In this paper, we present a data-driven framework to support exploratory spatial, temporal, and statistical analysis of intra-urban human mobility. We leveraged a new mobility data source, the dockless bike-sharing service Mobike, to quantify short-trip transportation patterns in Shanghai, China, the world’s largest bike-share city. A data-driven framework was established to integrate multiple data sources, including transportation network data (roads, bikes, and public transit), road characteristics, and urban land use, to achieve a detailed, accurate analysis of cycling patterns at both the individual and group levels. The results provide a comprehensive view of mobility patterns in the use of shared-ride bicycles, including: (1) the temporal and spatiotemporal distribution of shared-bike usage and how this varies according to different land use; (2) the statistical distribution of Mobike trips, which are primarily short-distance; and (3) the travel behavior and road factors that influence Mobike users’ route choice. The findings offer valuable insights for city planners regarding infrastructure development, for shared-ride bike companies to offer better bike rebalancing strategies to meet user demand, and for the promotion of this new green transportation mode to alleviate traffic congestion and enhance public health.