On the Optimization of Visualizations of Complex Phenomena

The problem of perceptually optimizing complex visualizations is a difficult one, involving perceptual as well as aesthetic issues. In our experience, controlled experiments are quite limited in their ability to uncover interrelationships among visualization parameters, and thus may not be the most useful way to develop rules-of-thumb or theory to guide the production of high-quality visualizations. In this paper, we propose a new experimental approach to optimizing visualization quality that integrates some of the strong points of controlled experiments with methods more suited to investigating complex highly-coupled phenomena. We use human-in-the-loop experiments to search through visualization parameter space, generating large databases of rated visualization solutions. This is followed by data mining to extract results such as exemplar visualizations, guidelines for producing visualizations, and hypotheses about strategies leading to strong visualizations. The approach can easily address both perceptual and aesthetic concerns, and can handle complex parameter interactions. We suggest a genetic algorithm as a valuable way of guiding the human-in-the-loop search through visualization parameter space. We describe our methods for using clustering, histogramming, principal component analysis, and neural networks for data mining. The experimental approach is illustrated with a study of the problem of optimal texturing for viewing layered surfaces so that both surfaces are maximally observable

Perceptually optimizing textures for layered surfaces

In this paper, we take a new look at the problem of texturing surfaces so that they can be displayed layered over each other but remain clearly visible. Finding optimal textures that solve this problem is complex because of the perceptual interactions between the visual effects of parameters controlling texture generation. Instead of using controlled experiments to investigate this problem, we use a genetic algorithm based human-in-the-loop parameter space search to build a large database of human-rated textures. This database is then analyzed with a varity of datamining techniques, including clustering, principle component analysis, neural networks, and histogram analysis. We detail this analysis, concluding with a set of guidelines for building strong layered surface textures, and a display of a number of example textures