Simulating Urban Land Expansion in the Context of Land Use Planning in the Abuja City-Region, Nigeria
Abstract In the Global South, including the Sub-Saharan African city-regions, the possible future urban expansion patterns may pose a challenge towards improving environmental sustainability. Land use planning strategies and instruments for regulating urban expansion are faced with challenges, including insufficient data availability to offer insights into the possible future urban expansion. This study integrated empirical data derived from Geographic Information Systems, Remote Sensing, and surveys of experts to offer insights into the possible future urban expansion under spatial planning scenarios to support land use planning and environmental sustainability of city-regions. We analyzed the spatial determinants of urban expansion, calibrated the land cover model using the Multi-Layer Perceptron Neural Network and Markov, and developed three scenarios to simulate land cover from 2017 to 2030 and to 2050. The scenarios include Business As Usual that extrapolates past trends; Regional Land Use Plan that restricts urban expansion to the land designated for urban development, and; Adjusted Urban Land that incorporates the leapfrogged settlements into the land designated for urban development. Additionally, we quantified the potential degradation of environmentally sensitive areas by future urban expansion under the three scenarios. Results indicated a high, little, and no potential degradation of environmentally sensitive areas by the future urban expansion under the Business As Usual, Adjusted Urban Land, and Regional Land Use Plan scenarios respectively. The methods and the baseline information provided, especially from the Adjusted Urban Land scenario showed the possibility of balancing the need for urban expansion and the protection of environmentally sensitive areas. This would be useful to improve the environmental sustainability of the Sub-Saharan African city-regions and across the Global South, where insufficient data availability challenges land use planning.