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.

翻译：城市研究长期认识到社区是动态且相互关联的。然而，数据、方法及计算机处理能力的匮乏阻碍了对社区关系动态的正式定量研究。为解决这一问题，本研究提出了一种图神经网络（GNN）方法，该方法能够整合并评估关于社区内部特征、历史特征以及群体间流动的多源信息，有望在预测模型中提供更强的表达能力。通过探索来自Yelp的公开大规模数据集，我们展示了该方法在预测社区属性（特别是地方文化）时考虑结构连通性的潜力。从实质和方法论角度看，结果均具有前景。在实质层面，我们发现局部区域信息（如区域人口统计特征）或群体画像（Yelp评论者的品味）在预测地方文化时均能获得最佳结果，且在所有研究案例中两者效果几乎等同。在方法论层面，探索群体画像可作为有效的替代方案——当特定区域难以获取本地信息时，由于群体画像能够从多种形式的在线数据中自动提取，该方法尤为实用。因此，我们的方法能使研究人员和决策者在缺乏其他局部区域信息时，能够利用多种数据源。