APRIL 18-21, 2017
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Towards Perceptual Optimization of the Visual Design of Scatterplots
Designing a good scatterplot can be difficult for non-visualization experts, because they need to decide many parameters, such as marker size and opacity, aspect ratio, color, and rendering order. This paper contributes to research exploring the use of perceptual models and quality metrics to automatically set such parameters to enhance the visual quality of a scatterplot. A key consideration in this paper is the construction of a cost function to capture several relevant aspects of the human visual system examining a scatterplot design in some data analysis task. We show how the cost function can be used in an optimizer to search for the optimal visual design for a user窶冱 dataset and task objectives (e.g., 窶腕eliable linear correlation estimation is more important than class separation窶). The approach is extensible to different analysis tasks. To test its performance in a realistic setting, we pre-calibrated it for correlation estimation, class separation, and outlier detection. The optimizer was able to produce designs that achieved a comparable level of speed and success with those using human-designed presets (e.g., R, Matlab). Case studies demonstrate that the approach can adapt a design to the data to reveal patterns without user intervention.