Urban sprawl in Latin America: A systematic review of the existing literature and emerging insights

Urban sprawl has become one of the most pressing challenges in Latin American cities, driven by a complex interplay of economic, political, and spatial factors. Despite being one of the most urbanized regions in the world, Latin America lacks a consistent analytical framework to fully understand the indicators, patterns, approaches and consequences of urban sprawl. This study conducts a systematic review of 56 peer-reviewed articles published between 2004 and 2025, applying a six-dimensional analytical framework: (i) general publication features, (ii) scale of analysis, (iii) methodological approaches, (iv) indicators employed, (v) drivers of change, and (vi) effects of urban sprawl. Findings reveal a strong concentration of city and metropolitan level studies in Mexico, Brazil, and Chile, with GIS-based spatial analysis dominating the methodological landscape, while contemporary approaches, such as machine learning and multi-criteria decision analysis, remain underused. Density and land use/land cover change are the most frequently applied indicators, followed by fragmentation and centrality metrics. The review underlines the context of structural drivers, including neoliberal housing policies, infrastructure-led growth, land market speculation, and governance gaps, which critically shape the spatial outcomes of sprawl. Socio-spatial inequality emerges as the most persistent and structural effect, yet multidimensional indices capturing access to infrastructure, public services, and ecosystem amenities are almost absent. The discussion calls for rethinking the application of Global North metrics to Global South contexts and advocates for the development of justice-oriented, context-sensitive frameworks that reflect Latin America’s unique urban realities. • Market forces and policy gaps are the main drivers of Latin American urban sprawl. • Socio-spatial inequality emerges as the most persistent and documented effect. • The field relies on descriptive methods; predictive tools are a future path.