Abstract
Gaze data visualization constitutes one of the most critical processes during eye-tracking analysis. Considering that modern devices are able to collect gaze data in extremely high frequencies, the visualization of the collected aggregated gaze data is quite challenging. In the present study, contiguous irregular cartograms are used as a method to visualize eye-tracking data captured by several observers during the observation of a visual stimulus. The followed approach utilizes a statistical grayscale heatmap as the main input and, hence, it is independent of the total number of the recorded raw gaze data. Indicative examples, based on different parameters/conditions and heatmap grid sizes, are provided in order to highlight their influence on the final image of the produced visualization. Moreover, two analysis metrics, referred to as center displacement (CD) and area change (AC), are proposed and implemented in order to quantify the geometric changes (in both position and area) that accompany the topological transformation of the initial heatmap grids, as well as to deliver specific guidelines for the execution of the used algorithm. The provided visualizations are generated using open-source software in a geographic information system.
Highlights
The process of recording and analyzing eye-tracking data constitutes one of the most popular techniques for the examination of both perceptual and cognitive aspects related to human visual behavior
The present study aims to describe the application of the method of contiguous irregular cartograms as a technique for aggregated gazed data visualization
The aggregated gaze data were visualized as continuous irregular cartograms considering the different combinations of selected parameters/conditions reported in the methodology described above
Summary
The process of recording and analyzing eye-tracking data constitutes one of the most popular techniques for the examination of both perceptual and cognitive aspects related to human visual behavior. The importance of eye-tracking methods is direct and clear in several studies and applied domains related (but not limited) to behavioral analysis, artificial intelligence and human–computer interaction (HCI). Several recent studies have presented the wide range and influence of eye-tracking applications in different disciplines, such as medical education [1], diagnostic interpretation [2], multimedia learning [3], software engineering [4], computer programming [5], aviation [6], mathematics education [7], cartography [8], behavioral economics and finance [9], marketing [10], and tourism [11]. An eye-tracking data protocol consists of the spatiotemporal coordinates collected during the observation of a visual stimulus in two (2D) or three (3D) dimensional space
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