The way people live in cities forms human activity patterns, which affects how urban systems work. Therefore, it is essential to understand human activity patterns, where precise prediction of human movements and mechanistic modelling of human activity patterns are the two keys. Most of existing work on prediction of human movements cannot deal with activity changes, leading to a negative impact on the predictive accuracy. Furthermore, the majority of current work on modelling human activity patterns are mainly researched from spatiotemporal perspectives, but the motivation behind is usually being neglected, which is crucial to understanding activity changes. The objective of this study is to develop models and methods to better understand human activity patterns using crowdsourcing and geosocial media data. Thus, in this thesis, a method is first developed to detect activity changes, based on which a Markov chain-based model is developed to predict human movements. Then, semantics is introduced to uncover the motivation associated with the corresponding spatiotemporal patterns, which can infer what people do and discuss in a location at a specific time. Finally, human activity patterns are modelled from both spatiotemporal and semantic perspectives.
A 6-year GPS dataset of human movement in Beijing, China was used to evaluate the proposed predictive model. The results show that the predictive model can yield accurate prediction of the movement for those users who have significant activity changes (with R2 improved from 0.295 to 0.762). A whole-year geo-tagged tweets posted within Toronto, Canada was acquired to analyze human activity patterns. A network model was finally created by the proposed approach to represent human activity patterns. The experimental findings demonstrate that most of the individuals (61%) have a regular activity pattern, while only a small number of people (10%) have a different activity pattern from the mass. With the inclusion of semantic information together with the spatiotemporal data as well as detecting the activity changes, such an approach can enhance the capability of human mobility and activity modelling, and thus pave the way for a more mechanistic understanding of how urban systems are being shaped, as well as how their sub-systems/components interact.