A Data-Centric Perspective on Tree Visualizations
Authors
Manling Yang (Tufts University), Alexandra Scott (Tufts University), Chris Ahn (Tufts University), Daniel Jakab (Tufts University), Susie S.Y. Li (Tufts University), Mingwei S.G. Li (Tufts University), Remco Chang (Tufts University)
Presentation
- Session
- Lost in Dimensions
- Time
- Thursday, Nov 12, 15:54 – 16:03 (US/Eastern) · session 15:00 – 16:30
- Location
- Hall Essex north
Keywords
Tree Visualization, Visualization Theory
Abstract
Tree visualization (TreeVis) techniques span diverse designs. Existing taxonomies organize them by visual characteristics such as layout dimensionality, edge representation, and node alignment. However, this visual-centric perspective can obscure structural similarities and make it difficult to determine whether differences arise from data structures or visual encodings. We investigate TreeVis techniques from a data-centric perspective grounded in Prepared Tables, the final data state prior to visual encoding. Using TreeVis.net, we curate 133 two-dimensional techniques and characterize each by the object records and attribute roles required before encoding. Our analysis shows that the corpus is more concentrated at the prepared-data level than a visual reading would suggest. The techniques collapse to a small set of recurring object combinations and schemas. Many techniques across TreeVis representation categories share the same schema, suggesting that much of the apparent diversity of TreeVis designs lies in visual representation rather than fundamentally different pre-encoding data requirements. Prepared Table schemas therefore support reasoning about structural equivalence, sufficiency, and difference across TreeVis designs.
For Practitioners
This paper may interest visualization practitioners and tool designers working with hierarchical data. They can use the Prepared Table perspective to compare tree visualization designs and assess whether different designs require different data preparation.