Neighborhoods are important contexts in shaping interethnic group relationships and sites in which these may materialize through everyday routines in shared local spaces. In this paper, we approach neighborhoods as a small-scale set of spaces of encounter, defined as local public or semi-public spaces, where residents of different ethnic backgrounds may meet. Relying on the classical contact and group threat theories, the main assumption is that local spaces of encounter are facets of an intergroup neighborhood context and may shape intergroup relations, defined as perceived ethnic threat and intergroup friendship. Drawing on the georeferenced survey data from the Belgian National Election Study 2020 enriched with spatial features from OpenStreetMap, an innovative big geospatial data source, and census-based neighborhood characteristics, the study employs machine learning algorithms to investigate whether, which, and how neighborhood spaces of encounter can predict perceived ethnic threat and intergroup friendship, while also taking into account traditional local ethnic, socioeconomic, and individual indicators. By using OpenStreetMap to measure spaces of encounter as a novel neighborhood indicator, we develop a fine-grained typology of local spaces that is rooted in urban and intergroup relations research. The results show that for predicting intergroup friendship, the important spaces were educational, functional, public open, and user-selecting spaces, while for predicting threat functional, third, retail, and other spaces stood out prediction-wise. The results also revealed the predictive importance of individual characteristics for intergroup relations, while neighborhood characteristics were not so important, both in absolute and relative terms. We conclude by reflecting on what local spaces might matter and discuss the combination of OpenStreetMap and intergroup relations as a proof of concept and prospects for future research.

中文翻译：

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使用 OpenStreetMap、人口普查和调查数据预测比利时的种族间关系：一种机器学习方法


社区是塑造族群间关系的重要环境，也是这些关系可以通过共享当地空间的日常生活得以实现的场所。在本文中，我们将社区视为一组小规模的相遇空间，定义为当地公共或半公共空间，不同种族背景的居民可以在这里相遇。依靠经典的接触和群体威胁理论，主要假设是局部相遇空间是群体间邻里环境的各个方面，可能会塑造群体间关系，定义为感知的种族威胁和群体间友谊。该研究利用 2020 年比利时全国选举研究中的地理参考调查数据，丰富了 OpenStreetMap（一种创新的大地理空间数据源）的空间特征以及基于人口普查的邻里特征，采用机器学习算法来调查邻里空间是否、哪些以及如何遭遇的次数可以预测感知到的种族威胁和群体间友谊，同时还考虑到传统的当地种族、社会经济和个人指标。通过使用 OpenStreetMap 作为一种新颖的邻里指标来测量相遇空间，我们开发了一种植根于城市和群体间关系研究的细粒度局部空间类型学。结果表明，对于预测群体间友谊，重要的空间是教育、功能、公共开放和用户选择空间，而对于预测威胁，功能、第三、零售和其他空间在预测方面表现突出。结果还揭示了个体特征对群体间关系的预测重要性，而邻里特征无论是在绝对值还是相对值上都不那么重要。 最后，我们反思了哪些局部空间可能很重要，并讨论了 OpenStreetMap 和群体间关系的结合，作为概念证明和未来研究的前景。