The increasing availability of data in the urban context (e.g., mobile phone, smart card and social media data) allows us to study urban dynamics at much finer temporal resolutions (e.g., diurnal urban dynamics). Mobile phone data, for instance, are found to be a useful data source for extracting diurnal human mobility patterns and for understanding urban dynamics. While previous studies often use call detail record (CDR) data, this study deploys aggregated network-driven mobile phone data that may reveal human mobil-ity patterns more comprehensively and can mitigate some of the privacy concerns raised by mobile phone data usage. We first propose an analytical framework for characterizing and classifying urban areas based on their temporal activity patterns extracted from mobile phone data. Specifically, urban areas' diurnal spatiotemporal signatures of human mobility patterns are obtained through longitudinal mobile phone data. Urban areas are then classified based on the obtained signatures. The classification provides insights into city plan-ning and development. Using the proposed framework, a case study was implemented in the city of Wuhu, China to understand its urban dynamics. The empirical study suggests that human activities in the city of Wuhu are highly concentrated at the Traffic Analysis Zone (TAZ) level. This large portion of local activities suggests that development and planning strategies that are different from those used by metropolitan Chinese cities should be applied in the city of Wuhu. This article concludes with discussions on several common chal-lenges associated with using network-driven mobile phone data, which should be addressed in future studies.