JOURNAL ARTICLE

Structural Racism and Pedestrian Safety: Measuring the Association Between Historical Redlining and Contemporary Pedestrian Fatalities Across the United States, 2010‒2019.

  • Published In: American Journal of Public Health, 2023, v. 113, n. 4. P. 420 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Taylor, Nandi L.; Porter, Jamila M.; Bryan, Shenee; Harmon, Katherine J.; Sandt, Laura S. 3 of 3

Abstract

This article examines the association between historical redlining—a racially discriminatory housing policy implemented by the Home Owners' Loan Corporation (HOLC) in the 1930s—and contemporary pedestrian fatalities across the United States from 2010 to 2019. Using census tract-level data, the study found that areas graded "D" ("Hazardous") under HOLC maps had a 2.60 times higher incidence rate of pedestrian fatalities compared to "A" ("Best") graded areas, with a significant dose–response relationship observed across grades A to D. The findings suggest that structural racism embedded in historical redlining continues to influence present-day transportation inequities, disproportionately affecting communities of color and low-income populations. The study highlights the need for transportation safety programs and public health interventions to address these systemic inequities by incorporating historical context and focusing on structural factors rather than solely individual behaviors.

Additional Information

  • Source:American Journal of Public Health. 2023/04, Vol. 113, Issue 4, p420
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2023
  • ISSN:0090-0036
  • DOI:10.2105/ajph.2022.307192
  • Accession Number:162290783
  • Copyright Statement:Copyright of American Journal of Public Health is the property of American Public Health Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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