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作者

Zheng Li;Xinlei Wang;Jessie Zarazaga;Janille Smith-Colin;Barbara Minsker

来源

CITIES,Vol.130,Issue1,Article 103927

语言

英文

关键字

Infrastructure equity;Neighborhood infrastructure;Statistical model;Data-driven

作者单位

Department of Civil Environmental Engineering, Southern Methodist University, United States of America;Department of Statistical Science, Southern Methodist University, United States of America;Hunt Institute for Engineering and Humanity, Southern Methodist University, United States of America;Department of Civil Environmental Engineering, Southern Methodist University, United States of America;Department of Statistical Science, Southern Methodist University, United States of America;Hunt Institute for Engineering and Humanity, Southern Methodist University, United States of America

摘要

Neighborhood infrastructure, such as sidewalks, medical facilities, public transit, community gathering places, and tree canopy, provides essential support for safe, healthy, and resilient communities. This paper examines the presence and condition of neighborhood infrastructure and shows that “infrastructure deserts” exist, which are low-income neighborhoods with significantly more deficient infrastructure. A generalized data-driven framework is developed and implemented at the street-level for 12 types of neighborhood infrastructure in Dallas, Texas. The results show significant infrastructure inequities across income levels for most types of infrastructure. Statistical inference predicts (with 95 % confidence) that low-income neighborhoods are 2.0 to 3.5 times more likely to have highly deficient infrastructure (8 or more deficient infrastructure types) than high-income areas and 1.4 to 2.4 times more likely to have highly deficient infrastructure than middle-income neighborhoods. This paper addresses the methodological challenge of considering multiple infrastructure types and provides a guide for infrastructure investment prioritization.