Santos, D.; Freitas, A.; Lima, R.; Santos, C.G.; Meiguins, B. Investigating the Impact of Different Partial Overlap Levels on the Perception of Visual Variables for Categorical Data. Appl. Sci.2023, 13, 9268.
Santos, D.; Freitas, A.; Lima, R.; Santos, C.G.; Meiguins, B. Investigating the Impact of Different Partial Overlap Levels on the Perception of Visual Variables for Categorical Data. Appl. Sci. 2023, 13, 9268.
Santos, D.; Freitas, A.; Lima, R.; Santos, C.G.; Meiguins, B. Investigating the Impact of Different Partial Overlap Levels on the Perception of Visual Variables for Categorical Data. Appl. Sci.2023, 13, 9268.
Santos, D.; Freitas, A.; Lima, R.; Santos, C.G.; Meiguins, B. Investigating the Impact of Different Partial Overlap Levels on the Perception of Visual Variables for Categorical Data. Appl. Sci. 2023, 13, 9268.
Abstract
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 to read 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 generates random scenarios for a unique visual pattern target to perform location tasks. Each scenario presented the visual items in a grid layout with 160 elements (10 x16), each visual variable had from 3 to 5 distinct values encoded, and the partial overlap percentage applied, represented by a gray square in the center o 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 result analysis indicates that the Hue, Lightness, and Shape visual variables are robust to high percentages of occlusion and gradual increase in encoded visual values. The Text visual variable show promising results for accuracy, and resolution time was a bit higher than 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. At last, the Orientation and Saturation visual variables got the highest error and worst perfomance rates during the tests.
Keywords
Information Visualization; Visual Variables; Evaluation; Occlusion; Overlap; Visual Perception
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
