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

RAPID：纽约市 COVID-19 和天气相关灾害相互依存的社会脆弱性

基本信息

批准号：
2029918
负责人：
P. Timon McPhearson
金额：
$ 19.75万
依托单位：
The New School
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-05-01 至 2021-04-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2029918&HistoricalAwards=false
关键词：
RAPID Interdependent social vulnerability COVID

项目摘要

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.

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，并模拟潜在的新出现的与天气相关的极端情况，以研究纽约市对COVID-19和天气的相互依赖的社会脆弱性。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庇护政策，可能会暴露出潜在的相互依赖和级联的社会脆弱性，以应对COVID和极端天气。主要的研究问题是：旨在降低纽约市COVID-19传播率的新指导方针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对全国病毒爆发中心的影响，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推动相互依赖的脆弱性，将需要数月的解决方案。随着COVID-19大流行在纽约市迅速演变，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，以预测COVID-19在未来几个月天气动态变化时可能产生的相互依赖的影响。社会调查和社会媒体数据是特别重要的，现在收集的角度对位置的具体经验和观点的绿色空间的关键基础设施可以随着时间的推移而改变。此外，社交媒体数据只能每周通过Twitter以具有成本效益的方式访问，现在需要进行分析，以及时了解政策影响，从而规划应对措施和战略，以应对相互依存的COVID-极端天气影响。一种趋同的科学方法对于研究随着春季转向夏季，潜在的热浪和极端降雨事件如何进一步影响脆弱人群至关重要。该分析将研究在COVID-19就地庇护政策期间，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，数据将包括覆盖有建筑物和绿色空间数据的每周地理定位推文，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，以及哪些需要额外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，这些政策考虑了COVID-19、极端天气和相互依赖的脆弱性的直接和间接影响。对于应对COVID-19应采取何种形式的复原力，系统性的理解仍然有限，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。因此，这项研究的更广泛影响主要在于与当地的宣传人员-政府官员，非政府组织，社区组织-直接接触，以提高他们进行综合规划和改善实时决策的能力，以减少社会脆弱性，并在新的COVID-19传播的新背景下规划应急响应，可能与天气相关的极端情况相结合。此外，研究将提供给目前的NSF增长融合研究（GCR）合作者在亚特兰大，凤凰城，圣胡安（公关），并在UREX可持续发展研究网络的城市。(SRN)，播种机会，以复制方法和研究结果。项目PI将在这种融合科学方法中培训跨学科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并推进新的研究，以了解相互依赖的COVID-19和天气的潜力。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值进行评估，被认为值得支持和更广泛的影响审查标准。