JOURNAL ARTICLE

Stakeholders' longitudinal perspectives on a large public housing redevelopment in Los Angeles, California.

  • Published In: Journal of Urban Regeneration & Renewal, 2024, v. 18, n. 1. P. 75 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Perrigo, Judith L.; Scott, Jose J.; Shier, Victoria; Datar, Ashlesha 3 of 3

Abstract

This article examines longitudinal perspectives of service-oriented stakeholders on the Choice Neighborhoods Initiative (CNI)-funded redevelopment of Jordan Downs, a large public housing community in Watts, Los Angeles. Using qualitative interviews conducted before and two years into the redevelopment, the study identifies four key themes: persistent "community ghosts" reflecting historical mistrust rooted in decades of disinvestment; growing optimism about neighborhood regeneration; ongoing concerns about physical and psychological displacement of low-income residents; and ambivalence toward the introduction of mixed-income housing, highlighting potential social and cultural tensions. The findings emphasize the importance of sustained community engagement, transparent communication, and proactive social integration efforts to address mistrust and displacement fears while fostering inclusive, thriving neighborhoods. The study contributes real-time insights into public housing redevelopment under CNI, informing future policy and practice in similar urban contexts.

Additional Information

  • Source:Journal of Urban Regeneration & Renewal. 2024/09, Vol. 18, Issue 1, p75
  • Document Type:Article
  • Subject Area:Geography and Cartography
  • Publication Date:2024
  • ISSN:1752-9638
  • DOI:10.69554/mixv3597
  • Accession Number:179070254
  • Copyright Statement:Copyright of Journal of Urban Regeneration & Renewal is the property of Henry Stewart Publications LLP 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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