English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Rectangular treemaps are often the method of choice to visualize largehierarchical datasets. Nowadays such datasets are available over time, hencethere is a need for (a) treemaps that can handle time-dependent data, and (b)corresponding quality criteria that cover both a treemap's visual quality andits stability over time. In recent years a wide variety of (stable) treemappingalgorithms has been proposed, with various advantages and limitations. We aimto provide insights to researchers and practitioners to allow them to make aninformed choice when selecting a treemapping algorithm for specificapplications and data. To this end, we perform an extensive quantitativeevaluation of rectangular treemaps for time-dependent data. As part of thisevaluation we propose a novel classification scheme for time-dependentdatasets. Specifically, we observe that the performance of treemappingalgorithms depends on the characteristics of the datasets used. We identifyfour potential representative features that characterize time-dependenthierarchical datasets and classify all datasets used in our experimentsaccordingly. We experimentally test the validity of this classification on morethan 2000 datasets, and analyze the relative performance of 14 state-of-the-artrectangular treemapping algorithms across varying features. Finally, wevisually summarize our results with respect to both visual quality andstability to aid users in making an informed choice among treemappingalgorithms. All datasets, metrics, and algorithms are openly available tofacilitate reuse and further comparative studies.

Quantitative Comparison of Time-Dependent Treemaps