New Transit, Bike Infrastructure, and Green Space: Do They Have a Multiplying Effect on Gentrification and Displacement?

Researchers have documented how new rail transit and bus rapid transit (BRT), new bike infrastructure, and new parks have contributed to gentrification. Much of this research, however, has focused on one type of investment at a time, has used aggregate tract-level data, and has only examined whether gentrification follows public investment, and now whether it can also precede it. To start addressing this gap, this project seeks to disentangle the impacts of different public investments on neighborhood change. We ask: Do new transit, bike infrastructure, and green space have a multiplying effect on gentrification and displacement? Specifically, when new transit is built in underserved communities, do concurrent investments in bike infrastructure or green space increase the risks of gentrification and displacement? And does gentrification precede public investments in new transit, bike infrastructure, and green space? We will focus on four metropolitan areas in the Western U.S. (Los Angeles, Wasatch Front, Portland, and Seattle) that have seen significant investment in new rail/BRT, bike infrastructure, and parks. We will build a longitudinal dataset with household-level data from Data Axle between 2006 and 2023 in the four regions. Data for public investments will come from Transit Explorer (transit and BRT), metropolitan planning organizations and cities (bike infrastructure), and the Trust for Public Land (parks). We will classify households in gentrification-eligible tracts as treatment if within a half mile of a new public investment (e.g., new rail transit) and as control if otherwise, considering multiple combinations of proximity to several types of public investments (e.g., proximity to new rail transit and park vs. proximity to park only). We will then build mixed-effect models to track residential mobility in and out of areas near new transit but without new park investment and bike infrastructure investments and compare such residential mobility with new transit areas that do have new parks and/or new bike infrastructure. To do so, models will include interaction terms between the various treatments (e.g., transit treatment and park treatment). Thanks to this household-level dataset, we will be able to track the low-income households who will move out of various treatment areas, which will enable us to model potential displacement processes. In these models, we will control for several other variables such as neighborhood demographics, housing characteristics, crime, and other characteristics known to affect gentrification and displacement. We will use evidence from this study to define gentrification and displacement risk factors associated with new public investments in sustainable infrastructure. We will disseminate such risk factors via a peer-reviewed publication and policy brief. We believe that these risk factors will inform the planning of equitable transit-oriented developments by providing planners with information about the potential impacts of other public investments alongside transit. To make it easier for researchers to use household-level data such as Data Axle to model gentrification and displacement, we will share publicly the code we will develop to process and analyze such data. If permitted, we will share data about new parks and new bike infrastructure in the four selected metropolitan areas. Outputs will include 1. A peer-reviewed publication describing the results of the study 2. A peer-reviewed publication describing the technical aspects of using Data Axle to model neighborhood change and residential mobility 3. A policy brief for practitioners summarizing our main findings and providing recommendations about risk factors of gentrification and displacement induced by new transit on its own and in conjunction with other investments 4. A presentation for practitioners disseminated via a webinar 5. A conference presentation 6. A webpage that will host the R and Python codes we used to process and analyze Data Axle data; the page will also host all data collected and processed in this study except for Data Axle data.

• Record URL:
  https://www.uno.edu/cetoc
• Supplemental Notes:
  • Funding: $200,000 (USDOT) + $100,000 (matching funds) = $300,000 (total)

Language

• English

Project

• Status: Active
• Funding: $300000
• Contract Numbers:

  69A3552348337

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Center for Equitable Transit-Oriented Communities (CETOC)

  University of New Orleans
  New Orleans, LA  United States 
• Project Managers:

  Kline, Robin

  Danton, Bob

• Performing Organizations:

  University of Utah, Salt Lake City

  City & Metropolitan Planning
  201 South Presidents Circle
  Salt Lake City, UT  United States  84112

  Florida Atlantic University, Boca Raton

  Boca Raton, FL  United States  33431
• Principal Investigators:

  Rigolon, Alessandro

  Hong, Andy

  Renne, John

• Start Date: 20241001
• Expected Completion Date: 20250930
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Bicycle facilities; Bus rapid transit; Parks; Public transit; Rail transit; Transit oriented development
• Subject Areas: Pedestrians and Bicyclists; Planning and Forecasting; Public Transportation;

Filing Info

• Accession Number: 01928986
• Record Type: Research project
• Source Agency: Center for Equitable Transit-Oriented Communities (CETOC)
• Contract Numbers: 69A3552348337
• Files: UTC, RIP
• Created Date: Aug 27 2024 6:12PM