<p>This paper presents a methodological framework for detecting and modelling spatial clusters in urban areas. Diverse methods and empirical strategies are performed to provide researchers and planners with open-source tools for their implementation. Several clustering methods are compared, presenting their computational processes. Spatial clusters are modelled using a spatial probit with different specifications of the spatial effects. The proposed methods are applied to study immigrant populations in the metropolitan areas of Barcelona and Madrid. The framework is useful to detect different spatial patterns and to discover the driven factors that explain clustering. The results reveal that high-income areas attract EU clusters, while European non-EU and African clusters locate in low-income areas. Asian and American clusters show mixed results, influenced by density and urban location.&#xa0;A detailed analysis of spillover effects is carried out.</p>