Using OpenStreetMap, Census, and Survey Data to Predict Interethnic Group Relations in Belgium: A Machine Learning Approach

Abstract

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.