Without doubt, we are in the midst of a data explosion. A variety of data tracking human mobility, namely human mobility data, has been generated and collected within urban context, providing unprecedented opportunities to understand regional dynamics in urban area, which is of great social and business value in a variety of applications. However, due to large volumes and dynamic correlations of these data as well as high complexity of analytical tasks in real world applications, it is challenging for analysts to carry out in-depth analysis and extract valuable information. It often requires integrating human perception in the data exploration process, triggering a broad use of visual analytics. With visual analytics, we can include human perception in the data exploration proc...[ Read more ]

Without doubt, we are in the midst of a data explosion. A variety of data tracking human mobility, namely human mobility data, has been generated and collected within urban context, providing unprecedented opportunities to understand regional dynamics in urban area, which is of great social and business value in a variety of applications. However, due to large volumes and dynamic correlations of these data as well as high complexity of analytical tasks in real world applications, it is challenging for analysts to carry out in-depth analysis and extract valuable information. It often requires integrating human perception in the data exploration process, triggering a broad use of visual analytics. With visual analytics, we can include human perception in the data exploration process efficiently and combine the flexibility, creativity and domain knowledge of human beings with enormous storage capacity and computational power of today’s computers.

In this thesis, we introduce three advanced visual analysis techniques for uncovering regional dynamics in urban area from different aspects based on heterogeneous human mobility data. In particular, we first study the subject matter of regional boundary change and present BoundarySeer. It is a visual analytics system consisting of four major viewers to facilitate the general analytical tasks dealing with boundary changes of a region in urban area. Secondly, a visual analytics system, TelCoVis, is presented to facilitate the exploration of co-occurrence in human mobility (i.e. people from two regions visit an urban place during the same time span) and hidden correlations based on telco data. The system integrates a novel contour-based treemap with extended visualization techniques to enhance analysts’ perception for a comprehensive exploration of coordinated relationships among different regions and identify interesting patterns. The third study proposes a novel visual analysis approach to investigate people’s activity patterns for an interactive region segmentation based on three types of heterogeneous mobility data (i.e. taxi trajectories, metro passenger RFID card records and telco data). Combining advanced visualization techniques (e.g. NMF-based method to capture latent activity patterns, as well as metric learning to calibrate and supervise the underlying analysis) with intuitive visual designs (e.g. a voronoi-based texture map with elliptical activity glyphs to summarize people’s activities and enable a fast comparison), MobiSeg not only makes it easier for domain experts to perform a series of analyses on region segmentation, but also enables a new way to explore data from multiple levels and perspectives.

To the best of our knowledge, the above techniques are cutting-edge studies of visually analyzing regional dynamics in urban area based on heterogeneous human mobility data. To validate the effectiveness and usefulness of our study, all the proposed techniques and systems are deployed to analyze real-world datasets and evaluated by domain experts or target users.