Saliency Deficit and Motion Outlier Detection in Animated Scatterplots

Veras, Rafael; Collins, Christopher

doi:10.1145/3290605.3300771

Cited by 12 publications

(13 citation statements)

References 32 publications

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“…Veras and Collins [58] recently studied outliers in 2-state animated scatterplots, where points may be outliers in terms of speed and direction of motion. They also found that different visual channels affect outlier saliency to varying extents, and as a result it can be difficult to consistently detect them in animated scatterplots.…”

Section: Populationmentioning

confidence: 99%

“…This will likely require a systematic generation of synthetic stimuli modeled after the reversals and outliers apparent in the real United Nations data that we used in our current experiment. Generating synthetic stimuli would also permit a finer control over other visual variables such as color and size, as Veras and Collins' recently showed that these and other visual variables contribute unevenly to outlier detection [58]. Other chart types.…”

Section: Potential Extensions To This Studymentioning

confidence: 99%

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A Comparative Evaluation of Animation and Small Multiples for Trend Visualization on Mobile Phones

Brehmer

Lee

Isenberg

et al. 2020

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. Cropped screen captures from our experimental mobile application. Left: an instance of the animation condition (open this PDF in Acrobat Reader to view the animation). Right: an instance of the small multiples condition, in which the position of each item reflects its values in the year 2000; a "trail" encodes the item's previous positions from 1975 to 1999.Abstract-We compare the efficacy of animated and small multiples variants of scatterplots on mobile phones for comparing trends in multivariate datasets. Visualization is increasingly prevalent in mobile applications and mobile-first websites, yet there is little prior visualization research dedicated to small displays. In this paper, we build upon previous experimental research carried out on larger displays that assessed animated and non-animated variants of scatterplots. Incorporating similar experimental stimuli and tasks, we conducted an experiment where 96 crowdworker participants performed nine trend comparison tasks using their mobile phones. We found that those using a small multiples design consistently completed tasks in less time, albeit with slightly less confidence than those using an animated design. The accuracy results were more task-dependent, and we further interpret our results according to the characteristics of the individual tasks, with a specific focus on the trajectories of target and distractor data items in each task. We identify cases that appear to favor either animation or small multiples, providing new questions for further experimental research and implications for visualization design on mobile devices. Lastly, we provide a reflection on our evaluation methodology.

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Section: Populationmentioning

confidence: 99%

Section: Potential Extensions To This Studymentioning

confidence: 99%

A Comparative Evaluation of Animation and Small Multiples for Trend Visualization on Mobile Phones

Brehmer

Lee

Isenberg

et al. 2020

IEEE Trans. Visual. Comput. Graphics

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show abstract

“…When designing the animation between frames, we drew inspiration from the wellknown Gapminder tool and other animated visualizations [16], which tap into the perceptual system's ability to track objects and identify motion outliers. However, some studies have found that animation introduces perceptual inaccuracy compared to small multiples and overlays [36], and that motion outliers may be difficult to reliably perceive [46]. These concerns may be exacerbated in DR plots, which often contain tens of thousands of points (orders of magnitude more than the scatter plots tested in the aforementioned studies).…”

Section: Animation and Star Trailsmentioning

confidence: 98%

Emblaze: Illuminating Machine Learning Representations through Interactive Comparison of Embedding Spaces

Sivaraman

Perer

2022

27th International Conference on Intelligent User Interfaces

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Fig. 1. Interactive comparison of embeddings for 50,000 words from three different corpora (a), using Emblaze within a Jupyter notebook. Given a selection, in this case a group of words about winemaking (b), the Star Trail visualization (c) highlights points near the group in the high-dimensional space whose neighborhoods change significantly. The plot can be manually interpolated between the two embedding spaces using a slider (d). In the sidebar, the neighborhood comparisons for the current selection (e) show a greater emphasis on recreational wine-related activities in Twitter data, such as "tastings" and "Sonoma. " The sidebar can alternatively display Suggested Selections relevant to the current visualization state (f), including clusters of wine varieties and other beverages.Modern machine learning techniques commonly rely on complex, high-dimensional embedding representations to capture underlying structure in the data and improve performance. In order to characterize model flaws and choose a desirable representation, model builders often need to compare across multiple embedding spaces, a challenging analytical task supported by few existing tools. We first interviewed nine embedding experts in a variety of fields to characterize the diverse challenges they face and techniques they use when analyzing embedding spaces. Informed by these perspectives, we developed a novel system called Emblaze that integrates embedding space comparison within a computational notebook environment. Emblaze uses an animated, interactive scatter plot with a novel Star Trail augmentation to enable visual comparison. It also employs novel neighborhood analysis and clustering procedures to dynamically suggest groups of points with interesting changes between spaces. Through a series of case studies with ML experts, we demonstrate how interactive comparison with Emblaze can help gain new insights into embedding space structure. CCS Concepts: • Human-centered computing → Visualization systems and tools; • Computing methodologies → Learning latent representations.

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“…While position is generally considered separable from all other channels, evidence suggests that position may be integral with some channels for more complex visualization tasks [19,51]. For example, position is integral with motion in outlier detection in multivariate scatterplots [89]. Recent studies provide formal models of separability across color, shape, and size for comparing data values [22,80,85], but we lack formal models for the separability of position with other channels in perceiving means and distributions.…”

Section: Scatterplot Perceptionmentioning

confidence: 99%

The Weighted Average Illusion: Biases in Perceived Mean Position in Scatterplots

Matt-Heun¹,

Witt²,

Szafir³

2021

Preprint

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The mean provides a reference for judging data points as above or below average.If points vary in lightness or size, the perceived mean position will be biased... ...leading some data points to be misjudged as being above average. Fig. 1. We measured how integrating size or lightness into scatterplots can systematically bias the perceived mean of those points. We model how individual marks contribute to this bias, allowing us to predict differences in the actual mean (blue) and where people see the mean (orange). This misinterpretation can inhibit sensemaking and decision making. For instance, points in the grey boxes above will be incorrectly perceived as having higher-than average y-values. Under this illusion, graph readers' mean estimates can be increasingly biased as a function of size or lightness ranges mapped onto the data.

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Saliency Deficit and Motion Outlier Detection in Animated Scatterplots

Cited by 12 publications

References 32 publications

A Comparative Evaluation of Animation and Small Multiples for Trend Visualization on Mobile Phones

A Comparative Evaluation of Animation and Small Multiples for Trend Visualization on Mobile Phones

Emblaze: Illuminating Machine Learning Representations through Interactive Comparison of Embedding Spaces

The Weighted Average Illusion: Biases in Perceived Mean Position in Scatterplots

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