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); Ying Song (University of Minnesota)
|Presenter||Xin Hong*, Kent State University, The Application of Deep Learning to Assess Neighborhood Walkability on UAV Images||15|
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