Interaction is becoming an integral part of using visualization for analysis. When interaction is tightly and appropriately coupled with visualization, it can transform the visualization from display- ing static imagery to assisting comprehensive analysis of data at all scales. In this relationship, a deeper understanding of the role of interaction, its effects, and how visualization relates to interaction is necessary for designing systems in which the two components complement each other.
This thesis approaches interaction in visualization from three different perspectives. First, it considers the cost of maintaining interaction in manipulating visualization of large datasets. Namely, large datasets often require a simplification process for the visualization to maintain interactivity, and this thesis examines how simplification affects the resulting visualization. Secondly, example interactive visual analytical systems are presented to demonstrate how interactivity could be applied in visualization. Specifically, four fully developed systems for four distinct problem domains are discussed to determine the common role of interactivity in these visualizations that make the systems successful. Lastly, this thesis presents evidence that interactions are important for analytical tasks using visualizations. Interaction logs of financial analysts using a visualization were collected, coded, and examined to determine the amount of analysis strategies contained within the interaction logs. The finding supports the benefits of high interactivity in analytical tasks when using a visualization.
The example visualizations used to support these three perspectives are diverse in their goals and features. However, they all share similar design guidelines and visualization principles. Based on their characteristics, this thesis groups these visualizations into urban visualization, visual analytical systems, and interaction capturing and discusses them separately in terms of lessons learned and future directions
