  Urban research has long recognized that neighbourhoods are dynamic and
relational. However, lack of data, methodologies, and computer processing power
have hampered a formal quantitative examination of neighbourhood relational
dynamics. To make progress on this issue, this study proposes a graph neural
network (GNN) approach that permits combining and evaluating multiple sources
of information about internal characteristics of neighbourhoods, their past
characteristics, and flows of groups among them, potentially providing greater
expressive power in predictive models. By exploring a public large-scale
dataset from Yelp, we show the potential of our approach for considering
structural connectedness in predicting neighbourhood attributes, specifically
to predict local culture. Results are promising from a substantive and
methodologically point of view. Substantively, we find that either local area
information (e.g. area demographics) or group profiles (tastes of Yelp
reviewers) give the best results in predicting local culture, and they are
nearly equivalent in all studied cases. Methodologically, exploring group
profiles could be a helpful alternative where finding local information for
specific areas is challenging, since they can be extracted automatically from
many forms of online data. Thus, our approach could empower researchers and
policy-makers to use a range of data sources when other local area information
is lacking.
