Transit-Induced Gentrification: Who Will Stay, and Who Will Go?

Dawkins, Casey J.; Moeckel, Rolf

doi:10.1080/10511482.2016.1138986

Cited by 91 publications

(57 citation statements)

References 20 publications

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“…In this version of the ICT we have been able to follow a few lines of inquiry, e.g., examining impacts by age, gender, socio economic status, and a simply dichotomised ethnicity. Given debates on sustainable transport and gentrification, this is pertinent [ 38 , 39 ]. We anticipate in the future calculating propensities to cycle stratified by ethnicity and car ownership.…”

Section: Discussionmentioning

confidence: 99%

Development of the Impacts of Cycling Tool (ICT): A modelling study and web tool for evaluating health and environmental impacts of cycling uptake

et al. 2018

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BackgroundA modal shift to cycling has the potential to reduce greenhouse gas emissions and provide health co-benefits. Methods, models, and tools are needed to estimate the potential for cycling uptake and communicate to policy makers the range of impacts this would have.Methods and findingsThe Impacts of Cycling Tool (ICT) is an open source model with a web interface for visualising travel patterns and comparing the impacts of different scenarios of cycling uptake. It is currently applied to England. The ICT allows users to visualise individual and trip-level data from the English National Travel Survey (NTS), 2004–2014 sample, 132,000 adults. It models scenarios in which there is an increase in the proportion of the population who cycle regularly, using a distance-based propensity approach to model which trips would be cycled. From this, the model estimates likely impact on travel patterns, health, and greenhouse gas emissions. Estimates of nonoccupational physical activity are generated by fusing the NTS with the English Active People Survey (APS, 2013–2014, 559,515 adults) to create a synthetic population. Under ‘equity’ scenarios, we investigate what would happen if cycling levels increased equally among all age and gender categories, as opposed to in proportion to the profile of current cyclists. Under electric assist bike (pedelecs or ‘e-bike’) scenarios, the probability of cycling longer trips increases, based on the e-bike data from the Netherlands, 2013–2014 Dutch Travel Survey (50,868 adults).Outcomes are presented across domains including transport (trip duration and trips by mode), health (physical activity levels, years of life lost), and car transport–related CO2 emissions. Results can be visualised for the whole population and various subpopulations (region, age, gender, and ethnicity). The tool is available at www.pct.bike/ict. If the proportion of the English population who cycle regularly increased from 4.8% to 25%, then there would be notable reductions in car miles and passenger related CO2 emissions (2.2%) and health benefits (2.1% reduction in years of life lost due to premature mortality). If the new cyclists had access to e-bikes, then mortality reductions would be similar, while the reduction in car miles and CO2 emissions would be larger (2.7%). If take-up of cycling occurred equally by gender and age (under 80 years), then health benefits would be marginally greater (2.2%) but reduction in CO2 slightly smaller (1.8%). The study is limited by the quality and comparability of the input data (including reliance on self-report behaviours). As with all modelling studies, many assumptions are required and potentially important pathways excluded (e.g. injury, air pollution, and noise pollution).ConclusionThis study demonstrates a generalisable approach for using travel survey data to model scenarios of cycling uptake that can be applied to a wide range of settings. The use of individual-level data allows investigation of a wide range of outcomes, and variation across subgroups. Future work s...

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Section: Discussionmentioning

confidence: 99%

Development of the Impacts of Cycling Tool (ICT): A modelling study and web tool for evaluating health and environmental impacts of cycling uptake

et al. 2018

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“…Of interest in this chapter is the extent to which LRT, SCT and BRT station areas attract population, households and housing consistent with expectations (Belzer et al, 2007;Belzer and Poticha, 2010;Carrigan et al, 2013;Cervero et al, 2004;Chapple and Loukaitou-Sideris, 2019;Dawkins and Buehler, 2010;Dawkins and Moeckel, 2016). Indeed, only one study addresses population and housing change for all transit systems in the U.S.-the Center for Transit Oriented Development (2014)-but it does not differentiate by type of system, distance from transit stations, or provide detail on the race/ethnicity of people, households by age and type and income, and housing based on tenure.…”

