Abstract
The assessment of urban residential segregation plays a crucial role in guiding equitable governance and fostering healthy urban development. Existing research evaluating residential segregation from the perspective of wealth disparity often struggles to obtain accurate population distribution data across different socio-economic strata, leading to potential biases in the assessment outcomes. However, residential compounds, as the living spaces for various social groups, can serve as a surrogate for the data representing different socio-economic levels in the evaluation of residential segregation. This paper explores a method that classifies residential compounds based on multi-source data to replace socio-economic strata and assess residential segregation. The experimental results demonstrate that this method can effectively and conveniently evaluate residential segregation, providing a new approach for research in this area.
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Data availability
The data and code that support the findings of this study are publicly available via Zenodo at https://zenodo.org/records/18321286.
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This research was supported by the National Natural Science Foundation of China (42471453).
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YPZ was responsible for data curation, project administration, funding acquisition, validation, and writing review & editing; YS was responsible for conceptualization, methodology, validation, writing the original draft, and writing review & editing; AXZ contributed to project administration, conceptualization, and methodology; TG contributed to data curation, investigation, and methodology.
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Zhang, Y., Sun, Y., Zhu, AX. et al. Residential segregation assessment based on multi-source data and random forest method: a case study of Nanjing. Humanit Soc Sci Commun (2026). https://doi.org/10.1057/s41599-026-06840-w
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DOI: https://doi.org/10.1057/s41599-026-06840-w
