Globally, the risk of a child dying before celebrating their fifth birthday is still high at 5.3 million deaths in 2018 alone. Nigeria is among the few countries that are yet to achieve the Sustainable Development Goal Target of keeping under-5 death to as low as 25 deaths per 1000 live births by 2030. A recent study found that the under-5 mortality rate in Nigeria is still high with 1 in 8 Nigerian children dying before reaching the age of 5. In this study, the effect of a child’s spatial location in Nigeria on their likelihood of dying before age 5 was examined alongside other key covariates. Bayesian geo-additive regression models were fitted to the 2018 Nigeria Demographic and Health Surveys data. Statistical inference was based on the Bayesian paradigm via Markov chain Monte Carlo simulation methods, and models were assessed using the deviance information criterion. Under-five mortality rate varied significantly across spatial locations in Nigeria with Kebbi, Jigawa, Kaduna, Kogi and Gombe states having the highest rates. The likelihood of a child dying before age 5 increased among women with primary education and women aged 38 years and over. Other characteristics associated with high under-5 death are poverty, male child, low birth weight and multiple births. The current study has helped to identify geographical ‘hotspots’ as well as the key factors driving under-5 deaths in Nigeria to inform the effective design and implementation of timely and efficient interventions.

Comparative segregation analysis holds the potential to provide rich insights into urban socio-spatial dynamics. However, comparisons of the levels of segregation between two, or more, cities at the same point in time can be complicated by different spatial contexts as well as ethnic, racial, and class distributions. The extent to which differences in segregation between two cities is due to differences in spatial structure or to differences in composition remains an open question. This paper develops a framework to disentangle the contributions of spatial structure and composition in carrying out comparative segregation analysis. The approach uses spatially explicit counterfactuals embedded in a Shapley decomposition. We illustrate this framework in a case study of the 50 largest metropolitan statistical areas in the U.S.

Although scholarship regarding spatial inequality has grown in recent years, past research has seen limited use of spatial statistics—let alone comparison between spatial statistical techniques. Comparing and contrasting the application and use of spatial statistics is valuable in research because it allows for more precise identification of spatial patterns, and highlights results that may be hidden when only using a single method. This study serves as a demonstration on how the use of multiple LISA statistics can benefit inequality related research. Analyzing changes in county level poverty in the rural United States from 1990 to 2015 serves as a tool to demonstrate these techniques and this study examined how the geographic distribution of poverty has changed, and well as if there is evidence of diffusion effects. The three featured techniques utilized Local Indicators of Spatial Association (LISA) statistics. The techniques are Bivariate LISA, LISA Cluster Transitions, and LISA Diffusion Transitions, with the last technique specifically designed for this study. Each technique varies in how it reports the changes in the spatial structure of poverty. Bivariate LISA and LISA Cluster Transitions are complementary to each other—with the former technique providing a single global statistic while the latter is more easily interpretable. Diffusion Transitions show how the highest and lowest values of a variable may be spreading over time. The study also produces new findings regarding rural poverty, with poverty in Mountain-West and rural Sun Belt counties on the rise. Analysis shows a diffusion effect for poverty in Southeastern metropolitan fringe counties.

Population ageing is one of the most important demographic phenomena of this century. Driven by fertility decline and the continuing extension of the life expectancy, the process of population ageing has not been uniform across time and space. Italy has one of the oldest populations in the world. The combination of a very old population and large territorial differences has made Italy an interesting laboratory for studying population ageing. The purpose of this paper is to study how population ageing developed between 2002 and 2014 across different geographical areas within Italy. We analyse patterns of population ageing across the five major socio-economic regions using the 110 provinces of Italy as our spatial units of analysis. We use a statistical model that integrates patterns of variation of population ageing data by accounting for autocorrelation in space and time. The results indicate that the provincial age structures tend to converge and demonstrate the importance of considering the role of space in studies of population ageing.

Ethnic residential segregation has become a compelling issue in Europe, following from a debate that links segregation with the failure of integrating immigrants into host societies. This paper focus on residential segregation among foreign migrants in the city of Naples. We distinguish among ‘enforced’ and ‘voluntary’ segregation; to this end, we use point pattern analysis to assess the residual clustering of migrant households after having adjusted for economic inhomogeneity within the urban area, noticeably in the cost of rents and in the availability of specific types of jobs. To avoid the risk of confounding the two sources of segregation, a case–control approach in which cases are migrant households and controls are a random sample of native households is introduced. Overall, Eastern European migrants showed no segregation, whereas Chinese reported the highest voluntary segregation. Sri Lankans, Filipinos and Dominicans also showed significant levels of voluntary segregation.