Section: Introductionmentioning

confidence: 67%

The Link Between Transit Station Proximity and Real Estate Rents, Jobs, People and Housing with Transit and Land Use Planning Implications

Nelson¹,

Hibberd²,

Dixon³

2019

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The contents of this report reflect the views of the authors, who are solely responsible for the facts and the accuracy of the material and information presented herein. This document is disseminated under the sponsorship of the U.S. Department of Transportation University Transportation Centers Program in the interest of information exchange. The U.S. Government assumes no liability for the contents or use thereof. The contents do not necessarily reflect the official views of the U.S. Government. This report does not constitute a standard, specification, or regulation.

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“…[27][28][29] Likewise, neighbourhood and equity advocates have expressed concern that new TOD projects will lure wealthier and less diverse residents, which will lead to the displacement of existing populations, a phenomenon known as transit-induced gentrification (TIG). 30 TIG, a TOD socioeconomic by-product, is defined as a phenomenon, whereby the provision of transit service, particularly LRT, and associated area of development, change in the direction of neighbourhood 'upscaling'. 31 The role of LRT investments in triggering gentrification and displacement of low-income households has been examined in several cities throughout the USA, such as Portland, OR and Denver, CO. 32 33 For instance, the median household income increased by 10% in Denver, CO neighbourhoods near LRT stations and from 1990 to 2000 the housing values increased approximately 25% for those located within a mile from a LRT station.…”

Section: Open Accessmentioning

confidence: 99%

Case-comparison study protocol for gauging effects of neighbourhood trends and sickness: examining the perceptions of transit-Induced gentrification in Prince George’s County

et al. 2020

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IntroductionImpoverished neighbourhoods and communities of colour often bear the brunt of unintended transit-oriented development (TOD) impacts. These impacts have been known to come in the form of transit-induced gentrification (TIG), a socioeconomic by-product of TOD defined as a phenomenon that occurs when the provision of transit service, particularly light rail transit (LRT), ‘up-scales’ nearby neighbourhood(s) and displaces existing residents. Consequently, TIG or even the perception of TIG can impact health outcomes (eg, anxiety) and social determinants of health (SDOH) (eg, crime).Methods/AnalysisIn 2022, the purple line (PL), a 16.2 mile LRT line, is opening in Prince George’s County, Maryland, a suburb of Washington, DC, comprised of over 80% African American and Hispanic residents. By taking advantage of this natural experiment, we are proposing the GENTS (Gauging Effects of Neighborhood Trends and Sickness: Examining the Perceptions of Transit-Induced Gentrification in Prince George’s County) Study in order to evaluate perceived TIG and associated health outcome and SDOH changes, at two points in time, among Prince George’s County adults in a prospective case-comparison design during the pre-PL LRT period. Descriptive analysis and latent growth curve modelling will be used to examine these changes over time.Ethics and DisseminationEthics approval has been granted by the University of Maryland Institutional Review Board. The GENTS Study will identify temporal changes in perceived TIG, health outcomes and SDOH among case and comparison residents before the completion and operation of the PL LRT, an under researched period of TOD. The dissemination of GENTS Study findings will be able to address research questions and policy issues that are specifically tailored to PG County while also providing more effective procedural solutions for other regions undergoing TOD and TIG risks.

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Transit-Induced Gentrification: Who Will Stay, and Who Will Go?

Cited by 91 publications

References 20 publications

Development of the Impacts of Cycling Tool (ICT): A modelling study and web tool for evaluating health and environmental impacts of cycling uptake

Development of the Impacts of Cycling Tool (ICT): A modelling study and web tool for evaluating health and environmental impacts of cycling uptake

The Link Between Transit Station Proximity and Real Estate Rents, Jobs, People and Housing with Transit and Land Use Planning Implications

Case-comparison study protocol for gauging effects of neighbourhood trends and sickness: examining the perceptions of transit-Induced gentrification in Prince George’s County

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