Detecting and characterizing geographical areas that are attractive places for specific people, in specific contexts, is an important but challenging new problem. Mobility traces and their related circumstances can be modeled thanks to an augmented graph in which nodes denote geographic locations and edges are represented by a set of transactions that describe users' demographic information (e.g. age, gender, etc.) as well as the conditions of the movement (e.g. day/night, holiday , transportation mode, etc.). We propose to extract connected subgraphs that are related to some user profiles, and use it to understand the usages of the Vélo 'v bike sharing system.