的社会和建成环境脆弱性层次将在大面积和若干时间点上合并，以评估脆弱性综合措施的趋势。确定与海平面上升对脆弱地区的暴露和潜在影响有关的不同情景，将有助于了解沿海社区如何适应洪水事件，并确定特定的发展模式如何影响未来的脆弱性。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。

This project explores the vulnerability of humans and the built environment to the impacts of sea level rise by integrating detailed spatial housing data provided by Zillow with measures of social vulnerability, sea level rise risk, and historical flooding. This integrated dataset allows the theoretical exploration of the role of the built environment in mediating traditional human-natural system interactions within the context of slow moving, persistent or creeping disasters. Based on data from coastal communities impacted by recent hurricanes, innovative statistical techniques are used to understand the drivers of vulnerability and potential impacts of natural disasters on community resilience. The open, freely available vulnerability datasets created in this project will be foundational for future disaster preparation, planning, and post-disaster recovery. This scientific research contribution thus supports NSF's mission to promote the progress of science and to advance our national welfare. In this case, the benefits will be insights to improve coastal disaster preparedness, which will save lives and economic losses in future events.This project advances the methodological foundation of vulnerability analysis to better characterize populations and built environments across coastal communities. These advances are driven by several data innovations. Using structure-specific characteristics derived from Zillow data, novel settlement data layers will be created at fine granularity and for different points in time to derive built-environment vulnerability indices. The settlement layers will be integrated with census population data through statistical mapping techniques to create spatially refined population distributions and ultimately compute revised measures of social vulnerability at fine granularity within sea-level rise zones. The revised social and built-up environment vulnerability layers will be combined over large areas and across several points in time to assess trends in integrated measures of vulnerability. Identifying different scenarios related to the exposure and potential impacts of sea-level rise on vulnerable places will provide insight on how coastal communities adapt to flood events and determine how particular development patterns may affect vulnerability in the future. Publications, software, and data derivatives generated during this project will be made available on the project website, GitHub and public data repositories.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.