Uncertainty Treemaps

Sondag, Max; Meulemans, Wouter; Schulz, Christoph; Verbeek, Kevin; Weiskopf, Daniel; Speckmann, Bettina

doi:10.1109/pacificvis48177.2020.7614

Cited by 10 publications

(5 citation statements)

References 37 publications

(57 reference statements)

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“…The representation of uncertainty for hierarchical visualization is, in general, difficult and a topic of ongoing research [87]. All five articles in our survey do not use uncertainty indicators because the data is loaded in chunks that are precise for the current level of aggregation.…”

Section: Uncertainty Visualizationmentioning

confidence: 99%

A Survey on Progressive Visualization

Ulmer,

Angelini,

Fekete

et al. 2024

IEEE Trans. Visual. Comput. Graphics

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Fig. 1: Overview of our taxonomy with its four major categories: visualization, progressive processing, data domain, and visual update pattern. The visualization categories are based on previous taxonomies [47, 65, 85]. The progressive processing categories are an extension of the progressive visual analytics characterization [5], adding a new "Custom chunking" variant. The data domain categories cover the difference in visualization design if the end of the data or process is known or unknown. The fourth category is the visual update pattern which indicates how the visualizations are updated when new partial results are produced.Abstract-Currently, growing data sources and long-running algorithms impede user attention and interaction with visual analytics applications. Progressive visualization (PV) and visual analytics (PVA) alleviate this problem by allowing immediate feedback and interaction with large datasets and complex computations, avoiding waiting for complete results by using partial results improving with time. Yet, creating a progressive visualization requires more effort than a regular visualization but also opens up new possibilities, such as steering the computations towards more relevant parts of the data, thus saving computational resources. However, there is currently no comprehensive overview of the design space for progressive visualization systems. We surveyed the related work of PV and derived a new taxonomy for progressive visualizations by systematically categorizing all PV publications that included visualizations with progressive features. Progressive visualizations can be categorized by well-known visualization taxonomies, but we also found that progressive visualizations can be distinguished by the way they manage their data processing, data domain, and visual update. Furthermore, we identified key properties such as uncertainty, steering, visual stability, and real-time processing that are significantly different with progressive applications. We also collected evaluation methodologies reported by the publications and conclude with statistical findings, research gaps, and open challenges. A continuously updated visual browser of the survey data is available at visualsurvey.net/pva.

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Section: Uncertainty Visualizationmentioning

confidence: 99%

A Survey on Progressive Visualization

Ulmer,

Angelini,

Fekete

et al. 2024

IEEE Trans. Visual. Comput. Graphics

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“…Prior work on visualizing defined uncertainty and set membership in (a) Venn diagrams [41] and (b) node-link diagrams [39] as well as visualizing uncertainty and set size in (c) treemaps [36] and (d) with circle packing [14].…”

Section: Maceachren Et Al First Empirically Assessed the Intu-mentioning

confidence: 99%

“…A few attempts exist (see Figure 2) for the visualization of fuzzy sets [39], [41], where set membership has a defined uncertainty. For set attributes, we are aware of only two prior works, which represent set size with a defined uncertainty in set hierarchies using a treemap [36] and a circle packing visualization [14]. Visual representations of undefined uncertainty within sets have not been developed until now.…”

Section: Uncertainty In Set Visualizationmentioning

confidence: 99%

Visualizing Uncertainty in Sets

Tominski¹,

Behrisch²,

Bleisch³

et al. 2023

Preprint

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“…Grid layouts generally suffer from visual scalability problems for large numbers of members: representing each member on its own results in too many and too small tiles to be useful for analysis. Hierarchical techniques can significantly improve scalability, and various hierarchical visualization approaches have been proposed for geographical, hierarchical or multivariate data with different facets [SHS11,SDW09] (e.g., via treemaps [WD08,SMS∗20]). However, the data must have respective information associated with it, or it needs to be created prior to the presentation.…”

Section: Related Workmentioning

confidence: 99%

Optimizing Grid Layouts for Level‐of‐Detail Exploration of Large Data Collections

Frey

2022

Computer Graphics Forum

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This paper introduces an optimization approach for generating grid layouts from large data collections such that they are amenable to level‐of‐detail presentation and exploration. Classic (flat) grid layouts visually do not scale to large collections, yielding overwhelming numbers of tiny member representations. The proposed local search‐based progressive optimization scheme generates hierarchical grids: leaves correspond to one grid cell and represent one member, while inner nodes cover a quadratic range of cells and convey an aggregate of contained members. The scheme is solely based on pairwise distances and jointly optimizes for homogeneity within inner nodes and across grid neighbors. The generated grids allow to present and flexibly explore the whole data collection with arbitrary local granularity. Diverse use cases featuring large data collections exemplify the application: stock market predictions from a Black‐Scholes model, channel structures in soil from Markov chain Monte Carlo, and image collections with feature vectors from neural network classification models. The paper presents feedback by a domain scientist, compares against previous approaches, and demonstrates visual and computational scalability to a million members, surpassing classic grid layout techniques by orders of magnitude.

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Uncertainty Treemaps

Cited by 10 publications

References 37 publications

A Survey on Progressive Visualization

A Survey on Progressive Visualization

Visualizing Uncertainty in Sets

Optimizing Grid Layouts for Level‐of‐Detail Exploration of Large Data Collections

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