Changing neighborhoods, shifting connections: mapping relational geographies of gentrification using social media data

ABSTRACT

New sources of ‘big data’ are regularly described as revolutionizing the study of urban life. Of particular interest is analyzing gentrification, which has proven a challenging endeavor with conventional methods. Big data may offer a new approach to the persistent problem of defining and measuring gentrification, while also allowing us to rethink broader questions about theory and methodology in urban geography. Using geotagged Twitter data, we demonstrate how the changing geographies of users’ tweets are proxies for the evolving social and spatial contours of urban neighborhoods. We use the case of Lexington, Kentucky to analyze the mobilities and relational connections of neighborhood residents and visitors as gentrification intensified over time. We argue that these kinds of big data allow for an analytical approach that focuses on the dynamic, relational connections between people and places, and provides a useful, additional avenue in understanding a process as complex and multifaceted as gentrification.

KEYWORDS:

• Gentrification
• big data
• spatial networks
• social media
• Twitter
• Lexington, KY
• relational geography

Acknowledgments

The authors would like to thank Qingqing Chen for her assistance with preliminary data analysis for this article.

Data Availability Statement

The data and code that support the findings of this study are available on https://github.com/atepoorthuis/relationalgeographiesofgentrification and http://doi.org/10.5281/zenodo.4535943.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. Specifically, a bounding box with 37.67372° N 85.08614° W as the southwest corner and 38.38294° N 83.92788° W as the northeast corner. This includes the nearby towns of Frankfort to the west, Georgetown and Paris to the north and Harrodsburg and Richmond in the south.

2. To improve readability of the paper we do not use quotes around home for the rest of the article although our intent remains the same.

3. Geotagged tweets generally have a relatively precise point location (stored as longitude/latitude) attached to them which allows them to be easily aggregated to larger and flexibly defined spatial units that conform to the lived experience of neighborhoods. While this been used successfully at a range of scales in previous work (cf. Poorthuis, 2018; Poorthuis et al., 2020; Shelton et al., 2015) the analysis in this paper uses conventional Census tracts as aggregation units in order to more easily connect with data from the American Community Survey.

4. These periods do not have the same length as they have been chosen to divide the dataset equally in terms of total number of tweets. Because geotagged tweeting became less prevalent from mid-2015 onwards, the latter period is longer. Defining time periods in this way also allows us to distinguish between the period prior to the end of 2013 when many key commercial establishments first opened (West Sixth Brewery, Arcadium, North Lime Coffee and Donuts) and from 2014 onward as they received more visitors.

5. Data spans the entire metropolitan region of Lexington, KY but figures zoom in on Lexington’s urban core for clarity and simplicity.

6. Because the two time periods are of different length, we do not use absolute counts. Rather we use the relative frequency of visits (Number of Visits to Tract_i/Total Number of Visits to All Tracts) to make the periods comparable.

7. These characteristics are derived from the American Community Survey 2012–2016 5-year estimates (as to overlap with our study period).