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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

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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    Date Created
    • 2012
    Resource Type
  • Text
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    • Partial requirement for: Ph.D., Arizona State University, 2012
      Note type
      thesis
    • Includes bibliographical references
      Note type
      bibliography
    • Field of study: Geography

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    by Xun Li

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