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Street-source uncertainties in spatial accessibility and social equity: who is affected?

Authors: Yan Lin*, University of New Mexico, Department of Geography & Environmental Studies, Christopher Lippitt, University of New Mexico, Department of Geography & Environmental Studies, Daniel Beene, University of New Mexico, College of Pharmacy, Community Environmental Health Program
Topics: Geographic Information Science and Systems, Spatial Analysis & Modeling, Transportation Geography
Keywords: Spatial accessibility, Multi-modal E2SFCA, Uncertainties, Social equity, Street data
Session Type: Paper
Presentation File: No File Uploaded

Spatial accessibility research has been frequently adopted to monitor social equity and to help develop policy and planning for provision of public services. However, uncertainties in street data can propagate and amplify through the accessibility analysis and modeling process. The role of these uncertainties in affecting social equity measurement outcomes remains largely unknown. This empirical study on spatial access to public services, including primary care physicians, green space, and healthy food in Albuquerque, NM, USA, aims to address this question. Street-source related uncertainties in spatial accessibility metrics are evaluated using a multi-modal enhanced two-step floating catchment area (E2SFCA) method. We use four different street network datasets (one free, two proprietary, and one open-source), ESRI North America Detailed Streets, ESRI StreetMap Premium, Google Maps, and OpenStreetMap, to estimate travel time and spatial access both by car and bus. We then quantify the street-source uncertainties and evaluate the sensitivity of variations to changes in model parameters. Populations most affected by the uncertainties are identified to expose the imbedded biases within decision-making that strives for social equity based on seemingly egalitarian accessibility metrics. Analysis reveals significant variations in spatial access by car and bus among different street datasets and we find that those variations are significantly associated with demographic, socioeconomic factors and with population density. Results from this study suggest that extra precaution ought to be taken regarding street data selection when measuring spatial access, to mitigate furthering unintended bias and social disparity derived from data driven policy and decision making.

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