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<strong>Geospatial Fellows Webinar Series - Series Kick-off & 1st Webinar</strong></h1>
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<strong><span style="font-size:12pt; color:#000000">Topics:</span></strong> </p>
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<li><span style="font-size:12pt; color:#000000">Advancing COVID-19 Research and Education through Reproducible Geospatial Science</span></li><li><span style="font-size:12pt; color:#000000">Modeling COVID-19 Infections across Townships with Social Distancing Metrics</span></li></ul>
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<strong><span style="font-size:12pt; color:#000000">Speakers:</span></strong> </p>
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<td colspan="3" style="padding:0px!important"><img alt="Shaowen Wang, Xiang Chen, Michael F. Goodchild" width="800" border="0" align="middle" style="display:block; width:100%" src="https://emails.illinois.edu/files/100182571/processed_2003339.png?p=0310T131351"></td>
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<strong>Date:</strong> Monday, March 15, 2021 </p>
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<strong>Time:</strong> 4:00 - 5:00 pm U.S. Central Time </p>
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<a class="x_x_x_x_fallback-font" href="https://urldefense.com/v3/__https://aag-geospatialfellows-series.secure-platform.com/a/solicitations/16/sessiongallery/245__;!!DZ3fjg!rJS6QDjrkb78q6V0uJCTH62jIGwKXvBNbmhZ9UTiaS0UF2c65-Vg5yEEZbT6jljQ$" target="_blank" rel="noopener" style="font-family:"Open Sans",Arial,sans-serif,Arial,sans-serif; padding:10px 20px; border:1px solid rgb(14,64,95); border-top-left-radius:3px; border-top-right-radius:3px; border-bottom-right-radius:3px; border-bottom-left-radius:3px; font-size:16px; color:rgb(255,255,255); text-decoration:none; display:inline-block"><span style="color:#ffffff">Register
Now!</span></a> </td>
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The Geospatial Software Institute conceptualization project will hold a webinar series to showcase the findings and outcomes of the projects of the Geospatial Fellows for advancing COVID-19 research and education. These webinars will be scheduled on selected
Mondays between 4-5 pm Central Time through August. Check out the webinar series here:
<a href="https://urldefense.com/v3/__https://aag-geospatialfellows-series.secure-platform.com/__;!!DZ3fjg!rJS6QDjrkb78q6V0uJCTH62jIGwKXvBNbmhZ9UTiaS0UF2c65-Vg5yEEZZ8-IHNG$" target="_blank" rel="noopener" style="color:rgb(0,183,255)">
<span style="color:#00b7ff">https://aag-geospatialfellows-series.secure-platform.com/</span></a>
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The first webinar in this series will be held on March 15, 2021 at 4 pm CT. During this initial webinar,
<strong>Garry Langham</strong>, the Executive Director of the American Association of Geographers, will first provide a few words of welcome. Next,
<strong>Shaowen Wang</strong>, the Principal Investigator of the NSF-supported Geospatial Fellows program, will provide an introduction to the Geospatial Fellows Webinar Series. Then
<strong>Xiang Chen</strong> will showcase project findings thus far as a Geospatial Fellow for advancing COVID-19 research and education. Finally,
<strong>Michael F. Goodchild</strong>, Professor at Arizona State University and Emeritus Professor at University of California, Santa Barbara, will serve as a discussant for the webinar. We hope you will be able to join this webinar and hear about their interesting
work. </p>
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<span style="color:#f8fafc">Introduction to the Geospatial Fellows Webinar Series – Advancing COVID-19 Research and Education through Reproducible Geospatial Science</span></h2>
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<span style="color:#f8fafc; padding-left:20px"><strong>Shaowen Wang</strong></span><br>
<span style="color:#f8fafc; padding-left:20px">University of Illinois at Urbana-Champaign</span>
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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 <a href="https://gsi.cigi.illinois.edu/" target="_blank" rel="noopener" style="color:rgb(0,183,255)">
<span style="color:#00b7ff">Geospatial Software Institute</span></a> (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. </p>
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<td style="padding:0px!important"><img alt="Shaowen Wang" border="0" src="https://emails.illinois.edu/files/100182571/processed_2003540.png?p=0310T131351">
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<strong>Shaowen Wang</strong> 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).
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<span style="color:#f8fafc">Modeling COVID-19 Infections across Townships with Social Distancing Metrics</span></h2>
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<span style="color:#f8fafc; padding-left:20px"><strong>Xiang Chen</strong></span><br>
<span style="color:#f8fafc; padding-left:20px">University of Connecticut</span> </p>
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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.
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<td colspan="3" style="padding:0px!important"><img alt="Chen Figure 7" width="720" border="0" align="left" style="display:block; width:100%" src="https://emails.illinois.edu/files/100182571/processed_2003544.png?p=0310T131351"></td>
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<td style="padding:0px!important"><img alt="Xiang Chen" border="0" src="https://emails.illinois.edu/files/100182571/processed_2003552.png?p=0310T131351">
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<strong>Xiang Chen</strong> 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.
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<strong>Michael F. Goodchild</strong> 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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<td height="16" align="left" style="padding:0px!important; line-height:16px; height:16px; padding:0">
</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
</div>
</body>
</html>