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 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 EasternSouthern 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.

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Kigali is a rapidly growing city, as exemplified by the phenomenal increase of its inhabitants from 358,200 in 1996 to 1,630,657 in 2017. Nevertheless, there is a paucity of detailed analytical information about the processes and factors driving unprecedented urban growth in the period following the genocide perpetrated against the Tutsi (1994) and its impact on the natural environment. This article, therefore, analyses the growth of the city of Kigali with respect to its post-genocide spatial and demographic dimensions. The methodology involves a quantification of urban growth over the period of the last 30 years using remote-sensing imagery coupled with demographic data drawn from different sources. The analysis of land cover trends shows how significant the pressure of urban expansion has been on the natural environment, with a 14 per cent decrease in open land between 1999 and 2018. Spatially, the average annual growth rate was almost 10.24 per cent during the same period. This growth is associated with the building of a large number of institutions, schools and industries. Moreover, the increase in low-income residents led to the construction of bungalows expanding on large suburbs and the development of new sub-centres in the periphery instead of high-rise apartments.

Given the scarcity of resources in developing countries like Rwanda, malaria control requires new strategies that target specific populations, time periods and geographical areas. While the spatial pattern of malaria incidence varies depending on local conditions, its temporal evolution has yet to be evaluated in Rwanda. The aim of this study was to determine the spatio‐temporal dynamics of malaria incidence in Rwanda. GIS was used to delineate the health centre catchment areas. Optimized hotspots analysis was performed to detect the spatio‐temporal patterns of hotspots of malaria from 2010 to 2017. SPSS software was used to generate boxplots comparing the intensity of inter‐annual variation of malaria cases at health centre catchment scale. This study highlighted the spatial variability and relative temporal stability of malaria hotspots in lowlands and the coldspots of malaria incidence in highlands. Information on malaria hotspot dynamics can help to improve the health outcomes of malaria control and reduction strategies in Rwanda. As malaria data were aggregated at health centre level, further research can use malaria prevalence at the household scale and investigate the driving factors of malaria occurrence in the identified malaria hotspots. This would allow for the development of better‐targeted control strategies.

As found in the health studies literature, the levels of climate association between epidemiological diseases have been found to vary across regions. Therefore, it seems reasonable to allow for the possibility that relationships might vary spatially within regions. We implemented the geographically weighted random forest (GWRF) machine learning method to analyze ecological disease patterns caused by spatially non-stationary processes using a malaria incidence dataset for Rwanda. We first compared the geographically weighted regression (WGR), the global random forest (GRF), and the geographically weighted random forest (GWRF) to examine the spatial non-stationarity in the non-linear relationships between malaria incidence and their risk factors. We used the Gaussian areal kriging model to disaggregate the malaria incidence at the local administrative cell level to understand the relationships at a fine scale since the model goodness of fit was not satisfactory to explain malaria incidence due to the limited number of sample values. Our results show that in terms of the coefficients of determination and prediction accuracy, the geographical random forest model performs better than the GWR and the global random forest model. The coefficients of determination of the geographically weighted regression (R2), the global RF (R2), and the GWRF (R2) were 4.74, 0.76, and 0.79, respectively. The GWRF algorithm achieves the best result and reveals that risk factors (rainfall, land surface temperature, elevation, and air temperature) have a strong non-linear relationship with the spatial distribution of malaria incidence rates, which could have implications for supporting local initiatives for malaria elimination in Rwanda.