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.

This study evaluates the performance of four bias correction methods based on CORDEX (coordinated regional climate downscaling experiment) domain six regional climate models (RCMs) at the Mara River Basin. A suitable bias correction method was considered to develop the future climate scenario. The performance of bias correction methods was evaluated by various statistical metrics based on the historical period and revealed that the distribution mapping (DM) techniques have strong performance under the different climatic conditions. The effectiveness of the DM method is found to be better at capturing the coefficient of variation and standard deviation of observed rainfall and temperature. Therefore, this study considers the future climate (2026–2095) from bias-corrected RCMs output using DM techniques. The results from bias-adjusted RCMs show an increase of rainfall (+118.3%) and temperature (+2.91) in the future climate under Representative Concentration Pathways (RCPs) 2.6, 4.5 and 8.5. In addition, this study tested the random forest (RF) method to determine the capacity of each bias-corrected RCMs for reproducing the future rainfall and temperature under the RCP 4.5 and RCP 8.5 scenario. The results demonstrate that the RF can reproduce the climate variable with its average correlation (R2) of 0.93 for rainfall and 0.95 for temperature.

This study evaluates the high-resolution satellite estimated long-term precipitation data for monitoring the drought condition over the Lake Victoria Basin (LVB) from 1984 to 2020. Standardized Precipitation Indices (SPI) were used to capture the short, medium and long-term meteorological drought conditions at multiple time scales (i.e. 3, 6, and 12). For these, the following two primaries Quantitative Precipitation Estimation (QPEs) products were employed – 1) Climate Hazards group Infra-Red Precipitation with Station (CHIRPS), and 2) the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network -Climate Data Record (PERSIANN-CDR). This dataset was compared based on the observation data obtained from the Climate Research Unit (CRU) over the nine selected regions surrounding lake basins. The performance of these two QPEs products was evaluated using seven statistical metrics. The findings of this study indicate that the CHIRPS and PERSIANN-CDR datasets could capture the behavior of drought magnitude based on the time scale of SPI-3, SPI-6, SPI-12. The results indicate that 2012 and 2017 are significant severe drought years in the recent decade over LVB. However, the CHIRPS datasets provide good agreement (Correlation Coefficient (CC) = 0.65) with observation, whereas PERSIANN-CDR present satisfactory results (CC = 0.54). In addition, Hurst (H) exponent was used to predict the future drought trend and found that the CHIRPS performed well to predict the degree of drought trend. Therefore, this study considers the CHIRPS product for near-real-time drought monitoring and PERSIANN-CDR for historical drought assessment. Moreover, the outcome from the H values is greater than 0.5, which indicates the future drought trend would be decreased over LVB. These results are useful for developing the strategies for drought hazards and water resource management in LVB.

China launched the “critical battle against poverty” nationwide in 2012. As its main battlefield, Yunnan province promulgated the “triple medical security” (TMS) policy in 2017. This study, based on the pooled cross-section database of 2015–2020 of registered poor households in Yunnan province, employed the logit model to examine the effect of TMS on the vulnerability as expected poverty (VEP) of these households. It found that increasing the reimbursement rates for overall medical expenses and inpatient expenses and decreasing the proportion of out-of-pocket medical payment to income reduced the VEP; increases in the number of sick people in the family increased its VEP, and although the increase in the reimbursement rate for overall medical expenses or for inpatient expenses partially offset the VEP caused by the increase in the number of chronically ill people in the family, the VEP caused by the increase in the number of critically ill people would increase in the short term with the increase in the reimbursement rate for overall medical expenses or for inpatient expenses. The findings help improve policies concerning the medical security and health of the rural poor population, providing theoretical reference and practical guidance for future research.

Urbanization in African countries entails substantial growth in the urban population and economic development. The interdependent progress of the population and economy significantly impacts the sustainable development of these nations. By constructing an evaluation framework, this paper assesses the urban population growth and economic development systems in African countries. Building upon the coupling coordination model, it quantitatively investigates the relationship between the two and utilizes a geographical detector model to analyze the driving factors of the coordination of evolution. The findings reveal a continuous improvement in the quality of urban population growth and economic development between 2001 and 2020. Nevertheless, their overall quality remains relatively low, exhibiting considerable variation across different countries. Many African countries struggle with a low level of development coordination, with economic progress often trailing behind the pace of urban population growth. The average coupling coordination degree increased from 0.464 to 0.526 over 20 years, with 48.08% of countries still in uncoordinated development by 2020. Factors such as industrialization, foreign trade dependence, government spending, international aid, and political stability are all influential factors affecting the degree of coordination. The absence of industrialization in conjunction with urbanization poses a major impediment to effectively harnessing urban population growth for economic development. Ultimately, this study provides a targeted framework for integrating urban population growth and economic development to address low coupling coordination.