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We propose a visualization technique, Du Bois wrapped bar chart, inspired by work of W.E.B Du Bois. Du Bois wrapped bar charts enable better large-to-small bar comparison by wrapping large bars over a certain threshold. We first present two crowdsourcing experiments comparing wrapped and standard bar charts to evaluate (1) the benefit of wrapped bars in helping participants identify and compare values; (2) the characteristics of data most suitable for wrapped bars. In the first study (n=98) using real-world datasets, we find that wrapped bar charts lead to higher accuracy in identifying and estimating ratios between bars. In a follow-up study (n=190) with 13 simulated datasets, we find participants were consistently more accurate with wrapped bar charts when certain category values are disproportionate as measured by entropy and H-spread. Finally, in an in-lab study, we investigate participants' experience and strategies, leading to guidelines for when and how to use wrapped bar charts.
Fish Tank Virtual Reality (FTVR) displays provide compelling 3D experiences by rendering view-dependent imagery on a 2D screen. While users perceive a 3D object in space, they are actually looking at pixels on a 2D screen, thus, a perceptual duality exists between the object's pixels and the 3D percept potentially interfering with the experience. To investigate, we conducted an experiment to see whether the on-screen size of the 2D imagery affects the perceived object size in 3D space with different viewing conditions, including stereopsis. We found that the size of on-screen imagery significantly influenced object size perception, causing 83.3% under/overestimation of perceived size when viewing without stereopsis and reducing to 64.7% with stereopsis. Contrary to reality, objects look smaller when the viewer gets closer. Understanding the perceptual duality helps us to provide accurate perception of real-world objects depicted in the virtual environment and pave the way for 3D applications.
Bar charts with y-axes that don't begin at zero can visually exaggerate effect sizes. However, advice for whether or not to truncate the y-axis can be equivocal for other visualization types. In this paper we present examples of visualizations where this y-axis truncation can be beneficial as well as harmful, depending on the communicative and analytic intent. We also present the results of a series of crowd-sourced experiments in which we examine how y-axis truncation impacts subjective effect size across visualization types, and we explore alternative designs that more directly alert viewers to this truncation. We find that the subjective impact of axis truncation is persistent across visualizations designs, even for designs with explicit visual cues that indicate truncation has taken place. We suggest that designers consider the scale of the meaningful effect sizes and variation they intend to communicate, regardless of the visual encoding.
As physicalizations encode data in their physical 3D form, the orientation in which the user is viewing the physicalization may impact the way the information is perceived. However, this relation between user orientation and perception of physical properties is not well understood or studied. To investigate this relation, we conducted an experimental study with 20 participants who viewed 6 exemplars of physicalizations from 4 different perspectives. Our findings show that perception is directly influenced by user orientation as it affects (i) the number and type of clusters, (ii) anomalies and (iii) extreme values identified within a physicalization. Our results highlight the complexity and variability of the relation between user orientation and perception of physicalizations.
Heatmaps are a popular visualization technique that encode 2D density distributions using color or brightness. Experimental studies have shown though that both of these visual variables are inaccurate when reading and comparing numeric data values. A potential remedy might be to use 3D heatmaps by introducing height as a third dimension to encode the data. Encoding abstract data in 3D, however, poses many problems, too. To better understand this tradeoff, we conducted an empirical study (N=48) to evaluate the user performance of 2D and 3D heatmaps for comparative analysis tasks. We test our conditions on a conventional 2D screen, but also in a virtual reality environment to allow for real stereoscopic vision. Our main results show that 3D heatmaps are superior in terms of error rate when reading and comparing single data items. However, for overview tasks, the well-established 2D heatmap performs better.