Research Article Discovering Spatial Patterns in Origin-Destination Mobility Data Diansheng Guo Department of Geography University of South Carolina Xi Zhu Department of Geography University of South Carolina and School of Hydropower and Information Engineering Huazhong University of Science and Technology Hai Jin Department of Geography University of South Carolina Peng Gao Department of Geography University of South Carolina Clio Andris Department of Urban Studies and Planning Massachusetts Institute of Technology Abstract Mobility and spatial interaction data have become increasingly available due to the wide adoption of location-aware technologies. Examples of mobility data include human daily activities, vehicle trajectories, and animal movements, among others. In this article we focus on a special type of mobility data, i.e. origin-destination pairs, and present a new approach to the discovery and understanding of spatio-temporal patterns in the movements. Specifically, to extract information from complex connec- tions among a large number of point locations, the approach involves two steps: (1) spatial clustering of massive GPS points to recognize potentially meaningful places; and (2) extraction and mapping of the flow measures of clusters to understand the spatial distribution and temporal trends of movements. We present a case study with a large dataset of taxi trajectories in Shenzhen, China to demonstrate and evaluate the methodology. The contribution of the research is two-fold. First, it presents a new methodology for detecting location patterns and spatial structures embedded in Address for correspondence: Diansheng Guo, Department of Geography, University of South Carolina, 709 Bull Street, Columbia SC 29208, USA. E-mail: GUOD@mailbox.sc.edu Transactions in GIS, 2012, 16(3): 411–429 © 2012 Blackwell Publishing Ltd doi: 10.1111/j.1467-9671.2012.01344.x