Journal
COMPUTERS ENVIRONMENT AND URBAN SYSTEMS
Volume 86, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.compenvurbsys.2020.101590
Keywords
Equity; Equality; Environmental justice; Urban planning; Food desert
Categories
Funding
- Department of Civil and Natural Resources Engineering at the University of Canterbury
- University of Michigan Rackham PreDoctoral Fellowship
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The current methods for measuring inequality are inadequate for promoting equity in built environments and urban systems. The Kolm-Pollak EDE offers a new approach that can evaluate inequality of both desirable and undesirable quantities, providing opportunities to address inequities in urban systems. The EDE can also be calculated for different sociodemographic subgroups, enabling tailored assessments to promote environmental justice.
Current approaches for measuring inequality are insufficient or unsuitable for promoting and designing equitable built environments and urban systems. In this paper, we demonstrate how a recently developed inequality measure-the Kolm-Pollak equally-distributed equivalent (EDE)-could be used to support decision making to foster equity in the built environment. The EDE provides a measure of a distribution that is similar to the average (mean) but includes a penalty based on the inequality of that distribution. The primary advantage of the Kolm-Pollak EDE is that it can be used to evaluate the inequality of both desirable quantities (e.g., amenities) and undesirable quantities (e.g., burdens). This is essential in urban systems as inequities can manifest through, among other things, disparate access to opportunities like public amenities and unequal exposure to burdens, such as pollution and natural hazards. Additionally, the Kolm-Pollak EDE can be calculated for different sociodemographic subgroups, enabling needs-based assessments to promote environmental justice. Thus, the Kolm-Pollak EDE presents numerous opportunities for practitioners, policymakers, and researchers concerned with advancing equity. We demonstrate the approach with a case study of grocery store access in ten cities across the USA and provide a Python package (inequalipy) and R code to enable others to use these inequality metrics.
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