CyberGIS is defined as geographic information science and systems (GIS) based on advanced computing and cyberinfrastructure. Though geospatial big data have played important roles in many domains with significant societal impacts, geospatial data science remains to be established for advancing data-intensive geographic research and education in the era of big data and cyberGIS. At AAG 2019 annual meeting, the Symposium on Frontiers in Geospatial Data Science will be held to provide an exciting and timely forum for sharing recent progress and future trends on geospatial data science and related fields. A suite of paper and panel sessions will address cutting-edge advances of geospatial data science with a particular focus placed on the following themes: foundations, principles, and theories of geospatial data science; data-driven geography; artificial intelligence and data-intensive approaches to geographic problem solving; geographic knowledge discovery enabled by cyberGIS; education advances and challenges; and spatial cyberinfrastructure.
Geospatial data science represents an emerging interdisciplinary and transdisciplinary field intersecting among three broad knowledge domains: geospatial sciences and technologies, mathematical and statistical sciences, and cyberinfrastructure and computational sciences. The core of this intersection encompasses the synergies and interactions between big data and cyberGIS with geospatial principles guiding discovery and innovation
|Presenter||Shih-Lung Shaw*, University of Tennessee, GIScience Beyond Absolute Space: A Space-Place (Splatial) GIScience Framework||20||1:10 PM|
|Presenter||Shaowen Wang, Department of Geography and Geographic Information Science, University of Illinois at Urbana-Champaign, David Tarboton, Department of Civil and Environmental Engineering, Utah State University, Mike Hodgson, Department of Geography, University of South Carolina, Eric Shook, Department of Geography, Environment, and Society, University of Minnesota, Xingong Li*, Department of Geography & Atmospheric Science, University of Kansas, A Map Algebra Approach to Analyzing Spatiotemporal Data||20||1:30 PM|
|Presenter||Ying Song*, University of Minnesota - Minneapolis, MN, Yingling Fan, University of Minnesota - Twin Cities, Julian Wolfson, University of Minnesota - Twin Cities, Using smartphone-based activity survey to understand individual mobility and accessibility in urban environment across time: an object-oriented framework||20||1:50 PM|
|Presenter||Yi Qiang*, University of Hawaii - Manoa, Spatio-Temporal Data Mining and Analyses in a Multi-Scale Framework||20||2:10 PM|
|Discussant||Shaowen Wang University of Illinois at Urbana-Champaign||20||2:30 PM|
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