Monday, May 10, 2011
Song Gao, University of Wisconsin-Madison
To contain the COVID-19 spread, one of the nonpharmaceutical interventions is physical (social) distancing. An interactive web-based mapping platform, which provides up-to-date mobility and close contact information using large-scale anonymized mobile phone location data in the US, was developed and maintained by the GeoDS Lab at UW-Madison. Using the multiscale human mobility origin-to-destination (OD) flow data, a novel mobility-augmented epidemic model was further developed to help analyze the COVID-19 spread dynamics at multiple geographical scales (e.g., state, county, and neighborhood), inform public health policy, and deepen our understanding of human behavior under the unprecedented public health crisis.
Dr. Song Gao is an Assistant Professor in Geographic Information Science at the University of Wisconsin-Madison, where he leads the GeoDS Lab. He holds a Ph.D. degree at the University of California Santa Barbara. His main research interests include Place-Based GIS, Human Mobility, and GeoAI for Social Sensing. He is the author of 50+ publications with 3500+ Google Scholar citations in prominent journals and conferences. He is the principal investigator of multiple research grants from NSF, Wisconsin Alumni Research Foundation, Microsoft and geospatial industry partners. He currently serves as the Associate Editor of Annals of GIS, Editorial Board Member of Scientific Reports, PLOS ONE, Cartography and Geographic Information Science, the Academic Director of AAG Geographic Information Science and Systems Specialty Group, and the President-Elect of CPGIS. He was the recipient of the ‘Waldo Tobler Young Researcher Award’ in GIScience, among other awards.
Dr. Kathleen Stewart is Professor in the Department of Geographical Sciences and Director of the Center for Geospatial Information Science at the University of Maryland. She works in the area of geographic information science where she is interested in mobility and spatial access, often in a big geospatial data context and using approaches that lie in the expanding field of spatial data science. She investigates movement and mobility for a number of different application domains, for example, health and transportation where movement patterns and geospatial dynamics are key topics. She is also interested in modeling geospatial semantics including geospatial ontologies and their role for location-based applications. She serves as a member of the Mapping Science Committee of the National Academies of Sciences, Engineering and Medicine and is a member of the editorial board of The International Journal of Geographical Information Science, Transactions in GIS, Geographical Analysis, the Journal of Spatial Information Science, the International Journal of Geo-Information, and Geomatics.
Monday, April 26, 2021
Xun Shi, Dartmouth College
Most current modeling works of COVID-19 are based on the classic SIR model, typically at the population level and adopting top-down strategies. They are sensitive to modeling setting and parameter values that feature subjectivity. Their aggregate data also mask variation across people and space. An alternative approach is to model at the individual level and to resort to a bottom-up strategy, which takes advantage of increasingly available big data of individuals’ mobilities and high-performance computing capabilities. It anticipates general and high-resolution spatiotemporal patterns of the epidemic to emerge from the modeling process. We have been developing a bottom-up approach to epidemic modeling for years. The components of the approach include 1) disaggregating available human mobility data to obtain individual-level trajectory data, using a Markov Chain Monte Carlo (MCMC) process; 2) tracing contacts among individuals based on their daily activity trajectories; 3) building an Epidemic Forest model that represents transmission relationships among actual disease cases; and 4) predicting future epidemics based on the epidemic characteristics derived from the Epidemic Forest model, as well as data depicting intervention scenarios through agent-based modeling. The process has been preliminarily implemented within an ArcGIS environment and is being migrated to the CyberGISX platform. It has been applied to case studies of COVID-19 in China.
Xun Shi is a Professor of Geography at Dartmouth College. He has been highly active in the area of health-related geospatial research. His research covers disease mapping, disease-environment association detection, communicable disease modeling, healthcare accessibility assessment, and data infrastructure for health-GIS. He received funding support from NIH, NSF, CDC, and other sources. He published more than 70 research papers in international journals and developed ArcHealth, a software package serving spatial analytical functions particularly requested by health-related studies and practices. He served as the Chair of the Health and Medical Geography Specialty Group of the American Association of Geographers (AAG) during 2015-2016, and as an editor of AAG Annals during 2008-2018.
Sara L. McLafferty is a Professor at University of Illinois Urbana-Champaign. Her current research investigates place-based inequalities in health and well-being and access to health services for women, immigrants, and racial/ethnic minorities in the United States. Her ongoing work examines the impacts of increasing economic inequality and residential segregation on women's commuting times and modes and on maternal and infant health outcomes. She also uses and develops GIS and spatial analysis methods for examining health and social issues in cities and planning public health interventions. She is currently an Associate Editor of Health and Place and serves on the editorial boards of Geographical Analysis, and Spatial and Spatiotemporal Epidemiology.
Mon, March 15, 2021, 4:00 - 5:00 pm U.S. Central Time
Gary Langham Welcome Remarks
Shaowen Wang Geospatial Fellows Introduction
Xiang Chen Presentation
Mike Goodchild Discussant
Webinar details and registration: https://aag-geospatialfellows-series.secure-platform.com/a/solicitations/16/sessiongallery/245
Abstract: 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; https://gsi.cigi.illinois.edu/) 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).
Abstract: 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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