raceland

The raceland package implements a computational framework for a pattern-based, zoneless analysis, and visualization of (ethno)racial topography (Dmowska et al., 2020). It is a reimagined approach for analyzing residential segregation and racial diversity based on the concept of ‘landscape’ used in the domain of landscape ecology. A racial landscape, represented by a high-resolution raster grid with each cell containing only inhabitants of a single race, is quantified by two metrics (entropy and mutual information) derived from Information Theory concept (IT). Entropy is the measure of racial diversity and mutual information measures racial segregation.

Racial landscape method is based on the raster gridded data, and unlike the previous methods, does not depend on the division of 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. Racial landscape method also allows for performing calculations at different spatial scales.

Installation

You can install the released version of raceland from CRAN with:

install.packages("raceland")

You can install the development version from GitHub with:

# install.packages("remotes")
remotes::install_github("Nowosad/raceland")

Example 

library(raceland)
library(terra)
#> terra 1.5.40
# Plot the input data
race_raster = rast(system.file("extdata/race_raster.tif", package = "raceland"))
plot(race_raster)

# Construct racial landscape
real_raster = create_realizations(x = race_raster, n = 100)
race_colors = c("#F16667", "#6EBE44", "#7E69AF", "#C77213","#F8DF1D")
plot(real_raster, col = race_colors, maxnl = 9)

# Plot racial ladnscape 
plot_realization(x = real_raster[[1]], y = race_raster, hex = race_colors)

# Calculate local subpopulation densities
dens_raster = create_densities(real_raster, race_raster, window_size = 10)
plot(dens_raster, maxnl = 9)

# Calculate IT-metrics 
metr_df = calculate_metrics(x = real_raster, w = dens_raster,
                            neighbourhood = 4, fun = "mean", 
                            size = NULL, threshold = 1)
head(metr_df)
#>   realization row col      ent  joinent  condent    mutinf
#> 1           1   1   1 1.634765 3.137711 1.502945 0.1318199
#> 2           2   1   1 1.633231 3.165357 1.532126 0.1011056
#> 3           3   1   1 1.639965 3.164693 1.524728 0.1152377
#> 4           4   1   1 1.649191 3.181056 1.531865 0.1173264
#> 5           5   1   1 1.640224 3.167782 1.527558 0.1126660
#> 6           6   1   1 1.634800 3.149787 1.514986 0.1198139
# Summarize IT metrics 
summary(metr_df[, c("ent", "mutinf")])
#>       ent            mutinf       
#>  Min.   :1.608   Min.   :0.09286  
#>  1st Qu.:1.629   1st Qu.:0.10838  
#>  Median :1.635   Median :0.11413  
#>  Mean   :1.635   Mean   :0.11429  
#>  3rd Qu.:1.640   3rd Qu.:0.11990  
#>  Max.   :1.656   Max.   :0.13964

References

Contribution

Contributions to this package are welcome. The preferred method of contribution is through a GitHub pull request. Feel free to contact us by creating an issue.