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Gentrification and Displacement in the San Francisco Bay Area: A Comparison of Measurement Approaches

Author

Listed:
  • Mahasin S. Mujahid

    (Division of Epidemiology, Berkeley School of Public Health, University of California, 2121 Berkeley Way West, Berkeley, CA 94720-7360, USA)

  • Elizabeth Kelley Sohn

    (Kaiser Permanente, Oakland, CA 94611, USA)

  • Jacob Izenberg

    (Department of Psychiatry, San Francisco School of Medicine, University of California, 982 Mission St., San Francisco, CA 94103, USA)

  • Xing Gao

    (Division of Epidemiology, Berkeley School of Public Health, University of California, 2121 Berkeley Way West, Berkeley, CA 94720-7360, USA)

  • Melody E. Tulier

    (Center for Interdisciplinary Research on AIDS, Yale University, New Haven, CT 06510, USA)

  • Matthew M. Lee

    (Division of Epidemiology, Berkeley School of Public Health, University of California, 2121 Berkeley Way West, Berkeley, CA 94720-7360, USA)

  • Irene H. Yen

    (University of California, Merced, Public Health, 5200 N. Lake Road Merced, CA 95343, USA)

Abstract

Gentrification may play an important role in influencing health outcomes, but few studies have examined these associations. One major barrier to producing empirical evidence to establish this link is that there is little consensus on how to measure gentrification. To address this barrier, we compared three gentrification classification methodologies in relation to their ability to identify neighborhood gentrification in nine San Francisco Bay Area counties: the Freeman method, the Landis method, and the Urban Displacement Project (UDP) Regional Early Warning System. In the 1580 census tracts, 43% of the population had a bachelor’s degree or higher. The average median household income was $79,671 in 2013. A comparison of gentrification methodologies revealed that the Landis and Freeman methodologies characterized the vast majority of census tracts as stable, and only 5.2% and 6.1% of tracts as gentrifying. UDP characterized 46.7% of tracts at risk, undergoing, or experiencing advanced stages of gentrification and displacement. There was substantial variation in the geographic location of tracts identified as gentrifying across methods. Given the variation in characterizations of gentrification across measures, studies evaluating associations between gentrification and health should consider using multiple measures of gentrification to examine the robustness of the study findings across measures.

Suggested Citation

  • Mahasin S. Mujahid & Elizabeth Kelley Sohn & Jacob Izenberg & Xing Gao & Melody E. Tulier & Matthew M. Lee & Irene H. Yen, 2019. "Gentrification and Displacement in the San Francisco Bay Area: A Comparison of Measurement Approaches," IJERPH, MDPI, vol. 16(12), pages 1-13, June.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:12:p:2246-:d:242860
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    References listed on IDEAS

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    3. Hollis Hutchings & Qiong Zhang & Sue Grady & Lainie Mabe & Ikenna C. Okereke, 2023. "Gentrification and Air Quality in a Large Urban County in the United States," IJERPH, MDPI, vol. 20(6), pages 1-11, March.
    4. Gao, Xing & Thomas, Timothy A. & Morello-Frosch, Rachel & Allen, Amani M. & Snowden, Jonathan M. & Carmichael, Suzan L. & Mujahid, Mahasin S., 2023. "Neighborhood gentrification, displacement, and severe maternal morbidity in California," Social Science & Medicine, Elsevier, vol. 334(C).
    5. Richard C. Sadler & Julia W. Felton & Jill A. Rabinowitz & Terrinieka W. Powell & Amanda Latimore & Darius Tandon, 2022. "Inequitable Housing Practices and Youth Internalizing Symptoms: Mediation Via Perceptions of Neighborhood Cohesion," Urban Planning, Cogitatio Press, vol. 7(4), pages 153-166.
    6. Chongshi Gu & Xiao Fu & Chenfei Shao & Zhongwen Shi & Huaizhi Su, 2020. "Application of Spatiotemporal Hybrid Model of Deformation in Safety Monitoring of High Arch Dams: A Case Study," IJERPH, MDPI, vol. 17(1), pages 1-25, January.

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