Visualization Atlases: Explaining and Exploring Complex Topics Through Data, Visualization, and Narration
This paper defines, analyzes, and discusses the emerging genre of visualization atlases. We currently witness an increase in web-based, data-driven initiatives that call themselves "atlases" while explaining complex, contemporary issues through data and visualizations: climate change, sustainability, AI, or cultural discoveries. To understand this emerging genre and inform their design, study, and authoring support, we conducted a systematic analysis of 33 visualization atlases and semi-structured interviews with eight visualization atlas creators. Based on our results, we contribute (1) a definition of a visualization atlas as a compendium of (web) pages aimed at explaining and supporting exploration of data about a dedicated topic through data, visualizations and narration. (2) a set of design patterns of 8 design dimensions, (3) insights into the atlas creation from interviews and (4) the definition of 5 visualization atlas genres. We found that visualization atlases are unique in the way they combine i) exploratory visualization, ii) narrative elements from data-driven storytelling and iii) structured navigation mechanisms. They target a wide range of audiences with different levels of domain knowledge, acting as tools for study, communication, and discovery. We conclude with a discussion of current design practices and emerging questions around the ethics and potential real-world impact of visualization atlases, aimed to inform the design and study of visualization atlases.
Authors
- Xinhuan Shu (ORCID: https://orcid.org/0000-0002-9736-4454)
- Benjamin Bach (ORCID: https://orcid.org/0000-0002-9201-7744)
- Uta Hinrichs (ORCID: https://orcid.org/0000-0001-7494-0941)
- J. J. Wang (ORCID: https://orcid.org/0009-0007-9313-5180)
Institutions
- Newcastle University (GB)
- University of Edinburgh (GB)
Publication Details
- Journal
- IEEE Transactions on Visualization and Computer Graphics
- Published
- 2024-09-20
- DOI
- https://doi.org/10.1109/tvcg.2024.3456311
- Citations
- 5
- Primary Topic
- Data Visualization and Analytics
- Type
- article
- Field-Weighted Citation Impact
- 1.25