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Team: John Lauermann, Yuanhao Wu, Alice Viggiani, Anna Feldman, Ziqi Wang, Nathan Smash
This is a national longitudinal tract database on gentrification patterns in American cities. This is a spatial data science project that seeks to map varying degrees of gentrification intensity across most US metropolitan and micropolitan communities. The map was developed by John Lauermann's lab group in the School of Information at Pratt Institute. The work received support from the US National Science Foundation, award #2306194.
The map is available in two formats:
Gentrification-related data from 1990 to 2020, adjusted to 2020 census tract boundaries. This is based on Decennial Census and American Community Survey data, drawn from National Historical GIS using the IPUMS API. Crosswalks are based on the NHGIS Geographic Crosswalk methodology. It covers ~56,000 census tracts in ~880 core-based statistical areas, with all data crosswalked to 2020 census boundaries. We recommend using this version of the map for most applications.
Gentrification indicators from 1970 to 2020, adjusted to 2010 census tract boundaries. This is based on Decennial Census and American Community Survey data, drawn from National Historical GIS using the IPUMS API. Crosswalks are based on methods from both NHGIS Geographic Crosswalks and the Longitudinal Tract Data Base. It covers ~49,000 census tracts in ~860 core-based statistical areas, with all data crosswalked to 2010 census boundaries. While this map may be useful for historical research, we do not recommend using it for most applications due to spatial noise introduced through the geographic crosswalking method.
The replication code are available on this GitHub repository in three folders:
01_data contains the code to construct gentrification indicator data for the main map product (1990–2020 on 2020 tract boundaries).02_data_historic contains the code to construct the historical version of gentrification indicator data (1970–2020 on 2010 tract boundaries).03_spatial contains the code needed to construct spatial boundary files, which can be joined to data from the prior two folders for mapping.The statistical workflow runs on R, primarily using ipumsr for querying APIs, dplyr for data management, and psych and cluster for calculating gentrification scores. The spatial boundary files are developed in Python, primarily using ipumpspy and requests for querying APIs and geopandas for geoprocessing. Each script defines its required packages, uses the IPUMS API to call source data, and uses project-oriented workflows with relative file paths. To run the scripts, download the entire folder structure to your local drive and run from any directory. You'll also need a free IPUMS account and API key (register here).
Lauermann J, Wu Y, Feldman A, et al. (2026) National Gentrification Intensity Map. Harvard Dataverse. Available at: https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/DPKO3I