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Space-Time Analytics and CyberGIS

Friday, December 06, 2013, 14:00 - 15:30
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Location 地理系3樓視廳教室
講者: Professor May Yuan 袁玫 教授 (Director, Center for Spatial Analysis, College of Atmospheric and Geographic Sciences, University of Oklahoma)
演講大綱:

Data analytics is a popular buzzword in business and data science, and like many buzzwords, there is no authoritative definition. This talk considers that data analytics, in contrast to data mining, seeks actionable insights from data. It goes beyond blindly searching for interesting patterns as in data mining; data analytics emphasizes a scientifically-inclined process of problem definition and inference for data-driven decision making. In cyberinfrastructure, Data as a Service (DaaS) and Analytics as a Service (AaaS) providers innovate ways to aggregate, process and manage wide range of data sources with a wide range of analytics functions for the consolidated data. Accordingly, Space-Time Analytics in this talk centers on the spatial and temporal dimensions of data in addressing problems that rely upon inferences across space and time to derive actionable insights and data-driven decisions. CyberGIS integrates cyberinfrastructure, GIS, and spatial analysis to provide application-driven and user-centric functions. The discussion on Space-Time Analytics and CyberGIS is intended to explore new approaches that center on problem definitions and inference processes for data-driven actionable insights in the context of cyberGIS. The emphasis on problem definitions and inference processes signifies the importance of representation and construct to handle essential concepts in the problem of interest as well as the importance of methodology with sound logic and statistical and computational methods. These ideas will be discussed and illustrated by cases from three research projects: comparison of temperature estimates from general circulation models (GCMs), spatial narrative modeling of historical events, and patterns of life from GPS trajectory analysis. These projects use web resources, build new representation and constructs for the defined problems, and develop methodology with space-time analytics tools to address the problems. The GPS project is being developed on the web, and the other two projects are in the transition to web GIS applications.

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