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  "description" : "\u003ch1\u003eNational Gentrification Intensity Map\u003c/h1\u003e\u003cp\u003eTeam: \u003ca target=\"_blank\" href=\"https://www.pratt.edu/people/john-lauermann/\" rel=\"nofollow ugc noopener noreferrer\"\u003eJohn Lauermann\u003c/a\u003e, \u003ca target=\"_blank\" href=\"https://www.linkedin.com/in/yuanhao-wu-80603723a/\" rel=\"nofollow ugc noopener noreferrer\"\u003eYuanhao Wu\u003c/a\u003e, \u003ca target=\"_blank\" href=\"https://www.aliceviggiani.com/\" rel=\"nofollow ugc noopener noreferrer\"\u003eAlice Viggiani\u003c/a\u003e, \u003ca target=\"_blank\" href=\"https://www.linkedin.com/in/annaelsafeldman/\" rel=\"nofollow ugc noopener noreferrer\"\u003eAnna Feldman\u003c/a\u003e, \u003ca target=\"_blank\" href=\"https://www.linkedin.com/in/ziqi-wang-0623/\" rel=\"nofollow ugc noopener noreferrer\"\u003eZiqi Wang\u003c/a\u003e, \u003ca target=\"_blank\" href=\"https://www.linkedin.com/in/nathan-smash-b6b93a24a/\" rel=\"nofollow ugc noopener noreferrer\"\u003eNathan Smash\u003c/a\u003e\u003c/p\u003e\u003cp\u003eThis 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 \u003ca target=\"_blank\" href=\"https://www.pratt.edu/people/john-lauermann/\" rel=\"nofollow ugc noopener noreferrer\"\u003eJohn Lauermann's\u003c/a\u003e lab group in the \u003ca target=\"_blank\" href=\"https://www.pratt.edu/information/\" rel=\"nofollow ugc noopener noreferrer\"\u003eSchool of Information\u003c/a\u003e at Pratt Institute. The work received support from the US National Science Foundation, \u003ca target=\"_blank\" href=\"https://www.nsf.gov/awardsearch/show-award?AWD_ID=2306194\" rel=\"nofollow ugc noopener noreferrer\"\u003eaward #2306194\u003c/a\u003e.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003ch1\u003eMap products\u003c/h1\u003e\u003cp\u003eThe map is available in two formats:\u003c/p\u003e\u003ch4\u003eGentrification patterns on 2020 tract boundaries\u003c/h4\u003e\u003cp\u003eGentrification-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 \u003ca target=\"_blank\" href=\"https://www.nhgis.org/\" rel=\"nofollow ugc noopener noreferrer\"\u003eNational Historical GIS\u003c/a\u003e using the \u003ca target=\"_blank\" href=\"https://developer.ipums.org/docs/v2/get-started/\" rel=\"nofollow ugc noopener noreferrer\"\u003eIPUMS API\u003c/a\u003e. Crosswalks are based on the \u003ca target=\"_blank\" href=\"https://www.nhgis.org/geographic-crosswalks\" rel=\"nofollow ugc noopener noreferrer\"\u003eNHGIS Geographic Crosswalk\u003c/a\u003e 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. \u003c/p\u003e\u003ch4\u003eHistorical data on 2010 tract boundaries\u003c/h4\u003e\u003cp\u003eGentrification indicators from 1970 to 2020, adjusted to 2010 census tract boundaries. This is based on Decennial Census and American Community Survey data, drawn from \u003ca target=\"_blank\" href=\"https://www.nhgis.org/\" rel=\"nofollow ugc noopener noreferrer\"\u003eNational Historical GIS\u003c/a\u003e using the \u003ca target=\"_blank\" href=\"https://developer.ipums.org/docs/v2/get-started/\" rel=\"nofollow ugc noopener noreferrer\"\u003eIPUMS API\u003c/a\u003e. Crosswalks are based on methods from both \u003ca target=\"_blank\" href=\"https://www.nhgis.org/geographic-crosswalks\" rel=\"nofollow ugc noopener noreferrer\"\u003eNHGIS Geographic Crosswalks\u003c/a\u003e and the \u003ca target=\"_blank\" href=\"https://s4.ad.brown.edu/projects/diversity/researcher/bridging.htm\" rel=\"nofollow ugc noopener noreferrer\"\u003eLongitudinal Tract Data Base\u003c/a\u003e. 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. \u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003ch1\u003eHow to replicate\u003c/h1\u003e\u003cp\u003eThe replication code are available on this \u003ca target=\"_blank\" href=\"https://github.com/johnlauermann/gentrification-intensity-map\" rel=\"nofollow ugc noopener noreferrer\"\u003eGitHub repository\u003c/a\u003e in three folders:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003ccode\u003e01_data\u003c/code\u003e contains the code to construct gentrification indicator data for the main map product (1990–2020 on 2020 tract boundaries).\u003c/li\u003e\u003cli\u003e\u003ccode\u003e02_data_historic\u003c/code\u003e contains the code to construct the historical version of gentrification indicator data (1970–2020 on 2010 tract boundaries).\u003c/li\u003e\u003cli\u003e\u003ccode\u003e03_spatial\u003c/code\u003e contains the code needed to construct spatial boundary files, which can be joined to data from the prior two folders for mapping.\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eThe statistical workflow runs on R, primarily using \u003ccode\u003eipumsr\u003c/code\u003e for querying APIs, \u003ccode\u003edplyr\u003c/code\u003e for data management, and \u003ccode\u003epsych\u003c/code\u003e and \u003ccode\u003ecluster\u003c/code\u003e for calculating gentrification scores. The spatial boundary files are developed in Python, primarily using \u003ccode\u003eipumpspy\u003c/code\u003e and \u003ccode\u003erequests\u003c/code\u003e for querying APIs and \u003ccode\u003egeopandas\u003c/code\u003e for geoprocessing. Each script defines its required packages, uses the \u003ca target=\"_blank\" href=\"https://developer.ipums.org/docs/v2/apiprogram/\" rel=\"nofollow ugc noopener noreferrer\"\u003eIPUMS API\u003c/a\u003e 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 (\u003ca target=\"_blank\" href=\"https://developer.ipums.org/docs/v2/get-started/\" rel=\"nofollow ugc noopener noreferrer\"\u003eregister here\u003c/a\u003e).\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003ch1\u003eHow to cite\u003c/h1\u003e\u003cp\u003eLauermann J, Wu Y, Feldman A, et al. (2026) \u003ci\u003eNational Gentrification Intensity Map\u003c/i\u003e. Harvard Dataverse. Available at: \u003ca target=\"_blank\" href=\"https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/DPKO3I\" rel=\"nofollow ugc noopener noreferrer\"\u003ehttps://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/DPKO3I \u003c/a\u003e\u003c/p\u003e", 
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