Temporal Summary Images: An Approach to Narrative Visualization via Interactive Annotation Generation and Placement

Chris Bryan, Kwan-Liu Ma, Jonathan Woodring

Research output: Contribution to journalArticle

23 Scopus citations

Abstract

Visualization is a powerful technique for analysis and communication of complex, multidimensional, and time-varying data. However, it can be difficult to manually synthesize a coherent narrative in a chart or graph due to the quantity of visualized attributes, a variety of salient features, and the awareness required to interpret points of interest (POls). We present Temporal Summary Images (TSIs) as an approach for both exploring this data and creating stories from it. As a visualization, a TSI is composed of three common components: (1) a temporal layout, (2) comic strip-style data snapshots, and (3) textual annotations. To augment user analysis and exploration, we have developed a number of interactive techniques that recommend relevant data features and design choices, including an automatic annotations workflow. As the analysis and visual design processes converge, the resultant image becomes appropriate for data storytelling. For validation, we use a prototype implementation for TSIs to conduct two case studies with large-scale, scientific simulation datasets.

Original languageEnglish (US)
Article number7539294
Pages (from-to)511-520
Number of pages10
JournalIEEE Transactions on Visualization and Computer Graphics
Volume23
Issue number1
DOIs
StatePublished - Jan 1 2017

Keywords

  • annotations
  • comic strip visualization
  • Narrative visualization
  • storytelling
  • time-varying data

ASJC Scopus subject areas

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

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