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Towards Perceptual Optimization of the Visual Design of Scatterplots
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).ORCID iD: 0000-0002-1498-9062
2017 (English)In: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506, Vol. 23, no 6, p. 1588-1599Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
IEEE Computer Society, 2017. Vol. 23, no 6, p. 1588-1599
Keywords [en]
Scatterplot, optimization, perception, crowdsourcing
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-207870DOI: 10.1109/TVCG.2017.2674978ISI: 000400527500003PubMedID: 28252407Scopus ID: 2-s2.0-85009453141OAI: oai:DiVA.org:kth-207870DiVA, id: diva2:1102704
Conference
IEEE Pacific Visualization Symposium (IEEE PacificVis), APR 18-21, 2017, Seoul Natl Univ, Seoul, SOUTH KOREA
Note

QC 20170530

Available from: 2017-05-30 Created: 2017-05-30 Last updated: 2018-01-13Bibliographically approved

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  • apa
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