An Empirical Guide for Visualization Consistency in Multiple Coordinated Views

Shaocong Tan, Chufan Lai, Xiaolong Luke Zhang, Xiaoru Yuan

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

Abstract

Visual analytic systems usually provide multiple coordinated views (MCVs) to support data analysis and exploration. Coordination in visual graphics plays an important role in facilitating comprehensive analytical tasks, such as data comparison and cognitive inference. However, individual views in MCVs are probably designed for a specific purpose based on a particular type of data, and insufficient consideration of the intricate relationships among views may lead to inconsistency in visual representation and user interaction across different views. To better understand the inconsistency issues in MCVs and their impacts on user behaviors, this paper reports a study on the analysis and classification of visualization inconsistency based on the reviews of interactive visualization designs and visual analytic systems, and the interviews with stakeholders. We find that inconsistencies are prevalent in MCVs and frequently lead to misleading or even incorrect results. We classify the discovered inconsistencies based on a coordination model of MCVs, and develop an empirical guide for systematic and efficient visualization consistency checking in the design, implementation, and evaluation stage.

Original languageEnglish (US)
Title of host publicationProceedings - 2023 IEEE 16th Pacific Visualization Symposium, PacificVis 2023
PublisherIEEE Computer Society
Pages31-40
Number of pages10
ISBN (Electronic)9798350321241
DOIs
StatePublished - 2023
Event16th IEEE Pacific Visualization Symposium, PacificVis 2023 - Seoul, Korea, Republic of
Duration: Apr 18 2023Apr 21 2023

Publication series

NameIEEE Pacific Visualization Symposium
Volume2023-April
ISSN (Print)2165-8765
ISSN (Electronic)2165-8773

Conference

Conference16th IEEE Pacific Visualization Symposium, PacificVis 2023
Country/TerritoryKorea, Republic of
CitySeoul
Period4/18/234/21/23

All Science Journal Classification (ASJC) codes

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

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