Evaluation Of A Self-Modifying Cellular Automata In Modelling Urban Growth In Nyeri Kenya

Abstract Urban growth modelling cellular automata has blossomed due to the advancement in geographic information systems GIS remote sensing and computer technology. Among such urban growth models our urban growth model UGM was modified from SLEUTH Slope Land-use Transport Hill-shade model. UGM has been integrated in the XULU modeling frame-work eXtendable Unified Land Use Modelling Platform. In this research we evaluated a modified UGM whose transition rules were modified. In order to arrive at urban growth modelling we used multi-temporal Landsat satellite image sets for 1987 and 2010 to map urban land-use in Nyeri. We compared our results with a normal UGM simulation. Thus we arrived at two urban growth simulations for Nyeri in order to get a better glimpse of land-use system dynamics. Both models were calibrated and urban growth simulated until the year 2030 when Kenya plans to attain Vision 2030. Observed land-use changes in urban areas were compared to the results of both UGM models for the year 2010. The results indicate that the two models resulted in urban growth in different directions and magnitudes. This approach is useful to planners as it gives the scenarios of using different transition rules of a cellular automata model in urban growth modelling.