Abstract
Rectangular treemaps visualize hierarchical numerical data by recursively partitioning an input rectangle into smaller rectangles whose areas match the data. Numerical data often has uncertainty associated with it. To visualize uncertainty in a rectangular treemap, we identify two conflicting key requirements: (i) to assess the data value of a node in the hierarchy, the area of its rectangle should directly match its data value, and (ii) to facilitate comparison between data and uncertainty, uncertainty should be encoded using the same visual variable as the data, that is, area. We present Uncertainty Treemaps, which meet both requirements simultaneously by introducing the concept of hierarchical uncertainty masks. First, we define a new cost function that measures the quality of Uncertainty Treemaps. Then, we show how to adapt existing treemapping algorithms to support uncertainty masks. Finally, we demonstrate the usefulness and quality of our technique through an expert review and a computational experiment on real-world datasets.
Original language | English |
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Title of host publication | 2020 IEEE Pacific Visualization Symposium, PacificVis 2020 - Proceedings |
Editors | Fabian Beck, Jinwook Seo, Chaoli Wang |
Publisher | IEEE Computer Society |
Pages | 111-120 |
Number of pages | 10 |
ISBN (Electronic) | 9781728156972 |
DOIs | |
Publication status | Published - Jun 2020 |
Event | 13th IEEE Pacific Visualization Symposium, PacificVis 2020 - Tianjin, China Duration: 14 Apr 2020 → 17 Apr 2020 |
Conference
Conference | 13th IEEE Pacific Visualization Symposium, PacificVis 2020 |
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Country/Territory | China |
City | Tianjin |
Period | 14/04/20 → 17/04/20 |
Keywords
- Human-centered computing
- Treemaps
- Visualization
- Visualization techniques