Does cycling infrastructure prioritize gentrifying neighborhoods? The case of Mexico City

ABSTRACT

This paper aims to test the existence of a relationship between gentrification and the presence of cycling infrastructure in central Mexico City using a sample of 555 neighborhoods in central Mexico City and drawing from open spatial data. This research is contextualized within a growing body of Latin American research on state-led gentrification and the role of infrastructure as a tool for urban revalorization. Results show the existence of a clear and significant correlation between cycling infrastructure and gentrification, revealing that the city’s improved bike infrastructure has overwhelmingly favored gentrifying neighborhoods, and support existing research on these programs as a tool of gentrification. In addition, when controlling for other variables, the level of cycling infrastructure acts as the main predictor of gentrification. These findings are compelling and have implications for mobility equity policy in the Mexico City context.

KEYWORDS:

• State-led gentrification
• urban mobility
• Latin America
• global cities
• Mexico City

Acknowledgments

I would like to give very special thanks to Drs. Mara Sidney, Janice Gallagher, Jamie Lew, and Gregg Van Ryzyn for their continuous feedback, advice, and support throughout the development of this paper. Likewise, I would also thank my classmates and doctoral cohort at the Global Urban Studies Program for their feedback. Likewise, many thanks are also due to the editor and peer reviewers, whose comments and suggestions were instrumental in improving the quality of this research.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data sources

InsideAirbnb

http://insideairbnb.com/get-the-data

INEGI Mexican Census Data Portal:

https://www.inegi.org.mx/programas/ccpv/

Mexico City Cadastre:

https://sig.cdmx.gob.mx/datos

Mexico City Open Data Portal:

https://datos.cdmx.gob.mx/

OpenStreetMap (data scrapped using QGIS Quick OSM Plugin):

https://www.openstreetmap.org/

Notes

1. See: https://maps.app.goo.gl/RZdDmMidDKDwiCkb8 in 2009 versus https://maps.app.goo.gl/e5JAeoMB5ECW67k6A in 2022 and https://maps.app.goo.gl/ExGMNJF1hvjr2E2e8 in 2009 versus https://maps.app.goo.gl/h66hB3ug3buc7REcA in 2022.

2. A list of all neighborhoods can be found in Appendix A.

3. Hotels is being treated as a separate variable because Airbnbs often operate outside of traditional touristic zones and are popular in more residential neighborhoods. Thus, it could be a confounding variable.

4. Data points other than population and population density are not currently available at the neighborhood level for 2020. This data is only available at borough, tract, or block level. A summary of some demographic data at the borough level has been included in the paper but is not part of the model.

Additional information

Notes on contributors

Tamara Velasquez Leiferman

Tamara Velasquez Leiferman is a PhD student at the Global Urban Studies Program at Rutgers-Newark. Her research interests include gentrification studies and urban mobility in the context of global cities. She is currently working on her doctoral dissertation which aims to examine Mexico City’s gentrification processes.