In this paper, we presented a method for the structural analysis of a city, specifically for the determination of its functional areas, based on communities detection in graph. The nodes of the graph correspond to geographical units resulting from a cartographic division of the city according to the road network. The edges are weighted using a Gaussian distance-decay function and the amount of spatial interactions between nodes. Our approach optimize the modularity to ensure that the functional areas detected have strong interactions within their borders but lower interactions outside. Moreover, it leverages on POIs' entropy to maintain a good socioeconomic heterogeneity in the detected areas. We conducted experiments using taxi trips and POIs datasets from the city of Porto, as a study case, to demonstrate the ability of our method to portray functional areas while including spatial and socioeconomic dynamics.