Towards Perceptual Optimization of the Visual Design of Scatterplots

Luana Micallef, Gregorio Palmas, Antti Oulasvirta, Tino Weinkauf

Research output: Contribution to journalArticleScientificpeer-review

70 Citations (Scopus)
440 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)1588-1599
JournalIEEE Transactions on Visualization and Computer Graphics
Issue number6
Publication statusPublished - 2017
MoE publication typeA1 Journal article-refereed
EventIEEE Pacific Visualization Symposium - Seoul, Korea, Republic of
Duration: 18 Apr 201721 Apr 2017
Conference number: 10


  • scatterplot
  • optimization
  • perception
  • crowdsourcing


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