Mapping urban segregation through co-residence network reconstruction

Abstract

Urban segregation poses a critical challenge for cities, exacerbating inequalities, social tensions, fears, and polarisation. It emerges from the interplay of socio-economic disparities, housing constraints, and residential preferences, and can disproportionately affect migrant communities. Here, we study residential segregation in Vienna using a city-wide administrative snapshot of registered residents, covering the full foreign population and Austrian nationals at the district level. We introduce a network-based approach by constructing a statistically validated co-residence network in which nodes represent nationalities and links capture whether pairs of groups live in the same districts more or less often than expected under a population-size-preserving null model. Applying community detection to this network reveals two major clusters of nationalities with distinct co-residence patterns. These clusters are systematically associated with district-level income disparities and diversity, while also reflecting the geographical proximity of countries of origin, with nationalities from nearby regions tending to share similar residential patterns within Vienna. Our results show how network methods can provide an intuitive and interpretable map of urban residential sorting, complementing traditional segregation indices and highlighting the multiple dimensions underlying migrant integration in diverse cities.