Understanding socio-spatial inclusion: How age, ethnic, and income inclusion relate to neighborhood transport, land use, and housing features in Australia

Social inclusion is a crucial concept within the sustainability discourse and a fundamental goal in urban planning and design, encompassing multiple dimensions such as age, ethnicity, and income. It can be manifested spatially in the form of socio-spatial inclusion. However, existing studies that examine multi-dimensional social inclusion within spatial contexts remain limited, and the relationships between various dimensions of inclusion and neighborhood transport, land use, and housing features are still underexplored. Drawing on data at the Statistical Area Level 1 (SA1) from eight Australian capital cities, this study investigates the spatial patterns of age, ethnic, and income inclusion, as well as their neighborhood spatial predictors (including transport, land use, and housing) using explainable machine learning methods. The findings indicate that ethnically inclusive neighborhoods tend to cluster near urban centers, whereas those characterized by higher age or income inclusion are more spatially dispersed. Among the examined predictors, housing variables exhibit stronger associations with socio-spatial inclusion than those related to transport or land use. Specifically, factors such as road density, building density, separate housing, rental housing, affordable housing, and crowd housing demonstrate relatively higher predictive power, which should be considered differently in the formulation of urban planning strategies across diverse urban areas. This study reinforces the understanding of social inclusion in space through a multi-dimensional perspective and provides empirical evidence to support inclusive city and community development through transport, land use, and housing planning. © 2025 Elsevier Ltd