Description
In today's world, unprecedented amounts of data of individual mobile objects have become more available due to advances in location aware technologies and services. Studying the spatio-temporal patterns, processes, and behavior of mobile objects is an important issue for extracting useful information and knowledge about mobile phenomena. Potential applications across a wide range of fields include urban and transportation planning, Location-Based Services, and logistics. This research is designed to contribute to the existing state-of-the-art in tracking and modeling mobile objects, specifically targeting three challenges in investigating spatio-temporal patterns and processes; 1) a lack of space-time analysis tools; 2) a lack of studies about empirical data analysis and context awareness of mobile objects; and 3) a lack of studies about how to evaluate and test agent-based models of complex mobile phenomena. Three studies are proposed to investigate these challenges; the first study develops an integrated data analysis toolkit for exploration of spatio-temporal patterns and processes of mobile objects; the second study investigates two movement behaviors, 1) theoretical random walks and 2) human movements in urban space collected by GPS; and, the third study contributes to the research challenge of evaluating the form and fit of Agent-Based Models of human movement in urban space. The main contribution of this work is the conceptualization and implementation of a Geographic Knowledge Discovery approach for extracting high-level knowledge from low-level datasets about mobile objects. This allows better understanding of space-time patterns and processes of mobile objects by revealing their complex movement behaviors, interactions, and collective behaviors. In detail, this research proposes a novel analytical framework that integrates time geography, trajectory data mining, and 3D volume visualization. In addition, a toolkit that utilizes the framework is developed and used for investigating theoretical and empirical datasets about mobile objects. The results showed that the framework and the toolkit demonstrate a great capability to identify and visualize clusters of various movement behaviors in space and time.
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Details
Title
- Developing a cohesive space-time information framework for analyzing movement trajectories in real and simulated environments
Contributors
- Nara, Atsushi (Author)
- Torrens, Paul M. (Thesis advisor)
- Myint, Soe W (Committee member)
- Kuby, Michael (Committee member)
- Griffin, William A. (Committee member)
- Arizona State University (Publisher)
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2011
Subjects
- Geography
- Geographic Information Science and Geodesy
- Animal behavior
- Geocomputation
- Geosimulation
- Geovisualization
- Spatio-Temporal Analysis Toolkit
- Spatio-Temporal Behavior
- Trajectory Data Mining
- Spatial analysis (Statistics)
- Geodatabases
- Global Positioning System
- Automatic tracking--Mathematical models.
- Automatic tracking
Resource Type
Collections this item is in
Note
- thesisPartial requirement for: Ph.D., Arizona State University, 2011
- bibliographyIncludes bibliographical references (p. 305-332)
- Field of study: Geography
Citation and reuse
Statement of Responsibility
by Atsushi Nara