Urban areas exhibit different growth patterns spanning from linear development, transit-oriented development, concentric zonal development to multi-nuclei development patterns. In the world we live in today, main urban areas present themselves as Central Business Districts (CBDs), that double up as mixed use commercial and residential areas, which serve most of the population who live in and around them. Ideally, the CBD sites – for most cities around the world, were identified in advance, making it easier for the local authorities to demarcate and plan for sustainable development. Most, if not all jobs, are in these urban areas, making these employment areas urban growth hotspots. Changes in economic processes and evolution of transport networks are the foundation of urban growth and expansion, in that, there is a shift from functional specialization of the CBD to economic specialization of the surrounding urban areas, as in the case of Rhine Main Region in Germany. In Kenya, most of the known urban areas, like Limuru Town, emerged as traditional markets in the 1900’s and grew to modern urban areas and municipalities. Urban growth in Limuru was propelled by the existence of modern infrastructure, reduced land rates, presence of government facilities, security, water and employment from the nearby tea farms and factories. However, urban growth has been accompanied by rapid land use changes and sporicidal growth of informal settlements. As a result, urban areas growing in Limuru Central Ward, are deprived of basic infrastructure, public purpose facilities, land use harmonization and spatial synergies. This study therefore attempts to explore the use of GIS and Remote sensing technologies in observing past and present urban growth trends, that pave the way for predicting sustainable urban planning. The findings from this study are expected to contribute to the knowledge of simulating how urban centers can be planned in the present to cater for the future needs of the growing population. Predicting urban growth trends introduces more practical ways of spatial planning and policy development in developing countries, through spatial analysis and modelling using GIS and Remote Sensing technologies.
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