大都市区
衡平法
二元分析
地理
业务
空间分析
人口经济学
运输工程
社会经济学
统计
经济
工程类
政治学
数学
遥感
考古
法学
作者
Sanju Maharjan,Nebiyou Tilahun,Alireza Ermagun
标识
DOI:10.1016/j.jtrangeo.2022.103437
摘要
This study examines the Modal Access Gap (MAG) between transit and automobile to employment, groceries, hospitals, and schools in 15-min, 30-min, 45-min, and 60-min travel-time thresholds in the City of Chicago. We use automobile and transit access data from the Metropolitan Chicago Accessibility Explorer and augment it with data from the American Community Survey and the Smart Location Database. We employ a Spatial Lag Model to explore sociodemographic and built-environment correlates of MAG and the bivariate local indicator of spatial association to create cluster maps to offer a way to assess the spatial equity of MAG as it relates to carless households. The findings indicate that: (1) regardless of the travel-time threshold, the automobile has an advantage over transit in providing access to opportunities, (2) block groups with low MAG are concentrated and clustered in the Central Business District, (3) Millennials and car-free households are more likely to reside in areas with lower accessibility gap to employment, groceries, hospitals, and schools for 30-min and 60-min travel-time thresholds, and (4) areas with high access gap and a high proportion of carless households have a higher percentage of African Americans and low-income households. We recommend using the bivariate spatial autocorrelation analysis to classify areas according to the gap in accessibility and proportion of households without vehicles. This classification is then used to prioritize different planning actions for high-high, high-low, low-high, and low-low combinations of MAG and the proportion of carless households. We also show that this spatial identification, at least in the case of Chicago, captures racial and economic differences in the underlying population and can help address inequities in accessibility, particularly in high access gap, high carless areas.
科研通智能强力驱动
Strongly Powered by AbleSci AI