Masters Thesis Defense “Discovering Intrinsic Points of Interest from Spatial Trajectory Data Source” By Matthew Piekenbrock

Committee:  Drs. Derek Doran, Advisor, Michael Raymer, TK Prasad, and William Romine (Department of Biological Sciences)

ABSTRACT:

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 on both synthetic and real world data sets.