Towards Perceptual Optimization of the Visual Design of Scatterplots

TLDR

Designing a good scatterplot is challenging for non‑experts because they must choose many parameters such as marker size, opacity, aspect ratio, color, and rendering order. This study aims to automatically set scatterplot parameters using perceptual models and quality metrics to improve visual quality. The authors construct a cost function that models visual system aspects and use it in an optimizer to search for optimal scatterplot designs tailored to specific analysis tasks, pre‑calibrated for correlation estimation, class separation, and outlier detection. The optimizer produced designs matching the speed and success of human‑designed presets, and in case studies it adapted designs to data to reveal patterns without user intervention.

Abstract

Designing a good scatterplot can be difficult for non-experts in visualization, because they need to decide on 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 set such parameters automatically for enhanced 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 for 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's dataset and task objectives (e.g., "reliable linear correlation estimation is more important than class separation"). 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 level of speed and success comparable to that of those using human-designed presets (e.g., in R or MATLAB). Case studies demonstrate that the approach can adapt a design to the data, to reveal patterns without user intervention.