DataParticles: Block-based and Language-oriented Authoring of Animated Unit Visualization

要旨

Unit visualizations have been widely used in data storytelling within interactive articles and videos. However, authoring data stories that contain animated unit visualizations is challenging due to the tedious, time-consuming process of switching back and forth between writing a narrative and configuring the accompanying visualizations and animations. To streamline this process, we present DataParticles, a block-based story editor that leverages the latent connections between text, data, and visualizations to help creators flexibly prototype, explore, and iterate on a story narrative and its corresponding visualizations. To inform the design of DataParticles, we interviewed 6 domain experts and studied a dataset of 44 existing animated unit visualizations to identify the narrative patterns and congruence principles they employed. A user study with 9 experts showed that DataParticles can significantly simplify the process of authoring data stories with animated unit visualizations by encouraging exploration and supporting fast prototyping.

受賞
Best Paper
著者
Yining Cao
University of California, San Diego, San Diego, California, United States
Jane L. E
UCSD, San Diego, California, United States
Zhutian Chen
Harvard University, Boston, Massachusetts, United States
Haijun Xia
University of California, San Diego, San Diego, California, United States
論文URL

https://doi.org/10.1145/3544548.3581472

動画

会議: CHI 2023

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2023.acm.org/)

セッション: Visualization Grammars and Design

Room X11+X12
6 件の発表
2023-04-26 20:10:00
2023-04-26 21:35:00