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During the past few years, the analysis of data generated from Location-Based Social Networks (LBSNs) have aided in the identification of urban patterns, understanding activity behaviours in urban areas, as well as producing novel recommender systems that facilitate users’ choices. Recognizing crowd-mobility patterns in cities is very important for public safety, traffic managment, disaster management, and urban planning. In this article, we propose a framework for Recognizing the Crowd Mobility Patterns in Cities using LBSN data. Our proposed framework comprises four main components: data gathering, recurrent crowd-mobility patterns extraction, temporal functional regions detection, and visualization component. More specifically, we employ a novel approach based on Non-negative Matrix Factorization and Gaussian Kernel Density Estimation for extracting the recurrent crowd-mobility patterns in cities …