VISUAL ANALYTICS OF BI-DIRECTIONAL AND CLUSTER MOVEMENTS
The Hong Kong University of Science and Technology
Department of Computer Science and Engineering
PhD Thesis Defence
Title: "VISUAL ANALYTICS OF BI-DIRECTIONAL AND CLUSTER MOVEMENTS"
Mr. Yixian ZHENG
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 ODpair 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