The widespread informal settlements in Nairobi have interested many researchers and urban policymakers. Reasonable planning of urban density is the key to sustainable development. By using the spatial population data of 2000, 2010, and 2020, this study aims to explore the changes in population density and spatial patterns of informal settlements in Nairobi. The result of spatial correlation analysis shows that the informal settlements are the centers of population growth and agglomeration and are mostly distributed in the belts of 4 and 8 km from Nairobi’s central business district (CBD). A series of population density models in Nairobi were examined; it showed that the correlation between population density and distance to CBD was positive within a 4 km area, while for areas outside 8 km, they were negatively related. The factors determining population density distribution are also discussed. We argue that where people choose to settle is a decision process between the expected benefits and the cost of living; the informal settlements around the 4-km belt in Nairobi has become the choice for most poor people. This paper ends with suggestions for urban planning and upgrading informal settlements. The findings will increase our understanding of urban population distribution in underdeveloped countries.
Africa has been undergoing a rapid urbanization process, which is critical to the achievement of the 11th Sustainable Development Goal (SDG11). Using population density data from LandScan, we proposed a population density-based thresholding method to generate urban land and urban population data in Africa from 2001 to 2019, which were further applied to detect the spatiotemporal characteristics of Africa’s urbanization. The results showed that urban land and urban population have both grown rapidly in Africa, which increased by about 5.92% and 4.91%, respectively. The top three countries with the most intense urbanization process in Africa are Nigeria, the Democratic Republic of the Congo, and Ethiopia. The coupling relationship index of urban land expansion and population growth was 0.76 in Africa during 2001–2019. Meanwhile, the total proportion of uncoordinated development types at the provincial level was getting higher, which indicated an uncoordinated relationship between urban land expansion and population growth in Africa. Cropland, grassland, rural land, and forests were the most land-use types occupied by urban expansion. The proportion of cropland, grassland, and forests occupied was getting higher and higher from 2001 to 2019. The extensive urban land use may have an impact on the environmental and economic benefits brought by urbanization, which needs further research.
As the earth's third pole, Qinghai-Tibet Plateau belongs to one of the most sensitive regions to climate change in the world. Based on the observed and the simulated daily precipitation from the Coupled Model Intercomparison Project Phase 5 (CMIP5), we evaluated the simulation performance of daily precipitation from selected CMIP5 models from 1975 to 2005 over the Qinghai-Tibet Plateau. We found that daily precipitation exhibited obvious longrange correlation characteristics using the detrended fluctuation analysis method. The scaling exponents of daily precipitation in summer and autumn are significantly larger than those in spring and winter. MIROC4H with the best performance can reproduce long-range correlation characteristic of daily precipitation series probably because of the higher resolution, which can capture small scale cloud convections. Besides there are seasonal differences in the simulation results among different regions of the Qinghai-Tibet Plateau, simulation effects of all climate models in summer and winter are better than those in spring and autumn. The performance of MIROC4H model works the best in spring. Overall, the scaling exponents of daily precipitation from BCC-CSM1-1-M, CMCC-CM and MIROC4H are close to the observations. CCSM4 and MIROC4H climate models could reproduce the internal dynamics characteristic of daily precipitation in autumn. But for winter, all climate models have exaggerated the scaling value in southeastern Qinghai-Tibet Plateau compared with the observed values.
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