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The measurement of deprivation for small areas in the UK has provided the basis for the development of policies and targeting of resources aimed at reducing spatial inequalities. Most measures summarise the aggregate level of deprivation across all people in a given area, and no account is taken of differences between people with differing characteristics, such as age, sex or ethnic group. In recognition of the marked inequalities between ethnic groups in the UK, and the distinctive geographies of these inequalities, this paper presents a new ethnic group‐specific neighbourhood deprivation measure—the Ethnic Group Deprivation Index (EGDI). This index, using a custom cross‐tabulated 2021 Census dataset on employment, housing tenure, education and health by ethnic group, reveals the small area geographies of ethnic inequalities that have to date received scant attention, and yet have profound impacts on life chances and well‐being. Drawing on the methodological framework of the widely used English Index of Multiple Deprivation (IMD) and for the same geographies (Lower Layer Super Output Areas), the EGDI measures deprivation for each ethnic group using data from the 2021 Census of England and Wales. The EGDI reveals the complex geographies of ethnic inequality and demonstrates that while one ethnic group in a neighbourhood may have high relative levels of deprivation, another ethnic group in that same neighbourhood may experience very low relative levels. The EGDI explores ethnic inequalities within and between neighbourhoods, complementing and augmenting existing measures by offering an important means of better understanding ethnic inequalities. The EGDI can be used to help shape locally and culturally sensitive policy development and resource allocation. [ABSTRACT FROM AUTHOR]

Much effort has been expended on measuring deprivation in neighbourhoods across the UK and elsewhere. However, there has been only a relatively limited concern with deprivation histories of areas. This paper takes as its focus Belfast in Northern Ireland and it capitalises on rich data sources on deprivation generally and unemployment specifically. The study makes use of data on multiple time points – the Census from 1971 to 2011, multiple deprivation measures for 2005, 2010, and 2017, and benefits claimant count data for 2020. The paper explores the changing geography of deprivation and unemployment, identifying areas with persistently high deprivation and unemployment across time and across measures. It is argued that trajectories are important, particularly in terms of targeting resources. The final section of the analysis uses claimant count data to assess changing unemployment rates following the outbreak of the COVID‐19 pandemic and it shows that changes in these rates are strongly related to long‐term deprivation and unemployment patterns. [ABSTRACT FROM AUTHOR]

Capturing the dynamics of population change in urban areas necessitates access to geographically fine‐grained and temporally consistent data for several time points. Such data are generally not available and they must be created using standard population data which cannot usually be compared across time periods. In this paper, the focus is on changing spatial inequalities in Cape Town, South Africa. This paper details an approach to generation of gridded population counts (250 m by 250 m) for two census years – 2001 and 2011. Census data for Small Area Layers (SALs), Spot Building Count (SBC) data, and Open Street Map (OSM) landuse data were used to construct a grid of populated cells to which population counts are then reallocated. The reallocation of population counts from SALs to grid cells was undertaken using area‐to‐point kriging – an approach which is informed by the spatial variation in the population groups of interest as measured using the variogram. A case study based on population grids of unemployment rates shows how the grids can be used to chart changes and also to measure spatial inequalities across the city at two time points. The advantages of grids for capturing fine‐scale complexities and correctly accounting for physical separation between communities are demonstrated and the results show, while the broad patterns of inequality are consistent across time, there are pronounced increases in inequalities in some neighbourhoods. These areas – and what leads some areas to fall further behind – should be the subject of attention by policy‐makers. [ABSTRACT FROM AUTHOR]