Movement is a fundamental phenomenon exists ubiquitously in our daily life. Many crucial scientific, societal and commercial decisions are made depending on proper knowledge and correct understanding of movement patterns of people, animals or objects. However, analyzing and exploring movement is not an easy task due to its intrinsic multi-variate natures, hidden correlations among properties and complex analytical tasks in real world applications. Therefore, analysts seek the help of visualization to integrate humans in the data exploration process, applying their perceptual abilities to target datasets and leveraging their domain knowledge to guide the exploration for effective understanding, reasoning and decision making. Movement visualization has been widely studied in recent years...[ Read more ]

Movement is a fundamental phenomenon exists ubiquitously in our daily life. Many crucial scientific, societal and commercial decisions are made depending on proper knowledge and correct understanding of movement patterns of people, animals or objects. However, analyzing and exploring movement is not an easy task due to its intrinsic multi-variate natures, hidden correlations among properties and complex analytical tasks in real world applications. Therefore, analysts seek the help of visualization to integrate humans in the data exploration process, applying their perceptual abilities to target datasets and leveraging their domain knowledge to guide the exploration for effective understanding, reasoning and decision making. Movement visualization has been widely studied in recent years concerning about novel and effective visual designs and user interactions for analysts to gain insight into real world datasets. In this thesis, we follow this line of research and propose three visualization techniques or visual analysis approaches to facilitate in-depth analyses of bi-directional and cluster movements.

In the first work, we propose a visual analytics system to investigate bi-directional movement behaviors. More specifically, we first design a movement model with modular DoI specification characterizing bi-directional movement. Then several novel visualization designs, including OD-pair Flow View and Isotime Storyline View are proposed with intuitive user interactions to allow users to interactively explore and analyze both micro and macro patterns of bi-directional movement behaviors.

In the second work, we develop TelcoFlow, a visual analytics system to study collective behaviors based on the large scale of telco data. Advanced quantitative analyses including state-based probabilistic behavior model and biclustering are utilized to quantify and detect collective behaviors. Meanwhile, a set of intuitive visualization techniques with new designs is integrated to present the detected patterns for an in-depth analysis.

In the third work, we present a novel animation planning technique, namely Focus+Context Grouping, that can allow users to track movement of focal targets while not neglecting the context (i.e. the overall moving trend). It can be integrated with static visualizations to provide a more straightforward presentation of movement and improve users’ performance on movement analysis. In particular, a novel tree cut algorithm is proposed to cluster moving objects and balance between efficiency and accuracy based on the key factors affecting people’s perception of animation.

To evaluate the effectiveness and usefulness of the proposed techniques in facilitating various analytical tasks, we conduct several case studies based on real-world datasets, one user study for Focus+Context Grouping technique, and collect feedback from domain experts.