An urban data framework for assessing equity in cities: Comparing accessibility to healthcare facilities in Cascadia. (November 2019)

• Record Type: Journal Article
• Authors: Mayaud, Jerome R.; Tran, Martino; Nuttall, Rohan
• Is Part Of: Computers, environment and urban systems. Volume 78(2019)
• Publication Date: 2019-11
• Subjects: Machine learning -- Self-organizing maps -- Equality -- Inclusivity -- Gini index; City planning -- Data processing -- Periodicals; Regional planning -- Data processing -- Periodicals; 303.4834
• DOI: 10.1016/j.compenvurbsys.2019.101401
• Languages: English
• ISSNs: 0198-9715

Abstract

Abstract: As cities continue to grow worldwide, policymakers and urban planners face the dual task of meeting rising demand for essential services while ensuring that benefits accrue to their citizens equitably. We propose a framework for assessing inclusivity and equity in cities, which leverages open data and machine learning techniques to inform urban infrastructure investment strategies. The framework is applied at a regional scale to compare differential access to healthcare facilities (public hospitals and clinics) via public transit in Vancouver, Seattle and Portland. We find important distributional impacts on vulnerable populations across the three cities. Portland displays the highest inequity in hospital and clinic access, and Vancouver the least, owing to Vancouver's relatively compact geographic area and high population density. For seniors, over 75% are socially excluded from hospitals and over 50% from clinics in Portland, compared to 30% and 3% respectively in Vancouver. In all three cities, significantly more residents of low-income neighborhoods are excluded from healthcare compared to their counterparts in high-income neighborhoods. This translates into proportionally higher transportation costs for low-income area residents compared with high-income area residents, regardless of whether they are socially excluded or not. Transportation costs are notably high for low-income seniors in Seattle and Vancouver. These findings pose a challenge for inclusive planning, since low-income and senior populations may require specialized services and are more reliant on public transportation than the average population. Our evaluation framework provides a systematic approach for municipalities to account for the distributional effects of transportation and service infrastructure planning, to integrate equity into their decision-making, and to learn from the successes and pitfalls of each other's urban policies. Highlights: As cities grow, rising demand for essential services must be met by ensuring equitable benefits for citizens. We propose an open-data framework for assessing inclusivity and equity in cities that leverages machine learning techniques. We apply our framework regionally to analyze healthcare access via public transit in Vancouver, Seattle and Portland. We find important distributional impacts on vulnerable populations across all three cities. By integrating equity into decision-making, our framework allows municipalities to compare the impact of urban policies.