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Froese M.-E.,University of Victoria | Tory M.,Tableau Research
IEEE Computer Graphics and Applications | Year: 2016

A variety of visualization guidelines, principles, and techniques are available to help create a visualization-based dashboard, but few publications discuss the experience of designing dashboards in the real world. This article discuss the lessons learned from designing applications for small start-up companies and institutions. From their experience as visualization practitioners, the authors confirm the need for tailored and customizable approaches, emphasize the need for a quicker way to create functional prototypes, point out frequent misconceptions on the scope of a functional prototype, discuss how performance can affect prototyping, and discuss the resistance of industrial partners to involve their customers in requirements gathering. © 1981-2012 IEEE.


Skau D.,Visually Inc. | Harrison L.,Tufts University | Kosara R.,Tableau Research
Computer Graphics Forum | Year: 2015

(Figure Presented). As data visualization becomes further intertwined with the field of graphic design and information graphics, small graphical alterations are made to many common chart formats. Despite the growing prevalence of these embellishments, their effects on communication of the charts' data is unknown. From an overview of the design space, we have outlined some of the common embellishments that are made to bar charts. We have studied the effects of these chart embellishments on the communication of the charts' data through a series of user studies on Amazon's Mechanical Turk platform. The results of these studies lead to a better understanding of how each chart type is perceived, and help provide guiding principles for the graphic design of charts. © 2015 The Author(s) Computer Graphics Forum © 2015 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd.


Skau D.,UNC Charlotte | Kosara R.,UNC Charlotte | Kosara R.,Tableau Research
Computer Graphics Forum | Year: 2016

Pie and donut charts have been a hotly debated topic in the visualization community for some time now. Even though pie charts have been around for over 200 years, our understanding of the perceptual factors used to read data in them is still limited. Data is encoded in pie and donut charts in three ways: arc length, center angle, and segment area. For our first study, we designed variations of pie charts to test the importance of individual encodings for reading accuracy. In our second study, we varied the inner radius of a donut chart from a filled pie to a thin outline to test the impact of removing the central angle. Both studies point to angle being the least important visual cue for both charts, and the donut chart being as accurate as the traditional pie chart. © 2016 The Author(s) Computer Graphics Forum © 2016 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd.


Haroz S.,Northwestern University | Kosara R.,Tableau Research | Franconeri S.L.,Northwestern University
IEEE Transactions on Visualization and Computer Graphics | Year: 2016

The connected scatterplot visualizes two related time series in a scatterplot and connects the points with a line in temporal sequence. News media are increasingly using this technique to present data under the intuition that it is understandable and engaging. To explore these intuitions, we (1) describe how paired time series relationships appear in a connected scatterplot, (2) qualitatively evaluate how well people understand trends depicted in this format, (3) quantitatively measure the types and frequency of misinter pretations, and (4) empirically evaluate whether viewers will preferentially view graphs in this format over the more traditional format. The results suggest that low-complexity connected scatterplots can be understood with little explanation, and that viewers are biased towards inspecting connected scatterplots over the more traditional format. We also describe misinterpretations of connected scatterplots and propose further research into mitigating these mistakes for viewers unfamiliar with the technique. © 2015 IEEE.


Haroz S.,Northwestern University | Kosara R.,Tableau Research | Franconeri S.L.,Northwestern University
Conference on Human Factors in Computing Systems - Proceedings | Year: 2015

Although the infographic and design communities have used simple pictographic representations for decades, it is still un-clear whether they can make visualizations more effective. Using simple charts, we tested how pictographic representa-tions impact (1) memory for information just viewed, as well as under the load of additional information, (2) speed of find-ing information, and (3) engagement and preference in seek-ing out these visualizations. We find that superfluous images can distract. But we find no user costs - And some intriguing benefits - When pictographs are used to represent the data. © Copyright 2015 ACM.


Anand A.,Tableau Research | Talbot J.,Tableau Research
IEEE Transactions on Visualization and Computer Graphics | Year: 2016

Effective small multiple displays are created by partitioning a visualization on variables that reveal interesting conditional structure in the data. We propose a method that automatically ranks partitioning variables, allowing analysts to focus on the most promising small multiple displays. Our approach is based on a randomized, non-parametric permutation test, which allows us to handle a wide range of quality measures for visual patterns defined on many different visualization types, while discounting spurious patterns. We demonstrate the effectiveness of our approach on scatterplots of real-world, multidimensional datasets. © 1995-2012 IEEE.


Setlur V.,Tableau Research | Stone M.C.,Tableau Research
IEEE Transactions on Visualization and Computer Graphics | Year: 2016

When data categories have strong color associations, it is useful to use these semantically meaningful concept-color associations in data visualizations. In this paper, we explore how linguistic information about the terms defining the data can be used to generate semantically meaningful colors. To do this effectively, we need first to establish that a term has a strong semantic color association, then discover which color or colors express it. Using co-occurrence measures of color name frequencies from Google n-grams, we define a measure for colorability that describes how strongly associated a given term is to any of a set of basic color terms. We then show how this colorability score can be used with additional semantic analysis to rank and retrieve a representative color from Google Images. Alternatively, we use symbolic relationships defined by WordNet to select identity colors for categories such as countries or brands. To create visually distinct color palettes, we use k-means clustering to create visually distinct sets, iteratively reassigning terms with multiple basic color associations as needed. This can be additionally constrained to use colors only in a predefined palette. © 1995-2012 IEEE.


Kosara R.,Tableau Research
IEEE Computer Graphics and Applications | Year: 2016

Data visualization research focuses on data exploration and analysis, yet the vast majority of visualizations people see were created for a different purpose: presentation. Whether we are talking about charts showing data to help make a presenter's point, data visuals created to accompany a news story, or the ubiquitous infographics, many more people consume charts than make them. Traditional visualization techniques treat presentation as an afterthought, but are there techniques uniquely suited to data presentation but not necessarily ideal for exploration and analysis? This article focuses on presentation-oriented techniques, considering their usefulness for presentation first and any other purposes as secondary. © 1981-2012 IEEE.


Talbot J.,Tableau Research | Setlur V.,Tableau Research | Anand A.,Tableau Research
IEEE Transactions on Visualization and Computer Graphics | Year: 2014

Bar charts are one of the most common visualization types. In a classic graphical perception paper, Cleveland McGill studied how different bar chart designs impact the accuracy with which viewers can complete simple perceptual tasks. They found that people perform substantially worse on stacked bar charts than on aligned bar charts, and that comparisons between adjacent bars are more accurate than between widely separated bars. However, the study did not explore why these differences occur. In this paper, we describe a series of follow-up experiments to further explore and explain their results. While our results generally confirm Cleveland McGill's ranking of various bar chart configurations, we provide additional insight into the bar chart reading task and the sources of participants' errors. We use our results to propose new hypotheses on the perception of bar charts. © 1995-2012 IEEE.


Stone M.,Tableau Research | Szafir D.A.,Tableau Research | Szafir D.A.,University of Wisconsin - Madison | Setlur V.,Tableau Research
Final Program and Proceedings - IS and T/SID Color Imaging Conference | Year: 2014

This work describes a first step towards the creation of an engineering model for the perception of color difference as a function of size. Our approach is to non-uniformly rescale CIELAB using data from crowdsourced experiments, such as those run on Amazon Mechanical Turk. In such experiments, the inevitable variations in viewing conditions reflect the environment many applications must run in. Our goal is to create a useful model for design applications where it is important to make colors distinct, but for which a small set of highly distinct colors is inadequate. © 2014 Society for Imaging Science and Technology.

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