We investigate changes in patterns of usage in the Divvy bikeshare system in Chicago from 2016-2018. We devise a community detection method that finds clusters of nodes that are increasing, decreasing, or stable in connectivity across time. The method is based on an iterative testing approach that is augmented by trend testing and a novel time-dependent false-discovery-rate correction. In addition, we introduce a method of correcting for estimated forgone trips due to full or empty stations in high-activity areas. Results show stark geographical patterns in clusters that are growing and declining in relative bike-share usage across time and may elucidate latent economic or demographic trends.