Today's mobile information society depends increasingly on the use of Information and Communication Technologies (ICTs) such as mobile phones. A thorough understanding of the correlation between mobile phone usage and human mobility will help in predicting people's mobility patterns and therefore provide important guidelines for maintaining sustainable transportation, updating environmental policies, and designing early warning and emergency response systems.

This dissertation develops a generalizable framework for extracting and characterizing human mobility patterns from georeferenced mobile phone datasets. It begins with analyzing the different types of information that can be stored in mobile phone datasets. Extended human mobility models and data mining methodologies are developed for spatio-temporal knowledge discovery based on mobile phone data. These models provide the basis for investigating and quantifying the correlation between human physical travel, communication travel, and environmental structure from two perspectives: individual-oriented and urban-oriented. This research also addresses issues of uncertainty, which arise from the natural variability of human mobility, the inaccuracy and imprecision of recorded trajectories, and the imperfection of the underlying models. Finally, a large dataset of northeast China is utilized for a comprehensive evaluation of the developed methods and models and the correlation between spatial structure, human mobility patterns, and mobile phone usage. The intellectual merits of this research include a generalization UML model, algorithms to model movement patterns, and various empirical studies. The results of this research will provide references for policy makers and urban planners to understand the characteristics of individual mobility with wide spread ICT usage, as well as updating environmental and transportation policies.

This research enhances our understanding of the relationship between human mobility and ICT in general, and between human mobility patterns and mobile phone usage in particular. In addition, it advances conventional geographic knowledge discovery by focusing on knowledge extraction from sparse datasets with low resolution and individual attributes. The case study from northeast China allows us to examine the influence of mobile phone usage in a highly populated and rapidly developing country.