Comparing the Capability of Integrated Cellular Automata-Markov Chain Model and SLEUTH Model in Monitoring and Simulating Urban Growth
Paparu, Tracy Desire
MetadataShow full item record
Urban growth is a worldwide phenomenon but the rate of urbanization is very fast in developing countries like Uganda. Besides the benefits of urbanization such as better service delivery, environmental and socioeconomic problems such as emergence of slums, urban heat islands have emerged due to this rapid urbanization. To mitigate such adverse impacts, urbanization has to be planned and well managed hence its simulation would play a crucial role in effective urban planning and management of an ever-increasing world’s population. The problem being assessed is that from the variety of techniques developed, there are a range of activities that are related to urban models, including land use, housing, population, travel, networks, transport, employment, and workplaces where each urban model cannot comprehensively mimic all urban activities. Even though these models have a common goal, they vary widely in underlying methodologies, theoretical assumptions, spatial/temporal resolutions and extents, their accuracies may therefore vary from town to town due to various factors such as availability of accurate data. Researchers often find it hard to identify and use the appropriate prediction models hence the aim of this topic stated. The purpose of this study is to compare performance and accuracies of CA-Markov Chain (CA-MC) model with CA based SLEUTH model in Kampala district, since it is one of the fastest growing cities in Uganda. The expected contribution of this research is to identity a suitable prediction model for Kampala and this is intended to aid the necessary stakeholders such as the Urban Planning Department to make informed decisions. Using Landsat imagery for the years 1995, 2000, 2010 and 2020, Land Use Land Cover maps were generated through supervised classification. These land covers maps of 1995, 2000 and 2010 in particular were used for calibration of the respective models and that of 2020 was used for model validation using kappa index. The kappa index values from the different models were then compared against each other where the model with the higher index value was considered more appropriate. The results obtained include the land use land cover maps of 1995, 2000, 2010 and 2020 and urban growth projected maps of 2030 and 2040. In conclusion, the analysis indicates a consistent trend of decreasing vegetation cover as the urban extent expands and the findings also suggest that the CA-MC model outperformed SLEUTH model hence it is more suitable for modeling Kampala’s urban growth dynamics.