Rectangular treemaps are often the method of choice for visualizing large hierarchical datasets. Nowadays such datasets are available over time, and hence there is a need for (a) treemapping algorithms that can handle time-dependent data, and (b) corresponding quality criteria and evaluations . In recent years a wide variety of treemapping algorithms and corresponding quality criteria have been proposed, each with their own advantages and limitations , which are often hard to judge and compare . We aim to provide insights to allow researchers and practitioners to  make an informed choice when selecting a treemapping algorithm for their specific application and their data. For this , we perform an extensive quantitative evaluation of rectangular treemapping algorithms for time-dependent data. As part of this evaluation we propose a novel classification scheme for time-dependent tree datasets. We approximate the infinite problem space of all such datasets by a low-dimensional feature space spanned by measurable characteristics (features) of the problem instances. We propose four such features to characterize time-dependent hierarchical datasets and classify all datasets used in our experiments accordingly. Moreover, we introduce a new method to measure the stability of time-dependent treemaps which explicitly incorporates the change in the input data. We analyze and visually summarize the results with respect to both visual quality and stability  . All datasets, metrics, and algorithms are openly available to facilitate reuse and further comparative studies.
Rectangular treemaps are often the method of choice to visualize large hierarchical datasets. Nowadays such datasets are available over time, hence there 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 and its stability over time . In recent years a wide variety of (stable) treemapping algorithms has been proposed, with various advantages and limitations . We aim to provide insights to researchers and practitioners to allow them to make an informed choice when selecting a treemapping algorithm for specific applications and data. To this end , we perform an extensive quantitative evaluation of rectangular treemaps for time-dependent data. As part of this evaluation we propose a novel classification scheme for time-dependent datasets. Specifically, we observe that the performance of treemapping algorithms depends on the characteristics of the datasets used. We identify four potential representative features that characterize time-dependent hierarchical datasets and classify all datasets used in our experiments accordingly. We experimentally test the validity of this classification on more than 2000 datasets, and analyze the relative performance of 14 state-of-the-art rectangular treemapping algorithms across varying features. Finally, we visually summarize our results with respect to both visual quality and stability to aid users in making an informed choice among treemapping algorithms . All datasets, metrics, and algorithms are openly available to facilitate reuse and further comparative studies.
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for visualizing
to visualize
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and
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treemapping algorithms
treemaps
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and evaluations
that cover both a treemap's visual quality and its stability over time
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treemapping algorithms and corresponding quality criteria have
(stable) treemapping algorithms has
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each with their own
with various
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, which are often hard to judge and compare
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allow
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allow them to
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their specific application and their data. For this
specific applications and data. To this end
R
treemapping algorithms
treemaps
R
tree datasets. We approximate the infinite problem space of all such datasets by a low-dimensional feature space spanned by measurable characteristics (features) of the problem instances. We propose four such features to
datasets. Specifically, we observe that the performance of treemapping algorithms depends on the characteristics of the datasets used. We identify four potential representative features that
R
Moreover, we introduce a new method to measure the stability of time-dependent treemaps which explicitly incorporates the change in the input data. We analyze and visually summarize the
We experimentally test the validity of this classification on more than 2000 datasets, and analyze the relative performance of 14 state-of-the-art rectangular treemapping algorithms across varying features. Finally, we visually summarize our
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to aid users in making an informed choice among treemapping algorithms