Perception of Average Value in Multiclass Scatterplots

Gleicher, Michael; Correll, Michael; Nothelfer, Christine; Franconeri, Steven

doi:10.1109/tvcg.2013.183

Cited by 88 publications

(93 citation statements)

References 63 publications

(78 reference statements)

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“…A follow-up study [17] indicated that a 2D scatterplot is as good as an interactive 3D scatterplot or a scatterplot matrix in showing classes. Gleigher et al [18] explored the visual design of multiclass scatterplots for the task of identifying the mean value of a set of points. Lewis et al [19] compared grouping quality metrics with human evaluations by using a diverse set of measures based on a theoretical taxonomy.…”

Section: Empirical Studies Of Scatterplot Perceptionmentioning

confidence: 99%

Towards Perceptual Optimization of the Visual Design of Scatterplots

Micallef

Palmas

Oulasvirta

et al. 2017

IEEE Trans. Visual. Comput. Graphics

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Abstract-Designing a good scatterplot can be difficult for non-experts in visualization, because they need to decide on many parameters, such as marker size and opacity, aspect ratio, color, and rendering order. This paper contributes to research exploring the use of perceptual models and quality metrics to set such parameters automatically for enhanced visual quality of a scatterplot. A key consideration in this paper is the construction of a cost function to capture several relevant aspects of the human visual system, examining a scatterplot design for some data analysis task. We show how the cost function can be used in an optimizer to search for the optimal visual design for a user's dataset and task objectives (e.g., "reliable linear correlation estimation is more important than class separation"). The approach is extensible to different analysis tasks. To test its performance in a realistic setting, we pre-calibrated it for correlation estimation, class separation, and outlier detection. The optimizer was able to produce designs that achieved a level of speed and success comparable to that of those using human-designed presets (e.g., in R or MATLAB). Case studies demonstrate that the approach can adapt a design to the data, to reveal patterns without user intervention.

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Section: Empirical Studies Of Scatterplot Perceptionmentioning

confidence: 99%

Towards Perceptual Optimization of the Visual Design of Scatterplots

Micallef

Palmas

Oulasvirta

et al. 2017

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

show abstract

“…Recently, it could also be shown that crowdsourcing can be an effective way of investigating the perceptual capabilities of visualizations [Heer and Bostock 2010], which led to the conduction of several interesting crowdsourcing studies in visualization. Prominent examples include the perception of averages in multi-class scatter plots [Gleicher et al 2013], but also the memorability of visualizations [Borkin et al 2013]. However, in the area of scientific visualization not many such studies have been published.…”

Section: Related Workmentioning

confidence: 99%

Evaluating the perception of semi-transparent structures in direct volume rendering techniques

Englund

Ropinski²

2016

SIGGRAPH ASIA 2016 Symposium on Visualization

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Direct volume rendering (DVR) provides the possibility to visualize volumetric data sets as they occur in many scientific disciplines. A key benefit of DVR is that semi-transparency can be facilitated in order to convey the complexity of the visualized data. Unfortunately, semi-transparency introduces new challenges in spatial comprehension of the visualized data, as the ambiguities inherent to semi-transparent representations affect spatial comprehension. Accordingly, many visualization techniques have been introduced to enhance the spatial comprehension of DVR images. In this paper, we conduct a user evaluation in which we compare standard DVR with five visualization techniques which have been proposed to enhance the spatial comprehension of DVR images. In our study, we investigate the perceptual performance of these techniques and compare them against each other to find out which technique is most suitable for different types of data and purposes. In order to do this, a large-scale user study was conducted with 300 participants who completed a number of micro-tasks designed such that the aggregated feedback gives us insight on how well these techniques aid the end user to perceive depth and shape of objects. Within this paper we discuss the tested techniques, present the conducted study and analyze the retrieved results.

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“…In visualization research, these types of studies can be used to address perceptual and comprehensibility questions [20], whereby low-level tasks such as compare, contrast, associate, distinguish, rank, cluster, correlate or categorize can be considered [40]. Unfortunately, the number of quantitative user evaluations is still rather low [23], despite the fact that these evaluations can be used to address a broad variety of research questions, reaching from aesthetics [41], over spatial comprehension [18], and visual averaging [17] to memorability [3]. Crowdsourcing.…”

Section: Related Workmentioning

confidence: 99%

“…An additional benefit is that participants of crowdsourced user studies can participate at any given time, and is not limited to the opening hours of the lab. While there has not yet been any work published where crowdsourcing has been utilized for evaluating scientific visualization research, crowdsourcing has been successfully utilized for user studies in information visualization [9,10,16,17,19,24,38,47].…”

Section: Introductionmentioning

confidence: 99%

A crowdsourcing system for integrated and reproducible evaluation in scientific visualization

Englund

Kottravel

Ropinski

2016

2016 IEEE Pacific Visualization Symposium (PacificVis)

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User evaluations have gained increasing importance in visualization research over the past years, as in many cases these evaluations are the only way to support the claims made by visualization researchers. Unfortunately, recent literature reviews show that in comparison to algorithmic performance evaluations, the number of user evaluations is still very low. Reasons for this are the required amount of time to conduct such studies together with the difficulties involved in participant recruitment and result reporting. While it could be shown that the quality of evaluation results and the simplified participant recruitment of crowdsourcing platforms makes this technology a viable alternative to lab experiments when evaluating visualizations, the time for conducting and reporting such evaluations is still very high. In this paper, we propose a software system, which integrates the conduction, the analysis and the reporting of crowdsourced user evaluations directly into the scientific visualization development process. With the proposed system, researchers can conduct and analyze quantitative evaluations on a large scale through an evaluation-centric user interface with only a few mouse clicks. Thus, it becomes possible to perform iterative evaluations during algorithm design, which potentially leads to better results, as compared to the time consuming user evaluations traditionally conducted at the end of the design process. Furthermore, the system is built around a centralized database, which supports an easy reuse of old evaluation designs and the reproduction of old evaluations with new or additional stimuli, which are both driving challenges in scientific visualization research. We will describe the system's design and the considerations made during the design process, and demonstrate the system by conducting three user evaluations, all of which have been published before in the visualization literature.

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Perception of Average Value in Multiclass Scatterplots

Cited by 88 publications

References 63 publications

Towards Perceptual Optimization of the Visual Design of Scatterplots

Towards Perceptual Optimization of the Visual Design of Scatterplots

Evaluating the perception of semi-transparent structures in direct volume rendering techniques

A crowdsourcing system for integrated and reproducible evaluation in scientific visualization

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