A Study of Mental Maps in Immersive Network Visualization

Joseph Kotlarek, Oh Hyun Kwon, Kwan Liu Ma, Peter Eades, Andreas Kerren, Karsten Klein, Falk Schreiber

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

The visualization of a network influences the quality of the mental map that the viewer develops to understand the network. In this study, we investigate the effects of a 3D immersive visualization environment compared to a traditional 2D desktop environment on the comprehension of a network's structure. We compare the two visualization environments using three tasks - interpreting network structure, memorizing a set of nodes, and identifying the structural changes - commonly used for evaluating the quality of a mental map in network visualization. The results show that participants were able to interpret network structure more accurately when viewing the network in an immersive environment, particularly for larger networks. However, we found that 2D visualizations performed better than immersive visualization for tasks that required spatial memory.

Original languageEnglish (US)
Title of host publication2020 IEEE Pacific Visualization Symposium, PacificVis 2020 - Proceedings
EditorsFabian Beck, Jinwook Seo, Chaoli Wang
PublisherIEEE Computer Society
Pages1-10
Number of pages10
ISBN (Electronic)9781728156972
DOIs
StatePublished - Jun 2020
Event13th IEEE Pacific Visualization Symposium, PacificVis 2020 - Tianjin, China
Duration: Apr 14 2020Apr 17 2020

Publication series

NameIEEE Pacific Visualization Symposium
Volume2020-June
ISSN (Print)2165-8765
ISSN (Electronic)2165-8773

Conference

Conference13th IEEE Pacific Visualization Symposium, PacificVis 2020
CountryChina
CityTianjin
Period4/14/204/17/20

Keywords

  • Empirical studies in visualization
  • Graph drawings
  • Human-centered computing
  • Visualization
  • Visualization techniques

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Software

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