  In urban spatial networks, there is an interdependency between neighborhood
roles and the transportation methods between neighborhoods. In this paper, we
classify docking stations in bicycle-sharing networks to gain insight into the
human mobility patterns of three major United States cities. We propose novel
time-dependent stochastic block models (SBMs), with degree-heterogeneous blocks
and either mixed or discrete block membership, which classify nodes based on
their time-dependent activity patterns. We apply these models to (1) detect the
roles of bicycle-sharing docking stations and (2) describe the traffic within
and between blocks of stations over the course of a day. Our models
successfully uncover work, home, and other districts; they also reveal activity
patterns in these districts that are particular to each city. Our work has
direct application to the design and maintenance of bicycle-sharing systems,
and it can be applied more broadly to community detection in temporal and
multilayer networks with heterogeneous degrees.
