Enhancing Static Charts with Data-driven Animations

Min Lu, Noa Fish, Shuaiqi Wang, Joel Lanir, Daniel Cohen-Or, Hui Huang

Research output: Contribution to journalArticlepeer-review

Abstract

Static visual attributes such as color and shape are used with great success in visual charts designed to be displayed in static, hard-copy form. However, nowadays digital displays become ubiquitous in the visualization of any form of data, lifting the confines of static presentations. In this work, we propose incorporating data-driven animations to bring static charts to life, with the purpose of encoding and emphasizing certain attributes of the data. We lay out a design space for data-driven animated effects and experiment with three versatile effects, marching ants, geometry deformation and gradual appearance. For each, we provide practical details regarding their mode of operation and extent of interaction with existing visual encodings. We examine the impact and effectiveness of our enhancements through an empirical user study to assess preference as well as gauge the influence of animated effects on human perception in terms of speed and accuracy of visual understanding.

Bibliographical note

Publisher Copyright:
IEEE

Keywords

  • Animated effects
  • Animation
  • Charts
  • Data visualization
  • Data-driven
  • Encoding
  • Image color analysis
  • Task analysis
  • Visual effects
  • Visual encoding
  • Visualization

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

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