<p>This paper introduces a multiscalar extension of the Gini index for analysing spatial inequality in population concentration. While the classical Gini coefficient remains a widely used metric for measuring demographic and economic disparities, it does not account for the geographic structure of population distribution. Recent developments in spatial demography have highlighted the importance of multiscalar perspectives, acknowledging that population clustering and segregation occur simultaneously at local, regional, and national levels. Building on prior work in spatial decomposition, this study proposes a methodology that applies the Gini index to both contiguous and non-contiguous spatial units across increasing distances. The framework allows for a disaggregation of inequality patterns across multiple spatial scales, offering a more nuanced interpretation of demographic concentration. A key advantage of the proposed method is its preservation of the Gini index’s standard range and interpretability. Despite its spatial extension, the index retains its conventional form—ranging from 0 to 1—ensuring the results fully comparable with traditional applications. We apply this method to the case of foreigner populations across Italian provinces in 2024, identifying distinct scale-dependent patterns of spatial clustering. Results demonstrate that traditional aspatial measures may either overestimate or underestimate concentration depending on the geographic scale of analysis. Moreover, simulations demonstrate that the multiscalar Gini framework accurately distinguishes between spatially structured and random patterns. By incorporating spatial structure into inequality measurement, the proposed multiscalar Gini approach enhances our ability to analyse and interpret complex demographic distributions, with direct implications for territorial policy and urban planning.</p>