Mining and visualizing eye movement data

Eye movement data has a spatio-temporal nature which makes the design of suitable visualization techniques a challenging task. Moreover, eye movement data is typically recorded by tracking the eyes of various study participants in order to achieve significant results about applied visual task solution strategies. If we have to deal with vast amounts of eye movement data, a data preprocessing in form of data mining is useful since it can be applied to compute a set of rules. Those aggregate, filter, and hence reduce the original data to derive patterns in it. The generated rule sets are still large enough to serve as input data for a visual analytics system. In this paper we describe a visual analysis model for eye movement data combining data mining and visualization with the goal to get an impression about point-of-interest (POI) and area-of-interest (AOI) correlations in eye movement data on different levels of spatial and temporal granularities. Those correlations can support a data analyst to derive visual patterns that can be mapped to data patterns, i.e., visual scanning strategies with different probabilities of a group of eye tracked people. We show the usefulness of our data mining and visualization system by applying it to datasets recorded in a formerly conducted eye tracking experiment investigating the readability of metro maps.