Skip to main content

Evaluating graphical manipulations in automatically laid out LineSets

Tranquille, Dominique, Stapleton, Gem, Burton, Jim, Rodgers, Peter (2019) Evaluating graphical manipulations in automatically laid out LineSets. Behaviour & Information Technology, . ISSN 0144-929X. E-ISSN 1362-3001. (doi:10.1080/0144929X.2019.1690578) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided)

PDF - Author's Accepted Manuscript
Restricted to Repository staff only until 25 November 2020.
Contact us about this Publication Download (1MB)
[img]
Official URL
http://dx.doi.org/10.1080/0144929X.2019.1690578

Abstract

This paper presents an empirical study to determine whether alterations to graphical features (colour and size) of automatically generated LineSets improve task performance. LineSets are used to visualise sets and networks. The increasingly common nature of such data suggests that having effective visualisations is important. Unlike many approaches to set and network visualisation, which often use concave or convex shapes to represent sets alongside graphs, LineSets use lines overlaid on a graph. LineSets have been shown to be advantageous over shape-based approaches. However, the graphical properties of LineSets have not been fully explored. Our results suggest that automatically drawn LineSets can be significantly improved for certain tasks through the considered use of colour alongside size variations applied to their graphical elements. In particular, we show that perceptually distinguishable colours, lines of varying width, and nodes of varying diameter lead to improved task performance in automatically laid-out LineSets.

Item Type: Article
DOI/Identification number: 10.1080/0144929X.2019.1690578
Uncontrolled keywords: Set visualisation, LineSets, graphical properties
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.9.H85 Human computer interaction
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Depositing User: Peter Rodgers
Date Deposited: 26 Nov 2019 21:14 UTC
Last Modified: 27 Nov 2019 11:48 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/79002 (The current URI for this page, for reference purposes)
Stapleton, Gem: https://orcid.org/0000-0002-6567-6752
Rodgers, Peter: https://orcid.org/0000-0002-4100-3596
  • Depositors only (login required):

Downloads

Downloads per month over past year