RAPID: Interdependent social vulnerability of COVID-19 and weather-related hazards in New York City

该研究将整合调查、社交媒体、建筑基础设施、能源需求和使用以及社会人口数据，并模拟潜在的与天气有关的极端事件，以研究纽约市对COVID-19和天气的相互依存的社会脆弱性。该研究将利用城市社会和基础设施系统的尖端模拟、建模和可视化，以了解人类行为如何响应“就地避难”政策而发生变化，这可能使潜在的相互依存和级联的社会脆弱性暴露于COVID和极端天气。主要的研究问题是：旨在降低纽约市COVID-19传播率的新指南将如何影响现有的健康、天气和经济危害脆弱性？主要成果将是增进知识，以了解COVID-19对病毒爆发的国家中心的影响，以及随着大流行开始与天气动态相互作用，并随着时间的推移导致相互依存的脆弱性，在许多月内需要解决方案的地方。随着COVID-19大流行在纽约市的迅速发展，迫切需要收集有关社会和经济影响的数据，并将这些数据与现有的地方、区域和国家数据集结合起来，预测未来几个月天气动态变化时COVID-19可能产生的相互依存影响。现在收集社会调查和社交媒体数据尤其重要，因为关于地点特定体验的观点和绿色空间作为关键基础设施的观点会随着时间的推移而变化。此外，社交媒体数据只能每周通过Twitter经济有效地访问一次，现在需要进行分析，及时了解政策影响，以便规划应对相互依存的covid - 19极端天气影响的应对和恢复战略。随着春季进入夏季，潜在的热浪和极端降雨事件将如何进一步影响脆弱人群，一种趋同的科学方法对于研究这一问题至关重要。该分析将研究在COVID-19安置政策期间，重叠的脆弱性如何与城市绿色空间的可用性和使用情况相互作用，以促进身心健康。例如，数据将包括每周与建筑物和绿地空间数据叠加的地理位置推文，以探索纽约市社交媒体活动的主要位置，以了解哪些公园和开放空间使用最多，哪些需要额外的资源来满足公众对身心健康的需求。这些数据将为纽约市的实时决策提供输入，以影响当前的应急响应、规划和政策，考虑到COVID-19的直接和间接影响、极端天气和相互依存的脆弱性。对于应对COVID-19的韧性应采取何种形式，特别是在考虑与社会脆弱性的其他驱动因素的相互作用时，系统理解仍然有限。因此，本研究的更广泛影响主要在于与当地从业人员（政府官员、非政府组织、社区组织）直接接触，提高他们进行综合规划和改进实时决策的能力，以减少社会脆弱性，并在2019冠状病毒病持续传播的新背景下规划应急响应，这些传播可能与天气相关的极端事件相结合。此外，研究将提供给目前在亚特兰大、凤凰城、圣胡安（PR）和UREx可持续发展研究网络中各个城市的NSF增长趋同研究（GCR）合作者。（SRN），为复制方法和发现提供机会。项目pi将以这种融合科学的方式培养跨学科的研究生和博士后学者，并提供一个重要的机制，使具有先进数据科学技能的学者收集重要的新兴数据，推进新的研究，以了解相互依存的COVID-19和天气相关影响对纽约市弱势群体的潜在影响。该奖项反映了美国国家科学基金会的法定使命，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。

The study will integrate survey, social media, building infrastructure, energy demand and use, and social- demographic data with simulations of potential emerging weather-related extremes to examine interdependent social vulnerability to COVID-19 and weather in New York City (NYC). The research will leverage cutting-edge simulations, modeling, and visualizations of urban social and infrastructure systems to understand how human behavior changes in response to shelter-in-place policies may expose potential interdependent and cascading social vulnerability to COVID and weather extremes. The primary research question is: How will existing vulnerabilities to health, weather, and economic hazards be affected by new guidelines designed to reduce COVID-19 transmission rates in NYC? The primary outcome will be to advance knowledge for understanding COVID-19 impacts in the national epicenter of the virus outbreak and where solutions will be needed for many months as the pandemic begins to interact with weather dynamics and drive interdependent vulnerability over time. As the COVID-19 pandemic evolves rapidly in NYC, there is an urgent need to collect data on social and economic impacts as they emerge, and join these with existing local, regional, and national datasets to anticipate potential interdependent impacts of COVID-19 as weather dynamics shift over coming months. Social survey and social media data are especially critical to collect now as perspectives on location specific experiences and perspective of green space as critical infrastructure can change over time. Additionally, social media data can only be accessed cost-effectively via Twitter in weekly intervals and analyses are needed now to understand policy impacts in time to plan responses and strategies for resilience to interdependent COVID-weather extremes impacts. A convergent scientific approach is critical for examining how vulnerable populations may be further impacted as spring turns to summer with potential heat waves and extreme rainfall events. The analysis will examine how overlapping vulnerabilities interact with availability and usage of urban green spaces for physical and mental health during COVID-19 shelter-in-place policies. For example, data will include weekly geo-located tweets overlaid with buildings and green space spatial data to explore dominant locations of social media activity in NYC to understand which parks and open space are most used, and which will require additional resources to meet public need for physical and mental health. This data will provide input to real-time decision-making in NYC to impact current emergency responses, planning and policies that consider direct and indirect impacts of COVID-19, weather extremes, and interdependent vulnerabilities. There remains limited systemic understanding of what forms resilience to COVID-19 should take, especially when considering interactions with additional drivers of social vulnerability. Thus, the broader impacts of this research lie primarily in direct engagement with local practitioners—governmental officials, non-governmental organizations, community organizations—to improve their ability to conduct integrative planning and improve real-time decision-making to reduce social vulnerability and plan emergency response in the novel context of ongoing COVID-19 transmission that may be combined with weather-related extremes. Further, research will be provided to current NSF Growing Convergence Research (GCR) collaborators in Atlanta, Phoenix, San Juan (PR), and across the cities in the UREx Sustainability Research Network. (SRN), seeding opportunities to replicate methods and findings. The project PIs will train interdisciplinary graduate students and postdoctoral scholars in this convergent science approach and provide an important mechanism to bring scholars with advanced data science skills to gather important emerging data and advance novel research to understand the potential of interdependent COVID-19 and weather-related impacts on vulnerable populations in NYC.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.