HCC: Small: Collaborative Research: Graphical Perception Revisited: Developing and Validating Design Guidelines for Data Visualization

University Of California-Berkeley, Berkeley CA

Investigators

Abstract

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.