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：城市研究长期以来认识到邻里是动态且相互关联的。然而，数据、方法学和计算机处理能力的缺乏阻碍了对邻里关系动态的正式定量研究。为推进这一问题，本研究提出一种图神经网络（GNN）方法，该方法能够结合并评估关于邻里内部特征、历史特征以及群体间流动的多源信息，在预测模型中可能提供更强的表达能力。通过探索Yelp提供的公开大规模数据集，我们展示了该方法在考虑结构连通性预测邻里属性（特别是预测地方文化）方面的潜力。从实质和方法论角度看，结果令人期待。实质层面，我们发现地方区域信息（如区域人口统计）或群体画像（Yelp评论者的口味）在预测地方文化方面均能取得最佳结果，且在所有研究案例中几乎等价。方法论层面，探索群体画像可作为在特定区域获取地方信息困难时的有效替代方案，因其可通过多种在线数据形式自动提取。因此，当缺乏其他地方区域信息时，我们的方法可赋能研究人员和政策制定者使用多种数据源。