CHANGE DETECTION OF INFORMAL SETTLEMENTS USING REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEMS: CASE OF KAWANGWARE, NAIROBI (1990-2010)

Informal settlements behave dynamically over space and time and the number of people living in such housing areas is growing worldwide. The reasons for this dynamical behaviour are manifold. Nevertheless, informal settlements represent a status quo of housing and living conditions which is from a humanitarian point of view in the most cases below acceptable levels. Sub-standard sanitary situations and high crime rates are only a few of attributes which go aside with the phenomenon informal settlement. Due to their informal character, reliable and accurate data about informal settlements and their inhabitants is rarely available. On the other side there is a strong need to transform informal into formal settlements and to gain more control about the actual spatial development of informal settlements. Consequently, reliable procedures for detecting and monitoring the spatial behaviour of informal settlements are required in order to react at an early stage to changing housing situations. Thus, obtaining spatial information about informal settlement areas which is up to date is vital for any actions of enhancement in terms of urban or regional planning.
The complexity of urban systems makes it difficult to adequately address their changes using a model based on a single approach. In this project, the researcher developed a GIS-based integrated approach to modelling and prediction of urban growth in terms of land use change. The model was built upon a binomial logistic framework, coupled with a rule-based suitability module and focus group involvement, and is designed to predict land transition probabilities and simulate urban growth under different scenarios. The model was calibrated in the Kawangware region of Nairobi County through a GIS-facilitated participatory process involving both statistical assessment and human evaluation. The model achieved high overall success rates, although its predictive power varied spatially and temporally with different types of land use. The model was used to predict future urban growth in the region through the years 2020 and 2030.
The study findings indicate that there has been a steady growth in the built environment of the study area. The model also depicts that this growth will continue. As a result of this growth it is concluded that it is critical to establish a GIS land and infrastructure database. From the study findings, it is recommended that there should be public sensitization on planning regulations as well as development of land information systems.
The study is organised in to five chapters. Chapter one contains background information about the study problem, research objectives and questions. Chapter two discusses available literature, making comparison and drawing conclusions. Chapter three study methodology, data analysis, interpretation and presentations of findings in chapter four, and chapter five summary findings, conclusion and recommendations.

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