Download Title: Space-Time Analytics and CyberGIS Speaker: Professor May

Title: Space-Time Analytics and CyberGIS
Speaker:
Professor May Yuan 袁玫 教授
(Director, Center for Spatial Analysis, College of
Atmospheric and Geographic Sciences, University
of Oklahoma)
Date/Time: 2013/12/06(Fri.), 2:00pm-3:30pm
Place: 地理系館 3F, R305 視聽教室
Abstract
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.