Phase II I/UCRC Harvard: Center for Spatiotemporal Thinking, Computing and Applications (STCA)

II 期 I/UCRC 哈佛大学：时空思维、计算和应用中心 (STCA)

• 批准号: 1841403
• 负责人: Sankaran Subramanian
• 金额: $ 50万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-15 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1841403&HistoricalAwards=false
• 关键词: Phase; II; UCRC; Harvard; Center

Everything happens at some place, some time. Through collaboration among George Mason University, University of California at Santa Barbara, and Harvard University, this project builds national spatiotemporal capacity and advances education and research. This project aims to acquire superior knowledge of spatiotemporal principles, build computational infrastructure capable of processing big spatiotemporal data efficiently, develop insights on how to apply these principles and capabilities to solve real-world problems, and implement educational programs with state-of-the-art spatiotemporal curricula in order to understand our world better, to respond to regional and global emergencies in a timely way, and to prepare the next generation's workforce. As a center, the three universities will collaborate with industry, government agencies, and other organizations to form a leading national and international consortium for building a comprehensive infrastructure base for spatiotemporal thinking, computing, and application; conducting research to develop spatiotemporal thinking and its methodologies; developing new spatiotemporal technologies, solutions, datasets, tools, and software that will be integrated with existing and future industry products and services; building demonstrative applications for government agencies and organizations to improve the impact and reach of this work; and formulating new educational methodologies and materials to advance human knowledge for the public. The center will be a platform for academia, industry, and agencies to collaborate to achieve fundamental and discipline-wide results. Members from academia will get unique opportunities to interact with industry and agencies to understand real world problems and research needs. Industry members will be able to access the research results and turn them into future products and services to improve their competitiveness. Agencies will be able to apply the research results, incorporating them into their operations for better efficiency. The products, services, and operations will serve thousands to millions of end users, from the public and K-16 to professionals. Research results will be presented in international conferences or symposiums; published in Geographic Information System (GIS) journals or books; shared as code packages in GitHub (https://github.com/cga-harvard), posted on the center website (https://www.stcenter.net/), and circulated as center published newsletters, posters or flyers. Platforms developed by this project will be publicly accessible (as approved by the sponsoring board members).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.

一切都发生在某个时间、某个地点。通过乔治梅森大学、加州大学圣巴巴拉分校和哈佛大学之间的合作，该项目建设国家时空能力并推进教育和研究。该项目旨在获得时空原理的高级知识，构建能够有效处理大时空数据的计算基础设施，深入了解如何应用这些原理和能力来解决现实世界的问题，并实施具有最新水平的教育计划。艺术时空课程，以便更好地了解我们的世界，及时应对区域和全球紧急情况，并为下一代劳动力做好准备。作为中心，三所大学将与行业、政府机构和其他组织合作，组建一个领先的国内和国际联盟，建设时空思维、计算和应用的综合基础设施基地；进行研究以发展时空思维及其方法；开发新的时空技术、解决方案、数据集、工具和软件，将与现有和未来的行业产品和服务集成；为政府机构和组织构建示范应用程序，以提高这项工作的影响力和范围；制定新的教育方法和材料，以增进公众的人类知识。该中心将成为学术界、工业界和机构合作实现基础性和全学科成果的平台。来自学术界的成员将获得与行业和机构互动的独特机会，以了解现实世界的问题和研究需求。行业成员将能够获取研究成果并将其转化为未来的产品和服务，以提高竞争力。各机构将能够应用研究成果，将其纳入其运营中，以提高效率。这些产品、服务和运营将为数千至数百万最终用户（从公众、K-16 到专业人士）提供服务。研究成果将在国际会议或研讨会上发表；发表在地理信息系统 (GIS) 期刊或书籍上；在 GitHub (https://github.com/cga-harvard) 中作为代码包共享，在中心网站 (https://www.stcenter.net/) 上发布，并作为中心发布的时事通讯、海报或传单进行分发。该项目开发的平台将向公众开放（经赞助董事会成员批准）。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点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 spatial–temporal network analysis of patent transfers from U.S. universities to firms

美国大学到企业专利转移的时空网络分析

• DOI: 10.1007/s11192-020-03745-6
• 发表时间: 2020-10-29
• 期刊: Scientometrics
• 影响因子: 3.9
• 作者: Tao Hu;Tao Hu;Yin Zhang
• 通讯作者: Yin Zhang

A multiscale measure of spatial dependence based on a discrete Fourier transform

基于离散傅里叶变换的空间依赖性的多尺度测量

• DOI: 10.1080/13658816.2021.2017440
• 发表时间: 2021-12-21
• 期刊: International Journal of Geographical Information Science
• 影响因子: 5.7
• 作者: Hanchen Yu;S. Fotheringham
• 通讯作者: S. Fotheringham

Building an Open Resources Repository for Covid-19 Research

为 Covid-19 研究构建开放资源库

• DOI: 10.2139/ssrn.3587704
• 发表时间: 2020-04
• 期刊: SSRN Electronic Journal
• 作者: Hu, Tao;Guan, Weihe Wendy;Zhu, Xinyan;Shao, Yuanzheng;Liu, Lingbo;Du, Jing;Liu, Hongqiang;Zhou, Huan;Wang, Jialei;She, Bing;et al
• 通讯作者: et al

Taking the pulse of COVID-19: a spatiotemporal perspective

把握 COVID-19 的脉搏：时空视角

• DOI: 10.1080/17538947.2020.1809723
• 发表时间: 2020-10
• 期刊: International Journal of Digital Earth
• 影响因子: 5.1
• 作者: Yang, Chaowei;Sha, Dexuan;Liu, Qian;Li, Yun;Lan, Hai;Guan, Weihe Wendy;Hu, Tao;Li, Zhenlong;Zhang, Zhiran;Thompson, John Hoot;et al
• 通讯作者: et al

A scoping review of COVID-19 research adopting quantitative geographical methods in geography, urban studies, and planning: a text mining approach

在地理学、城市研究和规划中采用定量地理方法对 COVID-19 研究进行范围界定：文本挖掘方法

• DOI: 10.1080/19475683.2024.2304205
• 发表时间: 2024-01-02
• 期刊: Annals of GIS
• 影响因子: 5
• 作者: Junghwan Kim;Sampath Rapuri;Kevin Wang;Weihe Guan;Melinda Laituri
• 通讯作者: Melinda Laituri