  The objective of this study is to characterize inequality in infrastructure
quality across urban areas. While a growing of body of literature has
recognized the importance of characterizing infrastructure inequality in cities
and provided quantified metrics to inform urban development plans, the majority
of the existing approaches focus primarily on measuring the quantity of
infrastructure, assuming that more infrastructure is better. Also, the existing
research focuses primarily on index-based approaches in which the status of
infrastructure provision in urban areas is determined based on assumed
subjective weights. The focus on infrastructure quantity and use of indices
obtained from subjective weights has hindered the ability to properly examine
infrastructure inequality as it pertains to urban inequality and environmental
justice considerations. Recognizing this gap, we propose a machine
learning-based approach in which infrastructure features that shape
environmental hazard exposure are identified and we use the weights obtained by
the model to calculate an infrastructure quality provision for spatial areas of
cities and accordingly, quantify the extent of inequality in infrastructure
quality. The implementation of the model in five metropolitan areas in the U.S.
demonstrates the capability of the proposed approach in characterizing
inequality in infrastructure quality and capturing city-specific differences in
the weights of infrastructure features. The results also show that areas in
which low-income populations reside have lower infrastructure quality
provision, suggesting the lower infrastructure quality provision as a
determinant of urban disparities. Accordingly, the proposed approach can be
effectively used to inform integrated urban design strategies to promote
infrastructure equity and environmental justice based on data-driven and
machine intelligence-based insights.
