This Interactive Gentrification Definitions Explorer tool is intended to help researchers, communities, and/or other interested groups build locally-informed methods to quantify gentrification within their own unique communities by using data, indicators, and methods from previously published works. By aggregating multiple data sources across 6 decades (1970s - 2020s) for all major metropolitan areas in the US (places with >50 tracts), this interactive document provides a step-by-step walk through of several key considerations when determining which tracts may be experiencing gentrification within their communities.
To use this document, users can to select a time frame (by decade span) and stackable geographies (Census Places) to view the way how their communities have changed with regard to 8 indicators (Median Household Income, Median Rent, Higher Education, Percent Non-Hispanic White, Housing Structure Age, Household Tenure, and Percent Above the Federal Poverty Level). Users can select any combination of indicators, and define the criteria for each indicator to select which tracts would be eligible for gentrification, and among those which would be positive indicators.
Once users select the indicators and criteria, they can compare how their selected definitions of gentrification relate to various health outcomes, or compare their method with Urban Displacement's Gentrification Typologies. Finally, the tool allows for data downloads, which returns a .csv with census tract IDs and indicator labels to be used in any additional further analysis.
The repository contains the data, processing scripts, and code required to create a customizable gentrification definition explorer tool, using RMarkdown with Shiny integration to create an interactive document.
The following libraries and related dependencies are used to generate this interactive markdown document
# From RCRAN
install.packages(c(
"dplyr", "tidyr", "foreign", # Data Processing
"tidycensus", # Census API
"shiny", # Shiny
"tmap", "tigris", "sf", "rgdal", # Geospatial
"ggplot2", "DT", "plotly" # Visuals
)
In the top level directory, files are organized in the following sub-directory as follows:
-
raw data: contains all downloaded files (not available in API format) used within the dashboard -
scripts: scripts which read, process, and combine data used in the tool. -
outputs: .RDS or .csv files generated by scripts -
notebooks: exploratory notebooks used to inform dashboard component features, this section is not maintained. -
www: assets, styling, images generated by MITREShiny package
The following figure depicts the flow of data from source (green) through scripts (orange) to intermediate files (blue) into the gentrification tool (.Rmd).
All the original data and resulting output files directly used for this project can be downloaded directed from Gentrification Data Box link. https://mitre.box.com/s/igd4vdj7tvqhwgitftxfjiemsrjv3v2s
The original data sources used in this project are publicly available:
-
Longitudinal Tract Database (LTDB) Diversity and Disparities (brown.edu)
-
U.S. Small Area Life Expectancy Estimates (USALEEP) NVSS - United States Small-Area Life Expectancy Estimates Project (cdc.gov)
-
PLACES Census Tract 2020 Release PLACES: Local Data for Better Health | CDC
-
U.S. Census through Load US Census Boundary and Attribute Data as tidyverse and sf-Ready Data Frames • tidycensus (walker-data.com)
-
Redlining HOLC Data Mapping Inequality (richmond.edu)
-
Urban Displacement Typologies GitHub The Urban Displacement Project's Displacement Typology Map code
- run the following scripts in order:
acs_data_pull.R,ltdb_cleaning.R,combine.R, andgeo.R. - open the
Gentrification Tool.Rmdand click on Run Document button
For more information, please contact Karen Jiang kjiang@mitre.org{.email} or Hannah De los Santos hdelossantos@mitre.org{.email}.
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