We apply a novel clustering technique to London’s bikesharing network, deriving distinctive behavioral patterns and assessing community interactions and spatio-temporal dynamics. The analyses reveal self- contained, interconnected and hybrid clusters that mimic London’s physical structure. Exploring changes over time, we find geographically isolated and specialized communities to be relatively consistent, while the remaining system exhibits volatility. We increase understanding of the collective behavior of the bikesharing users.