Collaborative Research: RAPID: Building a Spatiotemporal Platform for Rapid Response to COVID-19

合作研究：RAPID：构建快速响应 COVID-19 的时空平台

• 批准号: 2027521
• 负责人: Chaowei Yang
• 金额: $ 10万
• 依托单位: George Mason University
• 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: 2020-06-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027521&HistoricalAwards=false
• 关键词: Collaborative; Research; RAPID; Building; Spatiotemporal

The Spatiotemporal Innovation IUCRC develops novel spatiotemporal analytical tools to enable applications of national and global significance. In response to the COVID-19 crisis, Harvard University and George Mason University, university sites within this IUCRC, propose this collaborative project to collect and share COVID related data in near real time, conduct spatiotemporal analytics, and mine socioeconomic and environmental knowledge to facilitate decision support systems in response to the pandemic. This project will build a unique cloud-based platform composed of a data collection subsystem for collecting global, high quality COVID-19-related data; spatiotemporal analytics tools for analyzing the disease evolution and socioeconomic patterns; and, modeling tools for assessing medical supplies and logistics. Through web access services, the platform will provide capabilities for easy access to the data collected as well as access to the developed spatiotemporal analytical and modeling tools. Such capabilities will facilitate quick production of data-driven decision support systems for community preparedness. This project has secured participation of 50+ international researchers in developing the proposed platform. These researchers will help collect and validate data, analyze how policies influence the outbreaks, how the Earth environment is impacted, and how to balance reopening of the economy and controlling the spreading of the disease in the U.S. based on experiences from Asia and Europe. Over 200 undergraduate volunteers, including many from underrepresented groups, are already involved in this project through Harvard’s Coronavirus Visualization Team efforts. Data, information, and knowledge accumulated in this project have been, and will continue to be, archived long term in a comprehensive gateway (covid-19.stcenter.net). Such data include spatiotemporal distribution of confirmed cases, relevant social, economic and natural information from different resources, such as authoritative reports, news releases, Earth observation, and social media. Software and tools developed are posted on GitHub for open access. Sustained online collaboration is being conducted to produce replicable research using spatiotemporal analyses to mine patterns and relations between COVID-19 and social and natural factors for community response and preparedness.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.

时空创新IUCRC开发新的时空分析工具，使国家和全球意义的应用。 为应对COVID-19危机，该IUCRC内的大学所在地哈佛大学和乔治梅森大学提出了这一合作项目，以近真实的时间收集和共享COVID相关数据，进行时空分析，并挖掘社会经济和环境知识，以促进应对疫情的决策支持系统。 该项目将建立一个独特的基于云的平台，包括用于收集全球高质量COVID-19相关数据的数据收集子系统;用于分析疾病演变和社会经济模式的时空分析工具;以及用于评估医疗用品和物流的建模工具。 通过网络访问服务，该平台将提供方便获取所收集数据以及获取已开发的时空分析和建模工具的能力。 这种能力将有助于迅速建立数据驱动的社区备灾决策支持系统。该项目已确保50多名国际研究人员参与开发拟议的平台。 这些研究人员将帮助收集和验证数据，分析政策如何影响疫情，地球环境如何受到影响，以及如何根据亚洲和欧洲的经验平衡经济重新开放和控制疾病在美国的传播。超过200名本科生志愿者，包括许多来自代表性不足的群体，已经通过哈佛的冠状病毒可视化团队的努力参与了这个项目。该项目中积累的数据、信息和知识已经并将继续在一个综合网关（covid-19.stcenter.net）中长期存档。这些数据包括确诊病例的时空分布，来自不同来源的相关社会、经济和自然信息，如权威报告、新闻稿、地球观测和社交媒体。开发的软件和工具发布在GitHub上以供开放访问。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。

项目成果

A spatiotemporal data collection of viral cases for COVID-19 rapid response

• DOI: 10.1080/20964471.2020.1844934
• 发表时间: 2021-01-01
• 期刊: BIG EARTH DATA
• 影响因子: 4
• 作者: Sha, Dexuan;Liu, Yi;Yang, Chaowei
• 通讯作者: Yang, Chaowei

Spatiotemporal changes in global nitrogen dioxide emission due to COVID-19 mitigation policies

• DOI: 10.1016/j.scitotenv.2021.146027
• 发表时间: 2021-02-22
• 期刊: The Science of the Total Environment
• 作者: Liu Q;Malarvizhi AS;Liu W;Xu H;Harris JT;Yang J;Duffy DQ;Little MM;Sha D;Lan H;Yang C
• 通讯作者: Yang C

Spatiotemporal Patterns and Driving Factors on Crime Changing During Black Lives Matter Protests

• DOI: 10.3390/ijgi9110640
• 发表时间: 2020-10
• 期刊: ISPRS Int. J. Geo Inf.
• 作者: Zhiran Zhang;D. Sha;Beidi Dong;S. Ruan;A. Qiu;Yun Li;Jiping Liu;C. Yang

A State-Level Socioeconomic Data Collection of the United States for COVID-19 Research

美国用于 COVID-19 研究的州级社会经济数据收集

• DOI: 10.3390/data5040118
• 发表时间: 2020
• 期刊: Data
• 影响因子: 2.6
• 作者: Sha, Dexuan;Malarvizhi, Anusha Srirenganathan;Liu, Qian;Tian, Yifei;Zhou, You;Ruan, Shiyang;Dong, Rui;Carte, Kyla;Lan, Hai;Wang, Zifu
• 通讯作者: Wang, Zifu

An Environmental Data Collection for COVID-19 Pandemic Research

• DOI: 10.3390/data5030068
• 发表时间: 2020-09-01
• 期刊: DATA
• 影响因子: 2.6
• 作者: Liu, Qian;Liu, Wei;Yang, Chaowei
• 通讯作者: Yang, Chaowei