This tutorial illustrates how the National Racial Geography Dataset
(NRGD2020), along with the R package
raceland, can be
employed to address racial geography problems similar to those examined
in demographic literature.
The National Racial Geography Dataset (NRGD2020) provides a high-resolution racial database for the conterminous US in 2020. The NRGD2020 utilizes Racial Landscape (RL) methodology to provide a comprehensive resource for visualization and quantitative analysis of racial geography.
NRGD2020 consists of a collection of precalculated GIS layers that can be used for quantification and visualization of racial distribution for any place in the conterminous United States at the resolution of 30-meters. In addition to 30-m resolution datasets, it also includes 10 racial diversity and 10 segregation grids depicting the level of diversity or segregation at the scale of 72km, 36km, 24km, 18km, 12km, 9km, 6km, 3km, 1.5km, 0.75km. All layers are available as GeoTiffs.
NRGD2020 datasets can be downloaded from http://www.socscape.edu.pl/index.php?id=nrgd
Racial Landscape method (RL) has been introduced by Dmowska et al (2021) as a pattern-based, zoneless method for analysis and visualization of racial geography in any user-define region. RL method is based on the raster gridded data, and unlike the previous methods, does not depend on the division for specific zones (census tract, census block, etc.). Calculation of racial diversity (entropy) and racial segregation (mutual information) can be performed for the whole area of interests (i.e., metropolitan area) without introducing any arbitrary divisions. RL method also allows for performing the calculation at different spatial scales.
Racial Landscape method has been implemented in R package -
Original paper introducing racial landscape method: Dmowska, A., Stepinski, T. F., & Nowosad, J. (2020). Racial Landscapes - a pattern-based, zoneless method for analysis and visualization of racial topography. Applied Geography, 122, 102239.
Documentation associated with the
Examples 1, 3, 4 use Atlanta dataset. Atlanta dataset can be downloaded from http://www.socscape.edu.pl/index.php?id=nrgd
Example 2 uses US-wide segregation grids that can be downloaded from http://www.socscape.edu.pl/index.php?id=nrgd
Atlanta dataset consists of 4 directories containing data for the Atlanta–Sandy Springs–Alpharetta, GA Metropolitan Statistical Area (Atlanta MSA in short):
These layers are used in the examples 1,3, and 4. RL grid racial ID consists of 6 categories that corresponds to the racial groups: 1 - Native Americans sub-populations (depicted in blue color); 2 - Asians sub-populations (depicted in red color); 3 - Blacks sub-populations (depicted in green color), 4 - Hispanics/Latino sub-populations (depicted in purple color); 5 - others sub-populations that include people who declared more than one racial groups (depicted in brown color); 6 - Whites sub-populations (depicted in yellow color),
RL image directory contains an RGB image that provides visualization of racial geography in the Atlanta MSA. This layer is included as a sample of US-wide dataset, and it is not used in the following examples.
segregation grids directory consists of 10 rasters showing spatial variability of segregation over the conterminous US at ten different scales (72-km, 36-km, 24-km, 18-km, 12-km, 9-km, 6-km, 3-km, 1.5-km, 0.75-km). Segregation is measured by the mutual information MI. Those layers are included as a sample of US-wide dataset, and they are not used in the following examples.
diversity grids consists of 10 rasters showing spatial variability of diversity over the conterminous US at ten different scales (72-km, 36-km, 24-km, 18-km, 12-km, 9-km, 6-km, 3-km, 1.5-km, 0.75-km). Diversity is measured by the Hill’s number that depicts the significant number of racial groups present in an area. Those layers are included as a sample of US-wide dataset, and they are not used in the following examples.
Figure 1 shows the main layers included in the Atlanta MSA dataset.