Geodemographics and residential differentiation: A methodological review and future directions for learned representations of the social landscape

Residential differentiation reflects the complex patterns by which social groups distribute themselves across urban spaces, fundamentally shaping social, economic, and spatial structures. This paper reviews the methodological development of geodemographic classification, tracing its evolution from early social area analysis and factorial ecology through to contemporary approaches. We critically evaluate this lineage of methods for quantifying residential patterns, and identifying persistent limitations in capturing the non-linear complexities of contemporary urban environments. Building on this review, we explore potential future directions involving learned representations of the social landscape, which may offer alternatives to traditional linear dimensionality reduction techniques. Drawing on recent empirical work applying deep learning to geodemographic classification, we consider how such approaches might address identified limitations while acknowledging that their advantages over established methods remain context-dependent and require further empirical validation. We emphasise that any adoption of these techniques must prioritise transparency and interpretability. The paper concludes by outlining potential directions for future research, including how learned representations might be integrated within existing geodemographic workflows. • Critiques legacy geodemographic methods and traces their historical evolution. • Proposes learned representations as a potential approach that can capture non-linear urban residential patterns. • Calls for exploration of enriched multi-source data to enhance geodemographic feature learning. • Advocates for transparent, fair and explainable GeoAI in policy-relevant urban applications.