Analyzing and predicting urbanization patterns in Nairobi using geographic information systems and data mining techniques

Abstract

Urbanization, characterized by the expansion and development of towns due to population growth from both natural increase and migration, has profoundly reshaped cities worldwide. Over the past four decades, the proportion of people residing in urban areas more than doubled. According to the United Nations, projections indicated that by 2050, approximately 68% of the global population would be living in cities. This growth implied that nearly 2.5 billion additional people would reside in urban areas by 2050, with the majority of this expansion occurring in Asia and Africa. In Kenya, rapid urbanization resulted in various challenges, including the proliferation of informal settlements and inadequate infrastructure. Nairobi, as a major metropolis, experienced substantial urban growth, exacerbating these issues. This study sought to model urbanization patterns in Nairobi through the use of Geographic Information System (Geographic Information Systems (GIS)) and Remote Sensing, alongside the application of Data Mining techniques to forecast future urban expansion. The methodology involved analyzing Land Satellite (LANDSAT) 8 satellite imagery from 2014 and 2024, computing the Normalized Difference Built-up Index (Normalized Difference Built-up Index (NDBI)), and utilizing change detection techniques to assess urban development. The findings can provide crucial insights for policymakers and urban planners, facilitating sustainable urban development and addressing challenges associated with rapid urbanization. This research contributed to a broader understanding of urban dynamics in Kenya and presented a predictive framework for future urban planning efforts.

Keywords: Urbanization, GIS, Predictive Modeling, Machine Learning