Thanks to the open data campaign, urban data are becoming increasingly ubiquitous, which provides vast opportunities for addressing challenging issues through big data analysis. Furthermore, most of the urban data exhibits spatial and temporal features that are widely used in solving severe urban problems, including urban planning and traffic management. However, when urban analysts make decisions on solving such issues, they may be limited by an unintuitive representation for understanding the urban environment, needing more interpretation of the data-driven model. Therefore, understanding the urban data so as to understand the urban environment and then making proper decisions is one of the goals of urban analysts. The visual analytics approach has recently been widely used to assist...[ Read more ]

Thanks to the open data campaign, urban data are becoming increasingly ubiquitous, which provides vast opportunities for addressing challenging issues through big data analysis. Furthermore, most of the urban data exhibits spatial and temporal features that are widely used in solving severe urban problems, including urban planning and traffic management. However, when urban analysts make decisions on solving such issues, they may be limited by an unintuitive representation for understanding the urban environment, needing more interpretation of the data-driven model. Therefore, understanding the urban data so as to understand the urban environment and then making proper decisions is one of the goals of urban analysts. The visual analytics approach has recently been widely used to assist analysts in solving urban problems with its intuitiveness, interactivity, and interpretability. Aiming to help analysts better understand the urban environment, this thesis proposed visual analytics approaches from three aspects respectively:

1. Representing and analyzing the urban movement data: The moving objects in the urban environment are usually limited by the complex urban infrastructure (i.e., the road network); that is, the two geographically distant locations may have a short commute time due to road connectivity. Therefore, visualizing and analyzing the urban movement data cannot be limited to measuring the distance between two locations by Euclidean distance. To this end, a new method named Topology Density Map is proposed, targeting accurate and intuitive density maps in the context of the urban environment.

2. Providing transparency to the urban traffic prediction models: The spatial and temporal features of urban movement data usually play an important role in traffic flow prediction. However, most prediction models are indeed “black boxes" which lack transparency. To convince the users when predicting the traffic and revealing the impact on the traffic from surroundings, a visual analytics approach named TrafPS is proposed, which includes two measurements named region SHAP and trajectory SHAP. Moreover, a visual analytics interface is developed to support the users in multi-level analysis for understanding traffic prediction and making decisions.

3. Modeling and exploring the urban higher-order movement: Higher-order patterns in the urban environment reveal sequential multi-step state transitions, usually superior to origin-destination (OD) analysis, which depicts only first-order geospatial movement patterns. However, conventional DAG-based movement modeling usually exhibits sparseness and ignores considering the temporal variants that are critical for movements in urban environments. Therefore, HoLens is firstly proposed for modeling and visualizing higher-order movement patterns in the context of an urban environment.

This thesis contributes to the VIS community with novel urban movement data processing algorithms, visualization techniques, and interactive visual analytics tools. Case studies with real-world dataset and interviews with domain experts are conducted to demonstrate the feasibility, usability, and effectiveness of the proposed three studies in this thesis.