A Review of Uncertainty in Data Visualization

Brodlie, Ken; Osorio, Rodolfo S. Allendes; Lopes, Adriano

doi:10.1007/978-1-4471-2804-5_6

Cited by 167 publications

(89 citation statements)

References 59 publications

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“…Cedilnik and Rheingans, 2000;Ehlschlaeger et al, 1997;Sanyal et al, 2010;Wittenbrink et al, 1996). A comprehensive review of uncertainty typologies is provided by MacEachren et al (2005) and a review of uncertainty visualisation across science by Brodlie et al (2012). From the broad literature, five common dichotomous categories for uncertainty visualisation can be identified:…”

Section: Introductionmentioning

confidence: 99%

How to Assess Visual Communication of Uncertainty? A Systematic Review of Geospatial Uncertainty Visualisation User Studies

Kinkeldey

MacEachren

Schiewe

2014

The Cartographic Journal

154

115

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For decades, uncertainty visualisation has attracted attention in disciplines such as cartography and geographic visualisation, scientific visualisation and information visualisation. Most of this research deals with the development of new approaches to depict uncertainty visually; only a small part is concerned with empirical evaluation of such techniques. This systematic review aims to summarize past user studies and describe their characteristics and findings, focusing on the field of geographic visualisation and cartography and thus on displays containing geospatial uncertainty. From a discussion of the main findings, we derive lessons learned and recommendations for future evaluation in the field of uncertainty visualisation. We highlight the importance of user tasks for successful solutions and recommend moving towards task-centered typologies to support systematic evaluation in the field of uncertainty visualisation.

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Section: Introductionmentioning

confidence: 99%

How to Assess Visual Communication of Uncertainty? A Systematic Review of Geospatial Uncertainty Visualisation User Studies

Kinkeldey

MacEachren

Schiewe

2014

The Cartographic Journal

154

115

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“…The three intervals of purity are shown with a graduation of color based on the Hue-Saturation-Intensity model (useful to encode uncertainty, according to Brodlie, Osorio, and Lopes (2012)), from pale, in the case of low purities, to intense, in high purities. Similar color models and uncertainty management criteria have been adopted in other geospatial work such as in Pebesma, de Jong, and Briggs (2007) or in Laskey, Wright, and da Costa (2010), both focusing on continuous variables at pixel scale, whereas this work has adopted the parcel scale.…”

Section: Discussionmentioning

confidence: 99%

Uncertainty visualization of remote sensing crop maps enriched at parcel scale: a contribution for a more conscious GIS dataset usage

Serra

Pons

2015

Journal of Maps

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Uncertainty is an inherent issue in all thematic maps, including those produced from remote sensing (RS) data. Factors such as the characteristics of the imagery used to obtain the map or the classification methods, among others, can contribute to differences in the level of uncertainty. Given that map accuracy is not spatially uniform and that confusion matrices do not resolve the issue, this paper proposes a methodology to visualize the spatial uncertainty of a crop map obtained through RS and enriched at parcel scale. The final map covers an area of 3323 ha represented at a scale of 1:35,000. The estimator used to show the classification uncertainty is 'purity', that is, the percentage of each parcel area occupied by the finally assigned category. This value is an indicator of misclassification probability analyzed at parcel scale, which is a more useful measure in real management than are per pixel approaches. ARTICLE HISTORY

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“…Many interactive visualization approaches for various data types could be used, and a comprehensive overview is beyond the scope of this paper. The reader is referred to reviews, e.g., [56], [57], [58].…”

Section: Visual Assessment Of Input Datamentioning

confidence: 99%

Visualization System Requirements for Data Processing Pipeline Design and Optimization

Landesberger

Fellner

Ruddle

2017

IEEE Trans. Visual. Comput. Graphics

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Abstract-The rising quantity and complexity of data creates a need to design and optimize data processing pipelines -the set of data processing steps, parameters and algorithms that perform operations on the data. Visualization can support this process but, although there are many examples of systems for visual parameter analysis, there remains a need to systematically assess users' requirements and match those requirements to exemplar visualization methods. This article presents a new characterization of the requirements for pipeline design and optimization. This characterization is based on both a review of the literature and first-hand assessment of eight application case studies. We also match these requirements with exemplar functionality provided by existing visualization tools. Thus, we provide end-users and visualization developers with a way of identifying functionality that addresses data processing problems in an application. We also identify seven future challenges for visualization research that are not met by the capabilities of today's systems.

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A Review of Uncertainty in Data Visualization

Abstract: Universities of Leeds, Sheffield and York http://eprints.whiterose.ac.uk/ This is an author produced version of a book chapter published in Expanding the Frontiers of Visual Analytics and Visualization.

Cited by 167 publications

References 59 publications

How to Assess Visual Communication of Uncertainty? A Systematic Review of Geospatial Uncertainty Visualisation User Studies

How to Assess Visual Communication of Uncertainty? A Systematic Review of Geospatial Uncertainty Visualisation User Studies

Uncertainty visualization of remote sensing crop maps enriched at parcel scale: a contribution for a more conscious GIS dataset usage

Visualization System Requirements for Data Processing Pipeline Design and Optimization

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