Uncertainty visualization: Fundamentals and recent developments

Hägele, David; Schulz, Christoph; Beschle, Cedric Aaron; Booth, Hannah; Butt, Miriam; Barth, Andrea; Deußen, Oliver; Weiskopf, Daniel

doi:10.1515/itit-2022-0033

Cited by 7 publications

(4 citation statements)

References 35 publications

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“…This allows us to model different flavors of uncertainty, such as missing data, measurement error, or data variability in ensembles. A discussion of different sources and levels of uncertainty is provided by Skeels et al [10] and Hägele et al [11].…”

Section: Related Workmentioning

confidence: 99%

Uncertainty-Aware Seasonal-Trend Decomposition Based on Loess

Krake,

Klötzl,

Hägele

et al. 2024

IEEE Trans. Visual. Comput. Graphics

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Seasonal-trend decomposition based on loess (STL) is a powerful tool to explore time series data visually. In this paper, we present an extension of STL to uncertain data, named uncertaintyaware STL (UASTL). Our method propagates multivariate Gaussian distributions mathematically exactly through the entire analysis and visualization pipeline. Thereby, stochastic quantities shared between the components of the decomposition are preserved. Moreover, we present application scenarios with uncertainty modeling based on Gaussian processes, e.g., data with uncertain areas or missing values. Besides these mathematical results and modeling aspects, we introduce visualization techniques that address the challenges of uncertainty visualization and the problem of visualizing highly correlated components of a decomposition. The global uncertainty propagation enables the time series visualization with STL-consistent samples, the exploration of correlation between and within decomposition's components, and the analysis of the impact of varying uncertainty. Finally, we show the usefulness of UASTL and the importance of uncertainty visualization with several examples. Thereby, a comparison with conventional STL is performed.

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Section: Related Workmentioning

confidence: 99%

Uncertainty-Aware Seasonal-Trend Decomposition Based on Loess

Krake,

Klötzl,

Hägele

et al. 2024

IEEE Trans. Visual. Comput. Graphics

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“…To not only communicate the alternatives and their computed ''goodness,'' but also their trustworthiness, their uncertainty may also be established and shown as meta-data for each alternative's rating. 68,69 This is particularly important for criteria derived from partial or predicted results.…”

Section: Stage 2 Design -Mcda To Generate Guidancementioning

confidence: 99%

Providing visual analytics guidance through decision support

Han

Schulz

2023

Information Visualization

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Guidance in visual analytics aims to support users in accomplishing their analytical goals and generating insights. Different approaches for guidance are widely adopted in many tools and frameworks for various purposes – from helping to focus on relevant data subspaces to selecting suitable visualization techniques. With each of these different purposes come specific considerations on how to provide the needed guidance. In this paper, we propose a generic method for making these considerations by framing the guidance problem as a decision problem and applying decision making theory and models toward its solution. This method passes through three stages: (1) identifying decision points; (2) deriving and evaluating alternatives; (3) visualizing the resulting alternatives to support users in comparing them and making their choice. Our method is realized as a set of practical worksheets and illustrated by applying it to a use case of providing guidance among different clustering methods. Finally, we compare our method with existing guidance frameworks to relate and delineate the respective goals and contributions of each.

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“…Haegele et al [21] provided several examples of workflows for several applications such as hierarchical graphs and texts. Padilla et al [22] summarized the potential design space for uncertainty visualization and provided existing visualization theories, which aim to minimize the large design space.…”

Section: Workflows and Guidelines For Uncertainty Visualizationmentioning

confidence: 99%

A Workflow to Systematically Design Uncertainty-Aware Visual Analytics Applications

Maack,

Raith,

Perez

et al. 2023

Preprint

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Visual Analytics (VA) is a paradigm for insight generation by using visual analysis techniques and automated reasoning by transforming data into hypotheses and visualization to extract new insights, feeding them back into the data. This enhances the data until the desired insight is found. Many applications use this principle to provide meaningful mechanisms to assist decision-makers in achieving their goals. This process can be affected by a variety of uncertainties that can interfere with the user decision-making process. Unfortunately, there is no methodical description and handling tool to systematically include uncertainty in VA. We provide a unified workflow to transform the classic VA cycle into an uncertainty-aware Visual Analytics (UAVA) cycle consisting of five steps. Three real-world applications represent examples of the implementation of the UAVA cycle and the described workflow to prove its usability.

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Uncertainty visualization: Fundamentals and recent developments

Cited by 7 publications

References 35 publications

Uncertainty-Aware Seasonal-Trend Decomposition Based on Loess

Uncertainty-Aware Seasonal-Trend Decomposition Based on Loess

Providing visual analytics guidance through decision support

A Workflow to Systematically Design Uncertainty-Aware Visual Analytics Applications

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