The huge amount of check-in data obtained through location-based social networks (LBSNs) provides a great opportunity to learn the characteristics of a geographic area through the users' collective check-in behavior. In this paper, we explore structure analysis of place networks, in which vertices are geographic places while the links between places are formed based on the user's check-in history. Specifically, we apply Louvain community detection algorithm on the place networks to obtain a set of place clusters (or communities). We found that these communities can be used to discover geographic layouts in the city and can uncover interesting patterns by analyzing geographically distant places within the same community. These results suggest that structural analysis of place networks is a promising approach for urban computing and has many implications of mobile networking, urban planning, user profiling and travel recommendations.