SizePairs: Achieving Stable and Balanced Temporal Treemaps using Hierarchical Size-based Pairing

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Chang Han, Anyi Li, Jaemin Jo, Bongshin Lee, Oliver Deussen, Yunhai Wang

 View presentation:2022-10-19T14:24:00ZGMT-0600Change your timezone on the schedule page
2022-10-19T14:24:00Z

Exemplar figure, described by caption below — Comparing different temporal treemap methods using three time steps (t = 0, t = 10, and t = 20) of the WorldBankHIV data: (a) Hilbert treemap method; (b) Local moves method; (c) Greedy insertion treemaps. (d) SizePairs; and (e) SizePairs with local moves. To reflect the stability of each method, we assign colors to each rectangle at the first time step and then apply the color scheme to the other time steps. SizePairs is more stable and maintains better aspect ratios, and SizePairs in combination with local moves (e) has even better aspect ratios than SizePairs alone (d).

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Abstract

We present SizePairs, a new technique to create stable and balanced treemap layouts that visualize values changing over time in hierarchical data. To achieve an overall high-quality result across all time steps in terms of stability and aspect ratio, SizePairs employs a new hierarchical size-based pairing algorithm that recursively pairs two nodes that complement their size changes over time and have similar sizes. SizePairs maximizes the visual quality and stability by optimizing the splitting orientation of each internal node and flipping leaf nodes, if necessary. We also present a comprehensive comparison of SizePairs against the state-of-the-art treemaps developed for visualizing time-dependent data. SizePairs outperforms existing techniques in both visual quality and stability, while being faster than the local moves technique.