Co-location networks are a special case of two-mode networks, or bipartite graphs, where ties can only exist between the two modes: individuals and locations. The goal of analyzing co-location networks is to simultaneously study the "dual" association between individuals and the geographic locations where they spend time. In this presentation, we present a Bayesian hierarchical model that allows us to achieve this goal. Our approach can be viewed as a model-based analogue to non-negative matrix factorization (NMF). In particular, we assume that there exists a collection of latent variables, which define "communities" that simultaneously identify groups of individuals who frequent the same locations and groups of locations that are visited by the same people. We illustrate our model via simulation and using data from the Adolescent Health and Development in Context Study.