The Utility of Beautiful Visualizations

Geovisualizations provide a means to inspect large complex multivariate datasets for information that would not otherwise be available with a tabular view or summary statistics alone. Aesthetically appealing visualizations can elicit prolonged exploration and encourage discovery. Creating data geovisualizations that are effective and beautiful is an important yet difficult challenge. Here we present a tool for rendering geovisualizations of continuous spatial data using the painterly techniques of impressionist-era artists. The techniques, which have been tested in controlled studies, vary the visual properties (e.g., hue, size, and tilt) of brush strokes to represent multiple data attributes simultaneously in each location. To demonstrate this technique, we render two examples 1) weather data attributes (e.g., temperature, windspeed, atmospheric pressure) from the NOAA Global Forecast System and 2) fragile state indices as assessed by Foreign Policy Magazine. These examples demonstrate how open source geospatial visualizations can harness aesthetics to enhance visual communication and viewer engagement

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Stroke Based Painterly Rendering

International audienceMany traditional art forms are produced by an artist sequentially placing a set of marks, such as brush strokes, on a canvas. Stroke based Rendering (SBR) is inspired by this process, and underpins many early and contemporary Artistic Stylization algorithms. This Chapter outlines the origins of SBR, and describes key algorithms for placement of brush strokes to create painterly renderings from source images. The chapter explores both local greedy, and global optimization based approaches to stroke placement. The issue of creative control in SBR is also briefly discussed

Evaluation of trend localization with multi-variate visualizations

Fig. 1 . Multi-variate visualization techniques we evaluated in our study, from left to right: Brush Strokes, Data-Driven Spots, Oriented Slivers, Color Blending, and Dimensional Stacking. These images depict a tri-county area in central Ohio. The encoded information is generated from a synthetic data set generated for the purposes of the study. See Section 3 for a explanation of the encoding. Abstract-Multi-valued data sets are increasingly common, with the number of dimensions growing. A number of multi-variate visualization techniques have been presented to display such data. However, evaluating the utility of such techniques for general data sets remains difficult. Thus most techniques are studied on only one data set. Another criticism that could be levied against previous evaluations of multi-variate visualizations is that the task doesn't require the presence of multiple variables. At the same time, the taxonomy of tasks that users may perform visually is extensive. We designed a task, trend localization, that required comparison of multiple data values in a multi-variate visualization. We then conducted a user study with this task, evaluating five multivariate visualization techniques from the literature (Brush Strokes, Data-Driven Spots, Oriented Slivers, Color Blending, Dimensional Stacking) and juxtaposed grayscale maps. We report the results and discuss the implications for both the techniques and the task