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.

该研究提出了一种图神经网络（GNN）方法，用于结合和评估关于社区内部特征、过去特征和群体流动的多个信息来源，从而在预测模型中提供更高的表达能力。通过探索来自 Yelp 的公共大规模数据集，我们展示了我们的方法在考虑结构联系以预测社区属性方面的潜力，特别是预测当地文化。研究结果在实质和方法上都具有潜力，发现当地区信息（如地区人口统计信息）或群体特征（Yelp 评论者的口味）在预测当地文化方面效果最佳，并且在所有研究案例中几乎相当。从方法论上来看，探索群体特征可能是一个有帮助的替代方法，当获取特定地区的本地信息具有挑战性时，因为它们可以从许多形式的在线数据中自动提取。因此，我们的方法能够在缺乏其他本地区域信息时，为研究人员和决策者使用一系列数据来源提供支持。