Network science maps urban segregation by nationality in Vienna

A preprint from arXiv (2501.15920) introduces a novel method for mapping urban segregation by reconstructing co-residence networks. The authors, including Marc Sadurní and other researchers, analyzed a city-wide administrative snapshot of Vienna's registered residents, covering the full foreign population and Austrian nationals at the district level. They built a statistically validated network where nodes represent nationalities and edges capture whether pairs of groups live in the same districts more or less often than expected by chance. Using community detection, they identified two main clusters of nationalities with distinct residential patterns. These clusters are systematically linked to district-level income disparities and diversity, and also reflect the geographical proximity of countries of origin.

This work goes beyond traditional segregation indices by providing an intuitive, interpretable map of residential sorting. The study's 41 pages, 18 figures, and 3 tables detail how network methods can capture multiple dimensions of migrant integration. For urban planners and policymakers, this approach offers a data-driven tool to understand the complex interplay of socio-economic factors, housing constraints, and preferences that drive segregation. The findings highlight how even within a single city, nationalities from nearby regions tend to share similar residential patterns, suggesting that cultural and geographic ties influence settlement choices.