Since the late 1800s, public data visualizations displaying election forecasts and results—such as the red and blue map of the United State—have presented an irreparably divided country. However, on November 8, 2016, the New York Times published a data visualization on their live presidential forecast page that broke over a century of visual expectations, inspiring many to tweet reactions to what popular media has dubbed the "jittery gauges." Not surprisingly, the tweets about this unique and difficult-to-interpret display were mostly negative. This paper argues, though, that the negative feedback indicates that the gauges, while imperfect, represent an important step away from visualizations that support the growing perception of party polarization. The key factor present in the gauges is the data design principle of uncertainty or possibility. If major news outlets were more thoughtful about introducing uncertain elements into visualizations of American politics, perhaps the nation could begin to imagine a political landscape that moves beyond red vs. blue, me vs. you.

Purpose – The increasing amounts of data has become an important factor for organizations. A visual dashboard is a BI tool that can be used for communication of insights from big data. One way for individuals in organizations to get insights from timely and large data sets is through visualizations displayed in visual dashboards, but studies show that most of them fall short of their potential. Therefore, the aim of this study is to examine how individuals make use of visual dashboards. Design/Methodology – To obtain this understanding a literature review was performed, followed by a study conducted in two phases. Firstly, a multiple-case study of four organizations was performed, which included interviews and the think-aloud technique. Secondly, the findings from the multiple-case study were tested through interviews with experts in the BI area. Findings – The findings indicate that low democratization, scarce effects, and simplicity are reasons for why the use of visual dashboards is not fully exploited. Low attention and understanding combined with a lack of timely information means that data-driven actions are not taken. The phase of predictive analysis has not yet been reached, rather organizations are still using the visual dashboard for descriptive analysis, which in turn hinders the possibility for effects. For these reasons the use of visual dashboards does not meet the often described purpose to make better and faster decisions, and organizations are still to take steps in that direction. Research limitations – The sampling of industries in the multiple-case study could affect variables as number of KPIs.

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Data from present day scientific simulations and observations of physical processes often consist of multiple scalar fields. It is important to study the interactions between the fields to understand the underlying phenomena. A visual representation of these interactions would assist the scientist by providing quick insights into complex relationships that exist between the fields. We describe new techniques for visual analysis of multifield scalar data where the relationships can be quantified by the gradients of the individual scalar fields and their mutual alignment. Empirically, gradients along with their mutual alignment have been shown to be a good indicator of the relationships between the different scalar variables. The Jacobi set, defined as the set of points where the gradients are linearly dependent, describes the relationship between the gradient fields. The Jacobi set of two piecewise linear functions may contain several components indicative of noisy or a feature-rich dataset. For two dimensional domains, we pose the problem of simplification as the extraction of level sets and offset contours and describe a robust technique to simplify and create a multi-resolution representation of the Jacobi set. Existing isosurface-based techniques for scalar data exploration like Reeb graphs, contour spectra, isosurface statistics, etc., study a scalar field in isolation. We argue that the identification of interesting isovalues in a multifield data set should necessarily be based on the interaction between the different fields. We introduce a variation density function that profiles the relationship between multiple scalar fields over isosurfaces of a given scalar field. This profile serves as a valuable tool for multifield data exploration because it provides the user with cues to identify interesting isovalues of scalar fields. Finally, we introduce a new multifield comparison measure to capture relationships between scalar variables. We also show that our measure is insensitive to noise in the scalar fields and to noise in their gradients. Further, it can be computed robustly and efficiently. The comparison measure can be used to identify regions of interest in the domain where interactions between the scalar fields are significant. Subsequent visualization of the data focuses on these regions of interest leading to effective visual analysis. We demonstrate the effectiveness of our techniques by applying them to real world data from different domains like combustion studies, climate sciences and computer graphics.

No description available.