As urban crimes (e.g., burglary and robbery) negatively impact our everyday life and must be addressed in a timely manner, predict- ing crime occurrences is of great importance for public safety and urban sustainability. However, existing methods do not fully ex- plore dynamic crime patterns as factors underlying crimes may change over time. In this paper, we develop a new crime prediction framework–DeepCrime, a deep neural network architecture that uncovers dynamic crime patterns and carefully explores the evolv- ing inter-dependencies between crimes and other ubiquitous data in urban space. Furthermore, our DeepCrime framework is capa- ble of automatically capturing the relevance of crime occurrences across different time periods. In particular, our DeepCrime frame- work enables predicting crime occurrences of different categories in each region of a city by i) jointly embedding all spatial, temporal, and categorical signals into hidden representation vectors, and ii) capturing crime dynamics with an attentive hierarchical recurrent network. Extensive experiments on real-world datasets demon- strate the superiority of our framework over many competitive baselines across various settings.