An evaluation of perceptually complementary views for multivariate data

Chunlei Chang, Tim Dwyer, Kim Marriott

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

1 Citation (Scopus)

Abstract

We evaluate the relative merits of three techniques for visualising multivariate data: parallel coordinates; scatterplot matrix; and a side-by-side, coordinated combination of these views. In particular, we report on: (1) the most effective visual encoding of multivariate data for each of the six common tasks considered; (2) common strategies that our participants used when the two views were combined based on eye-tracking data analysis; (3) the finding that these views are perceptually complementary in the sense that they both show the same information, but with different and complementary support for different types of analysis. For the combined view, our studies show that there is a perceptually complementary effect in terms of significantly improved accuracy for certain tasks, but that there is a small cost in terms of slightly longer completion time than the faster of the two techniques alone. Eye-movement data shows that for many tasks participants were able to swiftly switch their strategies after trying both in the training phase.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE Pacific Visualization Symposium, PacificVis 2018
Subtitle of host publication10–13 April 2018 Kobe, Japan
EditorsStefan Bruckner, Koji Koyamada, Bongshin Lee
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages195-204
Number of pages10
ISBN (Electronic)9781538614242
ISBN (Print)781538614259
DOIs
Publication statusPublished - 2018
EventIEEE Pacific Visualization Symposium 2018 - Kobe, Japan
Duration: 10 Apr 201813 Apr 2018
Conference number: 11th
http://research.cbs.chula.ac.th/pvis2019/home.aspx

Conference

ConferenceIEEE Pacific Visualization Symposium 2018
Abbreviated titlePacificVis 2018
CountryJapan
CityKobe
Period10/04/1813/04/18
Internet address

Keywords

  • High dimensional data visualisation
  • Information Visualisation
  • Multivariate data
  • Parallel coordinates
  • Perceptually Complementary views
  • scatterplot matrix
  • Side by side

Cite this

Chang, C., Dwyer, T., & Marriott, K. (2018). An evaluation of perceptually complementary views for multivariate data. In S. Bruckner, K. Koyamada, & B. Lee (Eds.), Proceedings - 2018 IEEE Pacific Visualization Symposium, PacificVis 2018: 10–13 April 2018 Kobe, Japan (pp. 195-204). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/PacificVis.2018.00033