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Dylan Keon's Ph.D. 2012
Automated Web-based Analysis and Visualization of Spatiotemporal Data
Doctor of Philosophy (Ph.D.), Geography, Oregon State University, Fall 2012
Graduate Certificate in Geographic Information Science
Graduate committee: D. Wright, A. Nolin, C. Daly, M. Bailey, R. Colwell
College of Earth, Ocean, and Atmospheric Sciences, Oregon State Univ
Corvallis, OR 97331-5503
keon-at-nacse.org | NACSE
Most data are associated with a place, and many are also associated with a moment in time, a time interval, or another linked temporal component. Spatiotemporal data (i.e., data with elements of both space and time) can be used to assess movement or change over time in a particular location, an approach that is useful across many disciplines. However, spatiotemporal data structures can be quite complex, and the datasets very large. Although GIS software programs are capable of processing and analyzing spatial information, most contain no (or minimal) features for handling temporal information and have limited capability to deal with large, complex multidimensional spatiotemporal data. A related problem is how to best represent spatiotemporal data to support efficient processing, analysis, and visualization.
This dissertation examines the automated web-based analysis and visualization of spatiotemporal data in the context of three distinct projects. Although particular methods were developed to solve the stated problems for each project, in most cases those methods can be generalized to other disciplines or computational domains where similar problem sets exist. Chapter 2 (to submitted for publication in the journal Transactions in GIS) describes methods of dynamically selecting and preparing data for tsunami modeling, and processing the resulting time-series output data. Chapter 3 (to be submitted for publication in the International Journal of Geographical Information Science) describes simulation modeling of potential human evacuation response to a modeled tsunami inundation event, with methods for the web-based definition of a simulation scenario and animated, interactive visualization of the simulation output. Chapter 4 (to be submitted for publication in the journal Computers & Geosciences) describes methods for web-based calculation and visualization of climate grid statistics over varying spatial and temporal scales, including methods for fast automated server-side grid processing.