Narrative Transitions in Data Videos

Junxiu Tang, Lingyun Yu, Tan Tang, Xinhuan Shu, Lu Ying, Yuhua Zhou, Peiran Ren, Yingcai Wu

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

11 Citations (Scopus)

Abstract

Transitions are widely used in data videos to seamlessly connect data-driven charts or connect visualizations and non-data-driven motion graphics. To inform the transition designs in data videos, we conduct a content analysis based on more than 3500 clips extracted from 284 data videos. We annotate visualization types and transition designs on these segments, and examine how these transitions help make connections between contexts. We propose a taxonomy of transitions in data videos, where two transition categories are defined in building fluent narratives by using visual variables.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE Visualization Conference, VIS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages151-155
Number of pages5
ISBN (Electronic)9781728180144
DOIs
Publication statusPublished - Oct 2020
Event2020 IEEE Visualization Conference, VIS 2020 - Virtual, Salt Lake City, United States
Duration: 25 Oct 202030 Oct 2020

Publication series

NameProceedings - 2020 IEEE Visualization Conference, VIS 2020

Conference

Conference2020 IEEE Visualization Conference, VIS 2020
Country/TerritoryUnited States
CityVirtual, Salt Lake City
Period25/10/2030/10/20

Keywords

  • Human-centered computing
  • Visualization
  • Visualization theory
  • concepts and paradigms

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