Visualization of Uncertainty and Reasoning

While their importance is increasingly recognized, there remain many challenges in the development of uncertainty visualizations. We introduce two uncertainty visualizations for 2D bidirectional vector fields: one based on a static glyph and the other based on animated flow. These visualizations were designed for the task of understanding and interpreting anisotropic rock property models in the domain of seismic data processing. Aspects of the implementations are discussed relating to design, interaction, and tasks.

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“…We expect it may be of value to integrate this or the interpreter's confidence directly into the visualization as a decision aid. 26 …”

Section: Resultsmentioning

confidence: 99%

“…26 MacEachren proposed the use of colour saturation and blurring as being conducive to indicate uncertainty. 13 These attributes may intuitively be more natural encodings but their superiority to other encodings remains to be proven.…”

Section: Uncertainty Visualizationmentioning

confidence: 99%

Exploration of uncertainty in bidirectional vector fields

Zuk

Downton²,

Gray³

et al. 2008

SPIE Proceedings

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“…There are many general categorisations of uncertainty in the literature: Gershon [18] discusses a taxonomy of imperfection; Thomson et al [54] present a typology of uncertainty for geospatially referenced information for intelligence analysts; Skeels et al [46] derive a classification from commonalities in uncertainty areas uncovered through qualitative interviews; and Zuk and Carpendale [59] extend Thomson's typology to support reasoning uncertainty. In our workshop session described in section 4, we identified three specific types of uncertainty as being of interest to our DAs in their HTA.…”

Section: Storing Uncertaintymentioning

confidence: 99%

An Extensible Framework for Provenance in Human Terrain Visual Analytics

Walker

Slingsby

Dykes

et al. 2013

IEEE Trans. Visual. Comput. Graphics

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Fig. 1: Graphical summaries of bookmarks are used to record and browse the analytical process, here ordered (row-by-row) in the sequence in which they were bookmarked. Each can be used to access the live data, enabling analysts to revisit parts of the analytical process and helping verify past interpretations. A legend describing the encodings is provided in Fig. 6.Abstract-We describe and demonstrate an extensible framework that supports data exploration and provenance in the context of Human Terrain Analysis (HTA). Working closely with defence analysts we extract requirements and a list of features that characterise data analysed at the end of the HTA chain. From these, we select an appropriate non-classified data source with analogous features, and model it as a set of facets. We develop ProveML, an XML-based extension of the Open Provenance Model, using these facets and augment it with the structures necessary to record the provenance of data, analytical process and interpretations. Through an iterative process, we develop and refine a prototype system for Human Terrain Visual Analytics (HTVA), and demonstrate means of storing, browsing and recalling analytical provenance and process through analytic bookmarks in ProveML. We show how these bookmarks can be combined to form narratives that link back to the live data. Throughout the process, we demonstrate that through structured workshops, rapid prototyping and structured communication with intelligence analysts we are able to establish requirements, and design schema, techniques and tools that meet the requirements of the intelligence community. We use the needs and reactions of defence analysts in defining and steering the methods to validate the framework.

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“…Research has shown that factors such as personality [18,53], spatial ability [9], biases [29,54,55] and emotional state [3,17,23,34,41,45] impact a user's performance. Though progress is undeniable, a common limitation is that every cognitive factor that affects visualization performance is not considered or properly controlled.…”

Section: Introductionmentioning

confidence: 99%

Towards a 3-dimensional model of individual cognitive differences

Peck

Yuksel

Harrison

et al. 2012

Proceedings of the 2012 BELIV Workshop: Beyond Time and Errors - Novel Evaluation Methods for Visualization

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The effects of individual differences on user interaction is a topic that has been explored for the last 25 years in HCI. Recently, the importance of this subject has been carried into the field of information visualization and consequently, there has been a wide range of research conducted in this area. However, there has been no consensus on which evaluation methods best answer the unique needs of information visualization. In this position paper we propose that individual differences are evaluated in three dominant dimensions: cognitive traits, cognitive states and experience/bias. We believe that this is a first step in systematically evaluating the effects of users' individual differences on information visualization and visual analytics.

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Visualization of Uncertainty and Reasoning

Cited by 58 publications

References 35 publications

Exploration of uncertainty in bidirectional vector fields

Exploration of uncertainty in bidirectional vector fields

An Extensible Framework for Provenance in Human Terrain Visual Analytics

Towards a 3-dimensional model of individual cognitive differences

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