  Modelling the dynamics of urban venues is a challenging task as it is
multifaceted in nature. Demand is a function of many complex and nonlinear
features such as neighborhood composition, real-time events, and seasonality.
Recent advances in Graph Convolutional Networks (GCNs) have had promising
results as they build a graphical representation of a system and harness the
potential of deep learning architectures. However, there has been limited work
using GCNs in a temporal setting to model dynamic dependencies of the network.
Further, within the context of urban environments, there has been no prior work
using dynamic GCNs to support venue demand analysis and prediction. In this
paper, we propose a novel deep learning framework which aims to better model
the popularity and growth of urban venues. Using a longitudinal dataset from
location technology platform Foursquare, we model individual venues and venue
types across London and Paris. First, representing cities as connected networks
of venues, we quantify their structure and note a strong community structure in
these retail networks, an observation that highlights the interplay of
cooperative and competitive forces that emerge in local ecosystems of retail
businesses. Next, we present our deep learning architecture which integrates
both spatial and topological features into a temporal model which predicts the
demand of a venue at the subsequent time-step. Our experiments demonstrate that
our model can learn spatio-temporal trends of venue demand and consistently
outperform baseline models. Relative to state-of-the-art deep learning models,
our model reduces the RSME by ~ 28% in London and ~ 13% in Paris. Our approach
highlights the power of complex network measures and GCNs in building
prediction models for urban environments. The model could have numerous
applications within the retail sector to better model venue demand and growth.
