Focus+context grouping for animated transitions

Abstract Animation is a commonly used technique in information visualization for smooth transitions between different views. When observing animations of moving objects, people often need to track several specific objects while identify the major trend of movement simultaneously. In this paper, we propose a novel focus+context grouping technique to facilitate target tracking and trend identification. It divides objects into several groups based on a comprehensive tree cut algorithm and generates a staggering animation in which groups are animated sequentially. A balance between efficiency and accuracy is achieved for an effective animation planning. To evaluate the effectiveness of the proposed technique, a carefully designed user study is conducted. The results indicate that focus+context grouping is effective for users to track targets without losing context (i.e., major trend of movement). Based on the study, we discuss advantages and limitations of the proposed grouping technique and conclude with design implications.