CHS: Small: Collaborative Research: Representing and Learning Visualization Design Knowledge

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This project contributes new methods and software tools for creating data-driven visualizations that improve the clarity and effectiveness of visual analysis and communication of data. Many visualization design guidelines, like "avoid highly saturated colors", or "start bars in a bar chart at 0", stem from empirical studies of how well people can read visualizations of various types. However, these guidelines are often stated informally in books or articles. In designing a visualization, an author may have to make decisions that prioritize one design guideline over another, yet the informal nature of such principles does not provide sufficient guidance for how to do this. Even when visualization researchers and system designers represent design guidelines in more formal "knowledge bases" that an authoring system can use to guide visualization authors towards more effective graphs, the guidelines are based on a person carefully summarizing the empirical results, an error-prone process. This project addresses these challenges to formulating and applying visualization design knowledge by creating new methods to identify, aggregate, edit, test, and search visualization design knowledge. This research will also address gaps in existing visualization design knowledge, applying novel methods to formulate and assess design guidelines for creating effective "multiple-view" visualizations (such as analysis dashboards or sequential presentations), visualizing very large datasets, and visually expressing uncertainty or error in data. We will create knowledge bases containing guidelines for these types of visualizations as well as an authoring tool to help authors manage competing design considerations between single and multiple views when designing visualizations like dashboards. All experimental results, knowledge bases, and authoring tools developed in this research will be made freely and publicly available.To meet these goals, this project develops a set of methods for identifying and evaluating visualization design guidelines from empirical research on visualization perception and interpretation. To do this, the team will develop ways to re-express existing results from relevant experimental literature on graphical perception and cognition as constraints, and create new methods and tools for directly eliciting design guidelines from visualization experts such as skilled designers or researchers. The project will also produce automated methods for generating visualizations and collecting task-specific visualization judgments in order to learn appropriate priority weights for a given set of design constraints. By developing representations and models for capturing empirical results that can account for the uncertainty that is inherent in results from human subjects experiments, the project stands to synthesize and clarify existing empirical knowledge about visualization design. The research will also advance the state of the art in visualization design knowledge by contributing fundamental methods for (1) identifying and learning guidelines for large dataset visualizations, multiple view visualizations like dashboards, and uncertainty visualizations, and (2) exploring effective interface designs for browsing, editing, and testing visualization knowledge bases.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.