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||Nathaniel Henry*, University of Oxford, Measuring health inequities in geostatistics||15||3:05 PM|
|Presenter||Eun-Kyeong Kim*, Department of Geography & University Research Priority Program “Dynamics of Healthy Aging”, University of Zurich, Switzerland, Michelle Pasquale Fillekes, Mobility Cooperative, Rotkreuz, Switzerland, Christina Röcke, University Research Priority Program “Dynamics of Healthy Aging”, University of Zurich, Switzerland, Robert Weibel, Department of Geography & University Research Priority Program “Dynamics of Healthy Aging”, University of Zurich, Switzerland, Latent Factors of Location-Based Daily Mobility in Older Adults||15||3:20 PM|
|Presenter||Qiang Pu*, University at Buffalo, SUNY, Eun-hye Yoo, University at Buffalo, SUNY, Missing MAIAC AOD imputation with quantified uncertainty and its implications on AOD derived PM2.5 levels||15||3:35 PM|
|Presenter||Lindsey Smith*, University of Toronto, Michael Widener, University of Toronto, Bochu Liu, University of Toronto, Steven Farber, University of Toronto, Kristian Larsen, University of Toronto, Leia Minaker, University of Waterloo, Zachery Patterson, Concordia University, Jason Gilliland, The University of Western Ontario, Comparing individual and household food environments: what household food opportunities are missed when measuring access to food retail at the individual level?||15||3:50 PM|
|Discussant||Ying Song University of Minnesota - Minneapolis, MN||15||4:05 PM|
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