Modeling urban growth in Kigali city Rwanda

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 forexpansion (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

Application of the trajectory error matrix for assessing the temporal transferability of OBIA for slum detection

High temporal and spatial-resolution imageries are a valuable data source for slum monitoring. However, the transferability of OBIA methods across space and time remains problematic, due to the complexity of the term “slum”. Hence, transparency is important when analysing the transferability of OBIA methods for slum mapping. Our research developed a framework for measuring the temporal transferability of OBIA methods employing the trajectory error matrix (TEM). We found relatively low trajectory accuracies indicating low temporal transferability of OBIA methods for slum monitoring using point-based assessment methods. However, the analysis of change needs to be combined with an analysis of the certainty of this change by considering the context of the change to deal with common problems such as variations of the viewing angles and uncertainties in producing reference data on slums