&lt;title&gt;ViA: a perceptual visualization assistant&lt;/title&gt;

Healey, Christopher G.; Amant, Robert St.; Elhaddad, Mahmoud

doi:10.1117/12.384859

Cited by 14 publications

(9 citation statements)

References 26 publications

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“…Subjects in his experiments were asked to perform numerical estimation tasks with varying hue and orientation differences, as well as varying display time. He also proposed ViA, a visualization system based on perceptual knowledge [15]. The goals of this system are very similar to the ones in our research.…”

Section: Previous Workmentioning

confidence: 60%

Subjective Quantification of Perceptual Interactions among some 2D Scientific Visualization Methods

Acevedo

Laidlaw

2006

IEEE Trans. Visual. Comput. Graphics

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Abstract-We present an evaluation of a parameterized set of 2D icon-based visualization methods where we quantified how perceptual interactions among visual elements affect effective data exploration. During the experiment, subjects quantified three different design factors for each method: the spatial resolution it could represent, the number of data values it could display at each point, and the degree to which it is visually linear. The class of visualization methods includes Poisson-disk distributed icons where icon size, icon spacing, and icon brightness can be set to a constant or coupled to data values from a 2D scalar field. By only coupling one of those visual components to data, we measured filtering interference for all three design factors. Filtering interference characterizes how different levels of the constant visual elements affect the evaluation of the data-coupled element. Our novel experimental methodology allowed us to generalize this perceptual information, gathered using ad-hoc artificial datasets, onto quantitative rules for visualizing real scientific datasets. This work also provides a framework for evaluating visualizations of multi-valued data that incorporate additional visual cues, such as icon orientation or color.

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Section: Previous Workmentioning

confidence: 60%

Subjective Quantification of Perceptual Interactions among some 2D Scientific Visualization Methods

Acevedo

Laidlaw

2006

IEEE Trans. Visual. Comput. Graphics

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“…The focus is on visualizing geospatially-referenced multi-dimensional data points, as is common in GIS, using simple glyph-based encoding (as in [4]). Data points are mapped to visual glyphs and overlaid on a geographic map.…”

Section: Methodsmentioning

confidence: 99%

“…Some advocate the integrated views approach [4] while others advocate multiple views [7]. The guidelines that do exist ( [1]) are not currently based on empirical evaluation.…”

Section: Introductionmentioning

confidence: 98%

Single complex glyphs versus multiple simple glyphs

Yost

North

2005

CHI '05 Extended Abstracts on Human Factors in Computing Systems

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Designers of information visualization systems have the choice to present information in a single integrated view or in multiple views. In practice, there is a continuum between the two strategies and designers must decide how much of each strategy to apply. Although high-level design guidelines (heuristics) are available, there are few low-level perceptual design guidelines for making this decision. We performed a controlled experiment with one, two, and four views to evaluate the strengths and weaknesses of these strategies on target detection and trend finding tasks in the context of multidimensional glyphs overlaid onto geographic maps. Results from the target detection tasks suggest that visual encoding is a more important factor when detecting a single attribute than the number of views. Additionally, for detecting two attributes, the trend indicates that reusing the most perceptually salient visual feature in multiple views provides faster performance than an integrated view that must map one of the attributes to a less salient feature.

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“…ViA [22,23] is another recent assistant. The authors listed important properties that visual assistants should check, and they used the following main ideas: ViA defines a search space of data to visualization mappings.…”

Section: State Of the Art 21 Knowledge-based And Behavior-based Assimentioning

confidence: 99%