Gentrification, Displacement, and GHG Emissions at Transit-Oriented Communities

Sustainable transportation investments and transit-oriented development (TOD) may produce unintended outcomes related to gentrification and displacement, raising concerns around equity. However, under the right circumstances, TOD could lead to desirable outcomes, resulting in a significant reduction in greenhouse gas (GHG) emissions. However, gentrification is associated with a range of outcomes: the spatial and financial instability of current residents as well as cultural displacement, eviction, expulsion, and exclusion. Under this broader context, this research will investigate factors contributing to equitable transit-oriented communities. What trends related to neighborhood change may be underway before and after transit investment? How can neighborhood stabilization efforts support environmental justice? Furthermore, if households are being displaced to remote suburbs with limited transit service (compared to their prior urban locations), the net effect could be a possible mode shift and the resulting changes in vehicle miles traveled (VMT) and GHG emissions. Hence, the primary objectives of this research can be categorized into two key components. These include the examination of gentrification and the subsequent displacement, as well as their repercussions on the local community and GHG emissions. (I). Gentrification Analysis: This project will assess if transit investment is causing gentrification to fulfill the first objective. Traditionally, gentrification was studied mostly through qualitative methods of observation and residents’ accounts of their lived experiences. However, over the past two decades, due to the increasing severity of gentrification-induced displacement, there have been growing efforts to quantify and model the process. This research uses household-level tracking data from Data Axle (formerly InfoUSA). In addition to that, longitudinal American Community Survey data at the census block group level will be used to explore and predict changes in the socio-economic status of neighborhoods. Machine learning (ML) models will be developed to calculate a gentrification score using multiple variables, such as income levels, ethnic composition, rental costs, educational attainment, etc. The analysis is conducted for years 2010, 2020, and 2030 using tract or block-level Census data, and the physical scope of the project is the rail-served urban areas in the state of Utah (seven counties). The algorithm modeling the socio-economic transition across the counties’ Census tracts/block groups is trained and tested with 2010 and 2020 data and then used to predict the socioeconomic scores for the year 2030. The results are then checked against actual values for 2010 and 2020. (II). Displacement Examination: The displacement scenario will be analyzed if the study finds any evidence of rail-induced gentrification. This project will adopt a unique and innovative method of measuring displacement at the household level by using the Data Axle databases for the years 2006–2022 (and 2023 once that data becomes available). The initial study will be conducted for the rail-served urban areas in the state of Utah—seven counties served by the Utah Transit Authority (UTA). The Data Axle U.S. consumer database is one of the best in the industry. It combines household-level data from over 100 contributing sources and is updated on a monthly basis. The data is compiled based on information from new utility connections and changes, real estate tax assessments and deed transfers, voter registrations, credit card billing statements, telephone white page directories, public records such as pilot licenses, bankruptcies, hunting licenses, and boat registrations, as well as other sources. Possible data categories include but are not limited to household location (longitude and latitude coordinates, street address, zip code, city, county) in each year, household wealth estimate, household income estimate, household ethnicity, house value estimate, length of residence at the current location, ownership status (renter/owner), and location type (single family dwelling unit, multi family dwelling unit, retirement home, nursing home, trailer). Data Axle data is provided at the household level, with some categories broken down by an individual household member (i.e., ethnicity, age, education). Using the Data Axle database, individual households will be tracked as they move out of the transit buffer and either (1) relocate within the buffer or (2) move away from the buffer. These groups will be stratified based on the data categories listed above to profile each of the subgroups and, as a final result, define and describe the displaced population. The end product is a detailed description of the group but also physical locations—at the most granular level, buildings that each household is moving out of and eventually into. In addition, rent level changes will be tracked for areas with the highest occurrence of displacement. This approach is innovative and highly informative. Displacement has not been studied at such a granular level before. (III). Socio-economic and neighborhood change analysis: The displacement data, which is the point data where each household is represented by a point with specific longitude and latitude coordinates, will be overlayed with the gentrification data (map) to further investigate the relationship between changes in the socio-economic status of neighborhoods at the tract and block group levels and displacement within rail station buffers. (IV). VMT and GHG Emissions Estimation: Based on the displacement findings, the study will estimate changes in VMT and GHG emissions as a result of displacement. Similar to the gentrification analysis, the research team will explore the possibility of using machine learning models to predict changes in VMT based on built environment features, commonly known as D variables (density, diversity, design destination, and distance to transit). Then GHG emissions associated with the VMT change will be calculated based on the emission factor from the US EPA. The calculated GHG emissions will be further calibrated using other sources, such as the 2022 Utah Household Travel Survey data. (V). Existing policies reviews: Lastly, the research team will examine whether transit operators are adjusting routes, schedules, and service types to better serve pockets of poverty in the suburbs. In addition, the research team will investigate whether cities are adopting regulatory tools and subsidies to help such households remain in transit-rich urban areas.

  • Supplemental Notes:
    • Funding: USDOT: $85,216 Matching: $50,604


  • English


  • Status: Active
  • Funding: $135820
  • Contract Numbers:


  • 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

    Tian, Guang

  • Performing Organizations:

    University of Utah, Salt Lake City

    City & Metropolitan Planning
    201 South Presidents Circle
    Salt Lake City, UT  United States  84112
  • Principal Investigators:

    Hong, Andy

    Ewing, Reid

  • Start Date: 20230901
  • Expected Completion Date: 20241031
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers Program

Subject/Index Terms

Filing Info

  • Accession Number: 01900181
  • Record Type: Research project
  • Source Agency: Center for Equitable Transit-Oriented Communities (CETOC)
  • Contract Numbers: 69A3552348337
  • Files: RIP
  • Created Date: Nov 20 2023 4:20PM