Well-designed visualizations leverage the human visual system to help people understand large data sets. Yet, producing effective visualizations is a challenging design task. Designers must carefully choose how to map the data to visual variables such as position, size, shape and color. In this process they make hundreds of nuanced judgments while balancing perceptual and cognitive tradeoffs. In response, researchers in psychology, cartography, statistics, and computer science have investigated the effects of different visual variables on graphical perception: the ability of viewers to interpret visual encodings and thereby decode information in graphs. Despite great progress in developing design guidelines based on laboratory experiments, comprehensive evaluation of the visualization design space and real-world validation of the resulting guidelines have remained elusive.
The research is advancing our understanding of graphical perception and formulate new guidelines for visualization design. The research involves new experiments to address unresolved issues in graphical perception, including large-scale web-based studies using crowdsourcing techniques and controlled laboratory studies using sensitive measurements, namely eye-tracking. The investigators are applying the results of these studies to (a) develop guidelines for effective visualization design, (b) instantiate these guidelines in automated design procedures, and (c) validate the guidelines and resulting tools through case study deployments.
The goal of this project was to advance our understanding of graphical perception and formulate new guidelines for visualization design. We proposed new experiments to address unresolved issues in graphical perception. We employed both large-scale web-based studies using crowdsourcing techniques and controlled laboratory studies using sensitive measurements. We applie the results of these studies to (a) develop guidelines for effective visualization design, (b) instantiate these guidelines in automated design procedures, and (c) build new computational models for extracting data from existing visualizations. Major outcomes of the work include - A methodology for using paid crowdsourcing to run graphical perception experiments. - Guidelines for producing more visually effective treemap visualizations. - Guidelines for using choosing colors when encoding data in a visualization. - An taxonomy for storytelling and presenting narratives in visualizations. - Techniques for extracting data and marks from a bitmap of a visualization. - Methods for converting an bitmap of a visualization into an interactive diagram through graphical overlays. Design guidelines are commonly stated as high-level rules of thumb for human designers. People can choose how they interpret and apply the guidelines to produce effective designs. Our experimentshave yielded a set of such guidelines that anyone, even novice designers, can apply to create their own visualizations. By further formalizing the guidelines into new algorithms that automate visualization design, we also enable non-designers to more easily create effective visualizations as part of their analysis. Finally, by extracting data from existing visualizations and then producing new, more effective visualizations based on the guidelines our tools can help users communicate information more effectively. The publications, methodologies, tools and techniques we havedeveloped for this project are all publicly available at our websites: http://vis.berkeley.edu http://idl.cs.washington.edu
