Enhanced Star Glyphs for Multiple-Source Data Analysis

The analysis of large sums of data can be extremely difficult to perform if the data is not presented graphically. As a result, many graphing techniques have been developed, such as scatter plots, histograms. Generally, the main purpose of graphically displaying data is to do one of two things: First, to find the general average of where most of the data lies. Second, to find the outliers, the data points that are most distant from the others. Our visualization will attempt to find both by using a multitude of common graphing techniques to expand upon the traditional star glyph and create a new way of graphing data. These techniques include clustering, using color as identifiers, and 3D graphing capabilities to present more data that would not be possible of being shown in a two dimensional environment. We apply our techniques to compare several air traffic trajectory predictors currently being analyzed by the U.S. Federal Aviation Administration.