Arcs, Angles, or Areas: Individual Data Encodings in Pie and Donut Charts

Skau, Drew; Kosara, Robert

doi:10.1111/cgf.12888

Cited by 71 publications

(58 citation statements)

References 18 publications

(28 reference statements)

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“…This indicates that the significant effect of the inner radius on accuracy is mainly the result of the thinnest doughnut chart ( IR 4 ). These results align with similar results by Skau and Kosara [SK16] who investigate the effect of the inner radius and find that it makes no difference to the accuracy, except when the doughnut chart becomes extremely thin.…”

Section: Discussionsupporting

confidence: 90%

“…Their results suggest that angle is the least important visual cue for both charts, while both chart types are equally accurate for proportion estimation. We note here that the present work and [SK16] have one similar experiment, namely, our Experiment 2 and their Study 2, which studied the effect of the inner radius on proportion estimation accuracy of doughnut charts. The results of the two experiments are the same although the details of the experimental setup are different.…”

Section: Related Workmentioning

confidence: 89%

“…However, the context of the two experiments and the experimental methods are different. Skau and Kosara compare in [SK16] individual encodings (arc length, area or angle) in chart reading and their three studies aim to answer the question of which encoding is the most important one. In contrast, our work does not focus on individual encodings but aims to study the effect of doughnut chart design parameters (inner radius, outer radius and additional visual cues) on proportion estimation accuracy and what can be improved in the chart design (e.g.…”

Section: Discussionmentioning

confidence: 99%

“…On the same question, Spence and Lewandowsky [SL91] suggest that subjects probably pay no attention to area when presented with a pie chart. Skau and Kosara [SK16] recently tested the effectiveness of individual data encodings (such as the arc, the angle and the area) in pie and doughnut charts. Their results suggest that angle is the least important visual cue for both charts, while both chart types are equally accurate for proportion estimation.…”

Section: Related Workmentioning

confidence: 99%

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A Study of the Effect of Doughnut Chart Parameters on Proportion Estimation Accuracy

Cai

Efstathiou

Xie

et al. 2018

Computer Graphics Forum

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Pie and doughnut charts nicely convey the part–whole relationship and they have become the most recognizable chart types for representing proportions in business and data statistics. Many experiments have been carried out to study human perception of the pie chart, while the corresponding aspects of the doughnut chart have seldom been tested, even though the doughnut chart and the pie chart share several similarities. In this paper, we report on a series of experiments in which we explored the effect of a few fundamental design parameters of doughnut charts, and additional visual cues, on the accuracy of such charts for proportion estimates. Since mobile devices are becoming the primary devices for casual reading, we performed all our experiments on such device. Moreover, the screen size of mobile devices is limited and it is therefore important to know how such size constraint affects the proportion accuracy. For this reason, in our first experiment we tested the chart size and we found that it has no significant effect on proportion accuracy. In our second experiment, we focused on the effect of the doughnut chart inner radius and we found that the proportion accuracy is insensitive to the inner radius, except the case of the thinnest doughnut chart. In the third experiment, we studied the effect of visual cues and found that marking the centre of the doughnut chart or adding tick marks at 25% intervals improves the proportion accuracy. Based on the results of the three experiments, we discuss the design of doughnut charts and offer suggestions for improving the accuracy of proportion estimates.

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Section: Discussionsupporting

confidence: 90%

Section: Related Workmentioning

confidence: 89%

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

A Study of the Effect of Doughnut Chart Parameters on Proportion Estimation Accuracy

Cai

Efstathiou

Xie

et al. 2018

Computer Graphics Forum

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“…Based on this work a great amount of studies has been conducted to extend and validate the variables (e.g., Cleveland and McGill [1984, 1985, 1987; Mackinlay [1986]) and is still an important topic in current research (e.g., Chung et al [2015Chung et al [ , 2016; Skau and Kosara [2016]). Among other things those studies led to a ranking of perceptual tasks which is useful for designers of visualizations by supporting the data mapping process.…”

Section: Visual Marks and Variablesmentioning

confidence: 99%

Exploring the Potential of Physical Visualizations

Stusak¹

2015

Proceedings of the Ninth International Conference on Tangible, Embedded, and Embodied Interaction

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The goal of an external representation of abstract data is to provide insights and convey information about the structure of the underlying data, therefore helping people execute tasks and solve problems more effectively. Apart from the popular and well-studied digital visualization of abstract data there are other scarcely studied perceptual channels to represent data such as taste, sound or haptic. My thesis focuses on the latter and explores in which ways human knowledge and ability to sense and interact with the physical non-digital world can be used to enhance the way in which people analyze and explore abstract data. Emerging technological progress in digital fabrication allow an easy, fast and inexpensive production of physical objects. Machines such as laser cutters and 3D printers enable an accurate fabrication of physical visualizations with different form factors as well as materials. This creates, for the first time, the opportunity to study the potential of physical visualizations in a broad range.The thesis starts with the description of six prototypes of physical visualizations from static examples to digitally augmented variations to interactive artifacts. Based on these explorations, three promising areas of potential for physical visualizations were identified and investigated in more detail: perception & memorability, communication & collaboration, and motivation & self-reflection.The results of two studies in the area of information recall showed that participants who used a physical bar chart retained more information compared to the digital counterpart. Particularly facts about maximum and minimum values were be remembered more efficiently, when they were perceived from a physical visualization.Two explorative studies dealt with the potential of physical visualizations regarding communication and collaboration. The observations revealed the importance on the design and aesthetic of physical visualizations and indicated a great potential for their utilization by audiences with less interest in technology. The results also exposed the current limitations of physical visualizations, especially in contrast to their well-researched digital counterparts.In the area of motivation we present the design and evaluation of the Activity Sculptures project. We conducted a field study, in which we investigated physical visualizations of personal running activity. It was discovered that these sculptures generated curiosity and experimentation regarding the personal running behavior as well as evoked social dynamics such as discussions and competition.Based on the findings of the aforementioned studies this thesis concludes with two theoretical contributions on the design and potential of physical visualizations. On the one hand, it proposes a conceptual framework for material representations of personal data by describing a production and consumption lens. The goal is to encourage artists and designers working in the field of personal informatics to harness the interactive capabilities af...

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Dissecting Pie Charts

Siirtola

Räihä

Istance

et al. 2019

Lecture Notes in Computer Science

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Pie charts can be regularly found both in the popular media and research publications. There is evidence that other forms of visualizations make it easier to evaluate the relative order of the data items. Doughnut charts have been suggested as a variation that has advantages over pie charts. We investigated how pie charts and doughnut charts are affected by the number of sectors in the chart, the difference in sector sizes, and the size of the hole. We carried out an eye tracking study to find out how the charts are read. Our study reveals the distribution of visual attention for each type of chart. The results indicate that doughnut charts with a medium size hole have a slight edge over the other chart types we studied. We also show that contrary to common claims, for information extraction also the area and length of sector arc are used in addition to the angles of the sectors.

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Arcs, Angles, or Areas: Individual Data Encodings in Pie and Donut Charts

Cited by 71 publications

References 18 publications

A Study of the Effect of Doughnut Chart Parameters on Proportion Estimation Accuracy

A Study of the Effect of Doughnut Chart Parameters on Proportion Estimation Accuracy

Exploring the Potential of Physical Visualizations

Dissecting Pie Charts

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