Geographical Information System and Multi-criteria Based Cellular Automata Markov Model for Urban Growth and Analysis the Case Study of Bungoma Town Kenya

Abstract

Increase in population and the desire to seek new opportunities has contributed to urban growth, putting pressure on facilities in urban centres. Bungoma town, being the headquarter of Bungoma County has undergone radical changes in its physical form, not only in territorial expansion, but also through internal physical transformation. In the process of urbanization, physical characteristic of the town is gradually changing as cropland (agricultural land), vegetation and wetland has been converted to built-up areas. This new urban fabric needs to be analysed to understand the impact of these changes. The aim of this research was to evaluate the suitability of Bungoma town setting, model its growth and predict the future growth of the town based on land cover changes (1985-2015). Landsat satellite images were classified with five land cover classes followed by change detection. To simulate land cover map for Bungoma town in 2030, Markov Chain model and Cellular Automata Markov (CA-Markov) model were used. It was found that built-up area increased over the study period. The major contributors to this change are cropland, vegetation and wetland land cover types. The CA-Markov model results showed that 52% of the total study area will be converted into built-up area, 19% to cropland, 20% to vegetation, 5% to open spaces and 3% to wetland by 2030. This would have negative implication on food security in the region which is a major source of income for the inhabitants. There is need therefore for proper land use planning in the area. In addition, vertical urban development should be encouraged to control rapid expansion of the town.