Useful approaches to exploratory analysis of gaze data: enhanced heatmaps, cluster maps, and transition maps

Exploratory analysis of gaze data requires methods that make it possible to process large amounts of data while minimizing human labor. The conventional approach in exploring gaze data is to construct heatmap visualizations. While simple and intuitive, conventional heatmaps do not clearly indicate differences between groups of viewers or give estimates for the repeatability (i.e., which parts of the heatmap would look similar if the data were collected again). We discuss difference maps and significance maps that answer to these needs. In addition we describe methods based on automatic clustering that allow us to achieve similar results with cluster observation maps and transition maps. As demonstrated with our example data, these methods are effective in highlighting the strongest differences between groups more effectively than conventional heatmaps.