Using GPS to Learn Significant Locations and Predict Movement Across Multiple Users. Daniel Ashbrook and Thad Starner College Of Computing Georgia Institute of Technology Personal and ubiquitous computing, 2003. Outline. 1. Introduction 2. Applications 3. Pilot Study Methodology
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Daniel Ashbrook and Thad Starner
College Of Computing
Georgia Institute of Technology
Personal and ubiquitous computing, 2003
3. Methodology- Movement Across Multiple UsersClustering places into locations
Figure 9: Picture (a) shows the results of the old place finding algorithm, while (b) shows the results of the new algorithm on the same data. Clusters are much more evident in (b), and the clusters match well with users' experiences. Each color (or shape) of dot in the pictures represents a different user.
Figure 11: increases in predictive power. An illustration of the data reduction that occurs when creating places and locations. Picture (a) shows the complete set of data collected in ZÄurich for one user, around 200,000 data points