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Treemaps and the Visual Comparison of Hierarchical Multi-Attribute Data

Authors:

Kent Wittenburg,

Tommaso TurchiAuthors Info & Claims

AVI '16: Proceedings of the International Working Conference on Advanced Visual Interfaces

Pages 64 - 67

https://doi.org/10.1145/2909132.2909286

Published: 07 June 2016 Publication History

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Abstract

Treemaps have the desirable property of presenting overviews along with details of data and thus are of interest in visualizations of multi-attribute tabular data with attribute hierarchies. However, the original treemap algorithms and most subsequent variations are hampered in making parallel structures in a hierarchical data structure visually comparable. Structurally parallel elements are not aligned, making it difficult to compare them visually. We propose a method that allows for proportional and non-proportional subdivisions of subtrees while preserving visual alignment of parallel structures. We extend the framework so that other types of data visualizations can be placed within the graphical areas of a treemap to allow for the visual comparison of a broad collection of data types including temporal data.

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Cited By

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Zheng BSadlo F(2021)On the Visualization of Hierarchical Multivariate Data2021 IEEE 14th Pacific Visualization Symposium (PacificVis)10.1109/PacificVis52677.2021.00026(136-145)Online publication date: Apr-2021
https://doi.org/10.1109/PacificVis52677.2021.00026
Rodrigues MBrito LPaiva J(2019)A Computational System for Structural Visual Analysis of Labor Accident DataAdvances in Visual Computing10.1007/978-3-030-33723-0_36(443-454)Online publication date: 21-Oct-2019
https://doi.org/10.1007/978-3-030-33723-0_36
Wittenburg KLee TCatarci TNorman KMecella M(2018)Equal-height treemaps for multivariate dataProceedings of the 2018 International Conference on Advanced Visual Interfaces10.1145/3206505.3206591(1-3)Online publication date: 29-May-2018
https://dl.acm.org/doi/10.1145/3206505.3206591
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Published In

AVI '16: Proceedings of the International Working Conference on Advanced Visual Interfaces

June 2016

400 pages

ISBN:9781450341318

DOI:10.1145/2909132

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 07 June 2016

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AVI '16

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AVI '16: International Working Conference on Advanced Visual Interfaces

June 7 - 10, 2016

Bari, Italy

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AVI '16 Paper Acceptance Rate 20 of 96 submissions, 21%;

Overall Acceptance Rate 128 of 490 submissions, 26%

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Cited By

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Zheng BSadlo F(2021)On the Visualization of Hierarchical Multivariate Data2021 IEEE 14th Pacific Visualization Symposium (PacificVis)10.1109/PacificVis52677.2021.00026(136-145)Online publication date: Apr-2021
https://doi.org/10.1109/PacificVis52677.2021.00026
Rodrigues MBrito LPaiva J(2019)A Computational System for Structural Visual Analysis of Labor Accident DataAdvances in Visual Computing10.1007/978-3-030-33723-0_36(443-454)Online publication date: 21-Oct-2019
https://doi.org/10.1007/978-3-030-33723-0_36
Wittenburg KLee TCatarci TNorman KMecella M(2018)Equal-height treemaps for multivariate dataProceedings of the 2018 International Conference on Advanced Visual Interfaces10.1145/3206505.3206591(1-3)Online publication date: 29-May-2018
https://dl.acm.org/doi/10.1145/3206505.3206591
Liu WXiao SBrowne JYang MDow S(2018)ConsensUsACM Transactions on Social Computing10.1145/31596491:1(1-26)Online publication date: 18-Jan-2018
https://dl.acm.org/doi/10.1145/3159649

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Equal-height treemaps for multivariate data

Browsing Zoomable Treemaps: Structure-Aware Multi-Scale Navigation Techniques

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