Danielle Albers Szafir

Lightness Constancy in Surface Visualization

Color is a common channel for displaying data in surface visualization, but is affected by the shadows and shading used to convey surface depth and shape. Understanding encoded data in the context of surface structure is critical for effective analysis in a variety of domains, such as in molecular biology. In the physical world, lightness constancy allows people to accurately perceive shadowed colors; however, its effectiveness in complex synthetic environments such as surface visualizations is not well understood. We report a series of crowdsourced and laboratory studies that confirm the existence of lightness constancy effects for molecular surface visualizations using ambient occlusion. We provide empirical evidence of how common visualization design decisions can impact viewers abilities to accurately identify encoded surface colors. These findings suggest that lightness constancy aids in understanding color encodings in surface visualization and reveal a correlation between visualization techniques that improve color interpretation in shadow and those that enhance perceptions of surface depth. These results collectively suggest that understanding constancy in practice can inform effective visualization design.

Design Factors for Summary Visualization in Visual Analytics

Data summarization allows analysts to explore datasets that may be too complex or too large to visualize in detail. Designers face a number of design and implementation choices when using summarization in visual analytics systems. While these choices influence the utility of the resulting system, there are no clear guidelines for the use of these summarization techniques. In this paper, we codify summarization use in existing systems to identify key factors in the design of summary visualizations. We use quantitative content analysis to systematically survey examples of visual analytics systems and enumerate the use of these design factors in data summarization. Through this analysis, we expose the relationship between design considerations, strategies for data summarization in visualization systems, and how different summarization methods influence the analyses supported by systems. We use these results to synthesize common patterns in real‐world use of summary visualizations and highlight open challenges and opportunities that these patterns offer for designing effective systems. This work provides a more principled understanding of design practices for summary visualization and offers insight into underutilized approaches.

Becoming butterflies: interactive embodiment of the butterfly lifecycle

Museum directors are often faced with the challenge of engaging users in the museum experience while preserving the intentions of exhibit content. Designing exhibits for children can heighten the tension between these sometimes competing goals. Working with the University of Colorado Museum of Natural History, we designed and implemented Metamorphosis, a system for an engaging, educational butterfly exhibit. The exhibit employs augmented reality and full-body interaction to guide users through critical phases of a butterflys metamorphosis process. In Metamorphosis, we incorporated participatory design methods in order to leverage engaging ubiquitous technologies while supporting an educational narrative aligned with the museums goals.

TextDNA: Visualizing Word Usage with Configurable Colorfields

Patterns of words used in different text collections can characterize interesting properties of a corpus. However, these patterns are challenging to explore as they often involve complex relationships across many words and collections in a large space of words. In this paper, we propose a configurable colorfield design to aid this exploration. Our approach uses a dense colorfield overview to present large amounts of data in ways that make patterns perceptible. It allows flexible configuration of both data mappings and aggregations to expose different kinds of patterns, and provides interactions to help connect detailed patterns to the corpus overview. TextDNA, our prototype implementation, leverages the GPU to provide interactivity in the web browser even on large corpora. We present five case studies showing how the tool supports inquiry in corpora ranging in size from single document to millions of books. Our work shows how to make a configurable colorfield approach practical for a range of analytic tasks.

The good, the bad, and the biased: five ways visualizations can mislead (and how to fix them)

→ Visualizations allow people to readily analyze and communicate data. However, many common visualization designs lead to engaging imagery but false conclusions. → By understanding what people see when they look at a visualization, we can design visualizations that support more accurate data analysis and avoid unnecessary biases. to help people see what matters. This article reviews common visualization practices that may inhibit effective analysis, why these designs are problematic, and how to avoid them. The discussion illustrates a need to better understand how visualizations can support flexible and accurate data analysis while mitigating potential sources of bias. Glancing at the bar chart in Figure 1 will likely convince you that one method performs twice as well as the other. However, this visualization is misleading: The true difference between methods is only 5 percent. Talks and articles frequently feature flashy visualizations like this—visualizations Data visualizations allow people to readily explore and communicate knowledge drawn from data. Visualization methods range from standard scatterplots and line graphs to intricate interactive systems for analyzing large data volumes at a glance. But how can we craft visualizations that effectively communicate the right information from our data? What aspects of data and design need to come together to develop accurate insights? The answer lies in the way we see the world: People use their visual and cognitive systems (i.e., our eyes and brain) to extract meaning from visualized data. However, flashy visualizations are not always optimized The Good, the Bad, and the Biased: Five Ways Visualizations Can Mislead (and How to Fix Them)

Effects of Size and Shape on Perceived Color Differences

[1] Mahy, Eycken, & Oosterlinck. (1994). Evaluation of uniform color spaces developed after the adoption of CIELAB and CIELUV. Color Research & Application, 19(2), 105-121. [2] Stone, Szafir, & Setlur. (2014). An engineering model for color difference as a function of size. In Color and Imaging Conference (pp. 253-258). IS&T. [3] Szafir, Stone, & Gleicher. (2014). Adapting color difference for design. In Color and Imaging Conference (pp. 228-233). IS&T. Experiment Two: Discriminability for symmetric elongated objects varies with the longest edge and edge ratio

Visualization-aware color design

Color encoding design currently focuses on the colors themselves: visualization designers choose sets of colors that work well in isolation. However, the effectiveness of a color encoding depends on properties of the visualization it is used for, such as the size or shape of marks. We argue for a new way of thinking about color design in visualizations: designers should choose colors based on a given context rather than in isolation. We identify three categories of design constraints that contribute to the effective color choices in visualization: aesthetic constraints, perceptual constraints, and functional constraints. The conceptual framework formed by these constraints helps designers optimize color choices based on known properties of a given visualization. In this poster, we discuss this framework in detail and illustrate how it informs more effective visualization design.