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Rwanda Journal

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Modeling urban growth in Kigali city Rwanda

G. Nduwayezu, R. Sliuzas, M. Kuffer

Abstract


The uncontrolled urban growth is the key characteristics in most cities in less developed countries. However, having a good understanding of the key drivers of the city's growth dynamism has proven to be a key instrument to manage urban growth. This paper investigates the main determinants of Kigali city growth looking at how they changed over time and also how they contributed to the city change through different Logistic Regression models. First, it analyses the spatio-temporal growth of Kigali city through a consistent set of land cover maps of during the period 1987, 1999, 2009 and 2014. Second, after building a Logistic Regression model; the main drivers of Kigali city growth are identified. Third to characterize the future pattern of the city in next 26 years, three scenarios are performed, i.e. urban growth model for
expansion (normal growth) and two densification (zoning implication) i.e. strict and moderate scenarios. Logistic Regression Models probability maps for the three scenario were evaluated by means of Kappa statistic, ROC value and the percentage of 2014 built-up land cover predicted. The results indicated that new urban  developments in Kigali city tend to be close to the existing urban areas, further from the Center Business District (CBD) and wetlands but on low slope sites. Three  scenarios built have patterns characterized by a strong compactness of urban  densities. However, all three models tend to exclude urban units in the Eastern-Southern part of the city. The three models tend to exclude urban units in the Eastern-Southern part of the city compared to the proposed zoning maps. Models results in 2040 indicate that the city trend will be doubled if the current trend rate continues. Models built, will help to better understand the dynamics of built-up area and guide sustainable urban development planning of the future urban growth in Kigali city.


Keywords-Urban growth, GIS, Remote Sensing, Logistic Regression modeling, Kigali city, Rwanda




http://dx.doi.org/10.4314/rj.v1i2S.7D
AJOL African Journals Online