GIS-Based Land-Use Planning For Sustainable Urban Growth

Abstract

Uncontrolled urban expansion threatens agricultural productivity, biodiversity corridors, and flood resilience across rapidly growing cities in Sub-Saharan Africa. This study applies Geographic Information System (GIS) based multi-criteria decision analysis (MCDA) and Cellular Automata–Markov (CA-Markov) modelling to evaluate land-use change and guide future development in the Kumasi Metropolitan Area, Ghana. Landsat imagery from 2000, 2010, and 2020 was classified into five land-cover categories using supervised Maximum Likelihood classification (overall accuracy > 87%, Kappa > 0.82). Between 2000 and 2020, built-up area expanded from 42.3 km² to 95.6 km², consuming 38.5 km² of vegetation and 20.2 km² of agricultural land. An Analytic Hierarchy Process (AHP) weighted overlay identified zones with high development suitability based on slope, road proximity, soil type, and flood risk. CA-Markov simulations projected three 2030 scenarios: business-as-usual (BAU), which predicts a 38% increase in built-up area; planned growth, limiting expansion to high-suitability zones (22% increase); and conservation, which designates ecological no-go zones (9% increase). The planned-growth scenario preserves 65% more agricultural land than BAU while accommodating projected population demand.