The ongoing COVID-19 pandemic has put the US and the world into an extremely difficult situation. Complex and massive geospatial data has been rapidly collected for the fight against the COVID-19 grand challenge across the globe. Harnessing such geospatial data requires developing and integrating cutting-edge geospatial software capabilities, building interdisciplinary and transdisciplinary collaborations, and ensuring that research findings are readily accessible and reproducible. The National Science Foundation (NSF) has funded a project to conceptualize a Geospatial Software Institute (GSI) for establishing a long-term hub of excellence in geospatial software infrastructure that can serve diverse research and education communities. The GSI conceptualization project has selected sixteen Geospatial Fellows to collaboratively advance COVID-19 research and education through reproducible geospatial science. This webinar series will feature the work of the Geospatial Fellows focused on the following four themes: (1) making geospatial research computationally reproducible; (2) developing novel geospatial analysis and modeling capabilities; (3) assessing health disparities; and (4) understanding COVID-19 impacts. This talk will briefly introduce the GSI conceptualization project and the Geospatial Fellows program for launching the webinar series.

Shaowen Wang is a Professor and Head of the Department of Geography and Geographic Information Science; Richard and Margaret Romano Professorial Scholar; and an Affiliate Professor of the Department of Computer Science, Department of Urban and Regional Planning, and School of Information Sciences at the University of Illinois at Urbana-Champaign (UIUC). He has served as Founding Director of the CyberGIS Center for Advanced Digital and Spatial Studies at UIUC since 2013. He served as Associate Director of the National Center for Supercomputing Applications (NCSA) for CyberGIS from 2010 to 2017 and Lead of NCSA’s Earth and Environment Theme from 2014 to 2017. His research has been actively supported by a number of U.S. government agencies (e.g., CDC, DOE, EPA, NASA, NIH, NSF, USDA, and USGS) and industry. He has served as the principal investigator of several multi-institution projects sponsored by the National Science Foundation (NSF) for establishing the interdisciplinary field of cyberGIS and advancing related scientific problem solving in various domains (e.g., agriculture, bioenergy, emergency management, geography and spatial sciences, geosciences, and public health).

In the early development of COVID-19, large-scale preventive measures, such as border control and air travel restrictions, were implemented to slow international and domestic transmissions. When these measures were in full effect, new cases of infection would be primarily induced by community spread, such as human interactions within and between neighboring cities and towns, which is generally known as the meso-scale. Existing studies of COVID-19 using mathematical models are unable to accommodate the need for meso-scale modeling, because of the unavailability of COVID-19 data at this scale and the different timings of local intervention policies. In this respect, we propose a meso-scale mathematical model of COVID-19, named the meso-scale Susceptible, Exposed, Infectious, Recovered (MSEIR) model, using town-level infection data in the state of Connecticut. We consider the spatial interaction in terms of the inter-town travel in the model. Based on the developed model, we evaluated how different strengths of social distancing policy enforcement may impact future epidemic curves based on two evaluative metrics: compliance and containment. The developed model and the simulation results have contributed to community-level assessment and better preparedness for COVID-19.

Xiang Chen is an Assistant Professor in the Department of Geography at the University of Connecticut, USA. He earned a Ph.D. in geography at The Ohio State University. His research interests are focused on GIScience and community health (e.g., obesity, COVID-19, and dengue fever). He employs GIS approaches (e.g., big data analytics, geovisualization, deep learning) and accessibility theories to unveil the socioeconomic and health inequalities within urban communities.

Michael F. Goodchild is Emeritus Professor of Geography at the University of California, Santa Barbara, where he also holds the title of Research Professor. He is also Distinguished Chair Professor at the Hong Kong Polytechnic University and Research Professor at Arizona State University, and holds many other affiliate, adjunct, and honorary positions at universities around the world. Until his retirement in June 2012 he was Jack and Laura Dangermond Professor of Geography, and Director of UCSB’s Center for Spatial Studies. He received his BA degree from Cambridge University in Physics in 1965 and his PhD in geography from McMaster University in 1969, and has received five honorary doctorates. He was elected member of the National Academy of Sciences and Foreign Member of the Royal Society of Canada in 2002, member of the American Academy of Arts and Sciences in 2006, and Foreign Member of the Royal Society and Corresponding Fellow of the British Academy in 2010; and in 2007 he received the Prix Vautrin Lud. He was editor of Geographical Analysis between 1987 and 1990 and editor of the Methods, Models, and Geographic Information Sciences section of the Annals of the Association of American Geographers from 2000 to 2006. He serves on the editorial boards of ten other journals and book series, and has published over 15 books and 500 articles. He was Chair of the National Research Council’s Mapping Science Committee from 1997 to 1999, and of the Advisory Committee on Social, Behavioral, and Economic Sciences of the National Science Foundation from 2008 to 2010. His research interests center on geographic information science, spatial analysis, and uncertainty in geographic data.

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