Introduction
This blog series walks through the steps taken to replicate the recent research paper “A small area index of gentrification, applied to New York City”, from a 2022 issue of the International Journal of Geographical Information Science (Johnson et al. 2022). Gentrification is commonly defined as a process by which the geography of urban areas is reshaped by an influx of wealthy businesses and residents. It is viewed positively by some as an economic strategy to attract new investment and real estate development, and negatively by others as it can drive up property values and displace local, often long-term residents and business owners. As such, it is an important topic of study for urban geographers, planners, and social scientists.
In the paper, the authors collect US census tract data from two different time periods (2000 and 2020) about five gentrification indicators. These indicators were chosen by domain experts based on an extensive review of the gentrification literature.
They then calculate how these indicators change between 2000 and 2020, combine them to create one overall gentrification index for each census tract, then map it for New York City.
What’s next
In the following blogs, I’ll cover the data engineering, data exploration, machine learning, and spatial analysis techniques I used to replicate the methodology in New York City, then take it a step further and apply it to other US metropolitan areas such as Philadelphia, Seattle, Washington DC, and San Diego.
Jump to blog #2 to learn how to get all of the data together and start preparing it for analysis…
References (for the blog series)
Johnson, G.D., Checker, M., Larson, S., Kodali, H., 2022. A small area index of gentrification, applied to New York City. International Journal of Geographical Information Science. 36(1), 137-157.
Hair Jr., J.F., Anderson, R.E., Tatham, R.L., Black, W.C., 1998. Multivariate Data Analysis (5th Edition). Prentice-Hall, Inc., Upper Saddle River, NJ.
Article Discussion: