If you are interested in joining this session, please email your PIN, title, abstract, and contact information to the following organizers: Moongi Choi (University of Utah; email@example.com), Marynia Kolak (University of Chicago, firstname.lastname@example.org), Michael R. Desjardins (Johns Hopkins University; email@example.com), Alexander Hohl (University of Utah; firstname.lastname@example.org), and Eun-Kyeong Kim (University of Zurich, email@example.com). Please note that we will not accept submissions after the AAG deadline.
Advanced computational capabilities that emerged through continuous technological and methodological developments have transformed many scientific disciplines, including the domain of health and medical geography. Today, scientists tackle computational challenges that used to be virtually impossible to solve, because 1) our ability to collect and store health-related data has improved substantially, and 2) analytical methods for solving scientific problems can now be applied on a massive scale. Therefore, the spatiotemporal analysis and modelling of health-related issues has experienced and driven fundamental changes.
The goal of this series of sessions is to create a platform for presenting and stimulate discussion of the accomplishments and remaining challenges of applying and developing computational methods that improve our understanding of health and medical geography. The sessions are also part of the 2021 Geospatial Health Symposium.
Session 1: Environmental Health
Session 2: Accessibility
Session 3: Epidemiology
Session 4: Methods
Topics may include, but are not limited to:
-Disease mapping: spatial and/or spatiotemporal analysis and visualization;
-Disparities in health care accessibility;
-Stochastic methods for assessing the significance of observed disease patterns;
-Spatially explicit and space-time disease modeling (retrospective or prospective);
-The use of high-performance computing to analyze complex and massive spatiotemporal data for the discovery of relationships and patterns between health and environment;
-Data science approaches, including statistics, data mining, and machine learning, to address issues within health and medical geography.
Sponsor Groups: Spatial Analysis and Modeling Specialty Group, Cyberinfrastructure Specialty Group, Health and Medical Geography Specialty Group
|Presenter||Jeon-Young Kang*, Kongju National University, Sandy Wong, Florida State University, Jinwoo Park, Texas A&M University, Jinhyung Lee, Western University, Jared Aldstadt, State University of New York at Buffalo, Ji Hoon Park, Kongju National University, Measuring Spatial Accessibility to Healthcare Service for Elderly Populations in South Korean Cities||15||4:40 PM|
|Presenter||Ran Tao*, University of South Florida, Joni Downs Firat, University of South Florida, Theresa Beckie, University of South Florida, He Zhang, University of South Florida, Yuzhou Chen, University of South Florida, Elizabeth Dunn, University of South Florida, Optimizing the Allocation of COVID-19 Testing & Vaccine Resources in Florida||15||4:55 PM|
|Presenter||Qinyun Lin*, University of Chicago, Marynia Kolak, University of Chicago, Olina Liang, University of Chicago, Mapping historical and ongoing spatial inequities in access to medications for opioid use disorder: A 40-year longitudinal analysis||15||5:10 PM|
|Presenter||Yan Lin*, University of New Mexico, Department of Geography and Environmental Studies, UNM Center for the Advancement of Spatial Informatics Research and Education (ASPIRE), Christopher Lippitt, University of New Mexico, Department of Geography and Environmental Studies, UNM Center for the Advancement of Spatial Informatics Research and Education (ASPIRE), Daniel Beene, University of New Mexico, Community Environmental Health Program, College of Pharmacy, Street-source uncertainties in spatial accessibility and social equity: who is affected?||15||5:25 PM|
|Discussant||Xun Shi Dartmouth College||15||5:40 PM|
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