If you are interested in joining this session, please email your PIN, title, abstract, and contact information to the following organizers: Michael R. Desjardins (UNC-Charlotte; email@example.com), Claudio Owusu (UNC-Charlotte; firstname.lastname@example.org), Alexander Hohl (University of Utah; email@example.com), and Eun-Kyeong Kim (University of Zurich, firstname.lastname@example.org). Please note that we will not accept submissions after the AAG deadline.
Advances in Computational Approaches for Geospatial Health Applications (Paper session)
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.
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
Discussants: Michael Widener (University of Toronto); Eric Delmelle (UNC-Charlotte); Fahui Wang (LSU); Xun Shi (Dartmouth)
|Presenter||Yu Lan*, University of North Carolina - Charlotte, Eric Delmelle, University of North Carolina - Charlotte, Wenwu Tang, University of North Carolina - Charlotte, A Web-based GIS Toolkit to Manage and Estimate, and Visualize Groundwater Contamination in Gaston County, North Carolina||15|
|Presenter||Colleen Reid*, University of Colorado, Boulder, Ellen Considine, University of Colorado Boulder, Melissa Maestas, University of Colorado Boulder, Gina Li, University of Colorado Boulder, Using machine learning to derive daily PM2.5 concentration estimates at fine spatial scales for the western US, 2008-2018||15|
|Presenter||Yoo Min Park*, Geography, Planning & Environment, East Carolina University, Assessing personal exposure to traffic-related air pollution using individual travel-activity diary data and an on-road source air dispersion model||15|
|Presenter||Amanda Kreuze*, Michigan State University, Sue Grady, Michigan State University, Maternal and Perinatal Health in the East-Central Region of Michigan: Environmental Investigation of Industrial Emissions on Adverse Birth Outcomes||15|
|Discussant||Eric Delmelle University of North Carolina at Charlotte||15|
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