Integration of Markov chain analysis and similarity-weighted instance-based machine learning algorithm (SimWeight) to simulate urban expansion

This study simulates urban expansion using Kaduna in North-West Nigeria as a case study. A hybrid model that integrates the similarity-weighted instance-based machine learning algorithm for transition potential modelling and the Markov chain model to quantify and allocate land-use change was used to overcome the identified weaknesses of known modelling techniques such as the cellular automata, Markov chain and standard logistic regression models. Environmental and urban physical variables that act as constraints and/or incentives to urban expansion were operationalized to create transition potentials for spatiotemporal states of built-up land use for the year 1990 and 2001. Model evaluation and validation was carried out using the relative operating characteristic and kappa index of agreement statistics. Having obtained satisfactory outcomes from the validation process, the modelled transition potentials were used to predict future urban expansion for forthcoming years. The simulated land-use maps provide valuable insights into the location and type of urban expansion that is likely to occur in Kaduna in the foreseeable future. This provides city managers and planners much needed information that could inform urban policy aimed at better planning and management of urban development.