Geography and Geographic Information Science

Back to Listing

Dr. May Yuan - Space-Time Analytics in CyberGIS

Event Type
Cyberinfrastructure and Geospatial Information Lab [CIGI] Geography and Geographic Information Science [GGIS] 
NCSA Building, Room 1040
May 3, 2013   11:00 am - 12:00 pm  
Dr. May Yuan, University of Oklahoma
Free and open to the public
Julie Carlson
(217) 244-9315

Data analytics is a popular buzzword in business and data science, and like many buzzwords, there is no authoritative denition. 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 with data mining; data analytics emphasizes a scientically-inclined process of problem denition 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 denitions and inference processes for data-driven actionable

insights in the context of CyberGIS. The emphasis on problem denitions and inference processes signies 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 dened

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 transitioning to web GIS applications.

link for robots only