This dissertation addresses the research challenge of developing efficient new methods for discovering useful patterns and knowledge in large volumes of electronically collected spatiotemporal activity data. I propose to analyze three types of such spatiotemporal activity data in a methodological framework that integrates spatial analysis, data mining, machine learning, and geovisualization techniques.
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- Partial requirement for: Ph.D., Arizona State University, 2012Note typethesis
- Includes bibliographical referencesNote typebibliography
- Field of study: Geography