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Discovering Intrinsic Points of Interest from Spatial Trajectory Data Sources
Piekenbrock, Matthew J.

2018, Master of Science (MS), Wright State University, Computer Science.
This paper presents a framework for intrinsic point of interest discovery from trajectory databases.
Intrinsic points of interest are regions of a geospatial area that are innately derivable by the spatial and temporal aspects of trajectory data. In contrast with other definitions of a point of interest, which often require a knowledge base or external location data, intrinsic points of interest are completely data-driven.
The framework unifies recent developments from the field of density level-set estimation, applied density-based clustering techniques, and common practices in spatial point pattern analysis, offering a more theoretically grounded framework towards how a point of interest may be defined.
Experiments are performed comparing the results across several modern approaches to POI discovery under thousands of parameter settings, and a marked improvement in fidelity by the proposed approach is shown in both synthetic and real world data sets.
Derek Doran, Ph.D. (Advisor)
Michael Raymer, Ph.D. (Committee Member)
William Romine, Ph.D. (Committee Member)
Krishnaprasad Thirunarayan, Ph.D. (Committee Member)
88 p.

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Piekenbrock, M. (2018). Discovering Intrinsic Points of Interest from Spatial Trajectory Data Sources. (Electronic Thesis or Dissertation). Retrieved from https://etd.ohiolink.edu/

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Piekenbrock, Matthew. "Discovering Intrinsic Points of Interest from Spatial Trajectory Data Sources." Electronic Thesis or Dissertation. Wright State University, 2018. OhioLINK Electronic Theses and Dissertations Center. 16 Aug 2018.

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Piekenbrock, Matthew "Discovering Intrinsic Points of Interest from Spatial Trajectory Data Sources." Electronic Thesis or Dissertation. Wright State University, 2018. https://etd.ohiolink.edu/

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Full text release has been delayed at the author's request until December 13, 2018