Measuring differential access to facilities between population groups using spatial Lorenz curves and related indices

Abstract

Access to certain types of facilities can promote health and well-being. When population and facilities are not uniformly distributed across the landscape, inequities in accessibility may occur. Current research into GIS-based accessibility measures has focused primarily on spatial inequities between different geographic locations but not directly on differences in accessibility between subgroups of the population. The research presented here develops a new method for measuring differential accessibility to facilities between various segments of the population. The method extends concepts and techniques in spatial point pattern analysis that account for the spatial structure of demand and its relationship to supply. In this approach, the traditional Lorenz curve and its associated indices, the Gini coefficient and the dissimilarity index, which are used to measure inequality, are recast in spatial terms for measuring differences in accessibility between population subgroups.An analysis of spatial accessibility to grocery stores in Akron, OH illustrates the value of the spatial Lorenz curve and its associated indices compared to other methods.

Examining the impact of COVID-19 vaccination rates on differential access to critical care

“…To address scalability, Andrienko and Andrienko (2004) suggest using a series of cumulative frequency curves (CFCs). CFCs have been used to compare accessibility patterns of different populations ( Cromley, 2019 ; Grengs, 2010 ). In this study, CFCs for the fully and not-fully vaccinated populations with respect to the three accessibility/congestion models are plotted.…”

How foreign aid and remittances affect poverty in MENA countries?

“…The application of Gini coefficient has expanded beyond the area of socioeconomics [ 37 ]. Areas such as human geography [ 38 ], public health [ 39 ] and many more have utilized the Gini index for measuring inequality. Along with the Gini coefficient, other measures of inequality have also been used such as Thiel’s index, Atkinson’s measures of income inequality, however, studies like [ 40 ] has reported that virtually all measures of income inequality are highly correlated.…”

Examining the interplay between racial segregation patterns and access to hospital care

“…Massey and Denton (1988) measured residential segregation along five dimensions: (1) evenness-how uniform the distribution of different groups are over an area; (2) exposure-the level of potential interaction that can occur between groups; (3) concentration-the amount of area each group uses relative to its population size; (4) centrality-how close a group is to the central city areas; and, (5) clustering-the degree to which members of a group locate near one another. Among the five dimensions, evenness measures for describing residential segregation have been deemed most important with respect to accessibility to facilities (Cromley, 2019). In the context of accessibility, the main concern is exposure to facilities rather than members of another group.…”

“…The same cumulative frequencies for two groups (white and non-white population) can also be plotted against one another ordered by accessibility to create a spatial Lorenz curve (Cromley, 2019). A spatial Gini coefficient can be used to measure the difference in accessibility between two groups; spatial Gini coefficients range from −1.0 to 1.0 in which a negative spatial Gini value implies better overall accessibility by members of the first group and a positive spatial Gini value implies better overall accessibility by members of the second group (Cromley, 2019). If the accessibility values for two population groups are about the same, then the cumulative increase for one group would match the other group’s cumulative increase, and the spatial Lorenz curve would follow the line of equality.…”