Urban growth dynamics in Southwest Nigeria: A geospatial and neural network approach

Urban growth dynamics have become a critical area of study due to their profound implications for sustainable development, land-use management, and socio-economic planning in rapidly expanding regions. Across many parts of the Global South, urbanization has outpaced infrastructural development, resulting in significant challenges including environmental degradation, traffic congestion, and pressure on public services. Geospatial technologies, particularly remote sensing and Geographic Information Systems (GIS), have provided a systematic means to monitor these changes by capturing spatial patterns over time. When integrated with artificial intelligence methods such as artificial neural networks (ANNs), these tools enable more accurate modeling, prediction, and interpretation of urban growth trends. In Nigeria, Southwest states such as Lagos, Oyo, Ogun, and Osun have experienced dramatic population increases driven by rural-urban migration, industrial expansion, and economic opportunities. This growth has led to unregulated land conversion, expansion of informal settlements, and heightened ecological pressures. Geospatial analysis offers a means to quantify the spatial extent of these changes, while ANN models can forecast future scenarios by learning complex, non-linear relationships between socio-economic drivers and spatial growth patterns. By combining historical satellite imagery, land-use data, and predictive modeling, researchers can not only detect the magnitude and direction of growth but also identify potential hotspots of urban sprawl. This integrated geospatial and ANN approach provides policymakers with valuable insights for urban planning, resource allocation, and environmental sustainability. Specifically, it highlights the need for proactive planning strategies that balance economic development with ecological preservation, ensuring that rapid urbanization in Southwest Nigeria evolves toward more resilient and inclusive urban systems.