Abstract
During the last decades, cities in sub-saharan Africa have undergone rapid urban growth due to increased population growth and high economic activities. This research explores the impacts of varying modelling settings including spatial extend and its location for the city of Nairobi using a cellular automata (CA) urban growth model (UGM). Our UGM used multi-temporal satellite-based data for classification of urban land-use of 1986, 2000 and 2010, road data, slope data and exclusion layer. Monte-Carlo technique was used for model calibration and Multi Resolution Validation (MRV) technique for validation. Simulation of urban land-use was done up to the year 2030 when Kenya plans to attain Vision 2030. Three spatial grid sizes varying in extent and location were applied in the UGM calibration and validation. Thus, this research explored the impacts of varying spatial extent (grid) and location on urban growth modelling and hence can contribute to an improved sustainable planning and development. This is useful for future planning as the Nairobi grows and expands into the peri-urban areas.
Highlights
Urban growth modelling studies are currently considered as an essential component for numerous complex environmental approaches (Triantakonstantis & Mountrakis, 2012). Tobler (1979) pioneered the use of Cellular automata (CA) in geographical modeling
Urban land-use maps from satellite image classification were used alongside other datasets in modelling urban growth in Nairobi using urban growth model (UGM)
We explored three urban growth models for Nairobi of varying location and extent to model urban growth
Summary
Urban growth modelling studies are currently considered as an essential component for numerous complex environmental approaches (Triantakonstantis & Mountrakis, 2012). Tobler (1979) pioneered the use of Cellular automata (CA) in geographical modeling. Impacts of Spatial Extend and Site Location on Calibration of Urban Growth Models. Urban models have been used to simulate land-use scenarios in the view of addressing plausible agenda that fosters sustainable development (Oguz, Klein, & Srinivasan, 2007; Mubea, Goetzke, & Menz, 2014). CA simulates urban growth within discrete grid space and simulates land-use state changes via rules that operate within a neighborhood that interconnects adjacent cells (Akın, Clarke, & Berberoglu, 2014). Varying the spatial extent of a research area, such as in the case of our model city of Nairobi, we came up with a variable CA grid varying in site location. Three cellular automata models (UGM 1, UGM 2 & UGM 3) were used to model urban growth in Nairobi up to the year 2030 as Kenya attains Vision 2030 (Government of Kenya, 2007). At the end of the research, calibration and validation of our three models were achieved
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