Investigating the Impact of Different Partial Overlap Levels on the Perception of Visual Variables for Categorical Data

The overlap of visual items in data visualization techniques is a known problem aggravated by data volume and available visual space issues. Several methods have been applied to mitigate occlusion in data visualizations, such as random jitter, transparency, layout reconfiguration, focus+context techniques, etc. This paper aims to present a comparative study of the reading of visual variables values with partial overlap. The study focuses on categorical data representations varying the percentage limits of partial overlap and the number of distinct values for each visual variable: hue, lightness, saturation, shape, text, orientation, and texture. A computational application generated random scenarios for a unique visual pattern target to perform location tasks. Each scenario involved presentation of the visual items in a grid layout with 160 elements (10 × 16), each visual variable had from three to five distinct values encoded, and the partial overlap percentages applied, represented by a gray square in the center of each grid element, were 0% (control), 50%, 60%, and 70%. Similar to the preliminary tests, the tests conducted in this study involved 48 participants organized into four groups, with 126 tasks per participant, and the application captured the response and time for each task performed. The results analysis indicated that the hue, lightness, and shape visual variables were robust to high percentages of occlusion and gradual increase in encoded visual values. The text visual variable showed promising results for accuracy, and the resolution time was a little higher than for the last visual variables mentioned. In contrast, the texture visual variable presented lower accuracy to high levels of occlusion and more different visual encoding values. Finally, the orientation and saturation visual variables exhibited the highest error and worst perfomance rates during the tests.