Visualization of Large, Time-Dependent, Abstract Data with Integrated Spherical and Parallel Coordinates

Abstract

Parallel coordinates is one of the most popular and widely used visualization techniques for large, high dimensional data. Often, data attributes are visualized on individual axes with polylines joining them. However, some data attributes are more naturally represented with a spherical coordinate system. We present a novel coupling of parallel coordinates with spherical coordinates, enabling the visualization of vector and multi-dimensional data. The spherical plot is integrated as if it is an axis in the parallel coordinate visualization. This hybrid visualization benefits from enhanced visual perception, representing vector data in a more natural spatial domain and also reducing the number of parallel axes within the parallel coordinates plot. This raises several challenges which we discuss and provide solutions to, such as, visual clutter caused by over plotting and the computational complexity of visualizing large abstract, time-dependent data. We demonstrate the results of our work-in-progress visualization technique using biological animal tracking data of a large, multi-dimensional, time-dependent nature, consisting of tri-axial accelerometry samples as well as several additional attributes. In order to understand marine wildlife behavior, the acceleration vector is reconstructed in spherical coordinates and visualized alongside with the other data attributes to enable exploration, analysis and presentation of marine wildlife behavior.