Constructing Frameworks for Task-Optimized Visualizations

Visualization is crucial in today’s data-driven world to augment and enhance human understanding and decision-making. Effective visualizations must support accuracy in visual task performance and expressive data communication. Effective visualization design depends on the visual channels used, chart types, or visual tasks. However, design choices and visual judgment are co-related, and effectiveness is not one-dimensional, leading to a significant need to understand the intersection of these factors to create optimized visualizations. Hence, constructing frameworks that consider both design decisions and the task being performed enables optimizing visualization design to maximize efficacy. This dissertation describes experiments, techniques, and user studies to model user perception for visualization design optimization and data transformation for low-level visual tasks. To begin with, I identify the limitations through a taxonomized state-of-the-art survey on perception-based visualization studies focusing on how visualization effectiveness is task-dependent.With a specific focus on the scatterplot, I developed perceptual models for cluster perception and design optimization. In addition to design guidelines from the first experiments, I employ the findings to show design choices based on the visual density of the scatterplot could influence the user’s judgment on visual tasks. Further, I address the challenge of assessing line chart smoothing effectiveness for a range of analytical tasks. Finally, I elaborate on utilizing the framework to provide less ambiguous data presentations, leading to better quality and higher confidence in decision-making