A pattern-based approach to analysis and visualization of spatio-racial distribution

Racial geography in US urban areas is extensively studied with the emphasis on assessing the extent of racial segregation. However, the used methodology has not changed for at least two decades; it relies on calculating ratios of population counts in the entire city and its subdivisions – census aggregation areas. This has a number of limitations; the two most important are: assessment of segregation depends on the subdivisions used, segregation can only be calculated for regions with census subdivisions. Here we present a different conceptualization of racial geography, which leads to a new method called racial landscape (RL). We use block-level census data to construct a high-resolution grid where each cell represents single race inhabitants. The result is a spatial, racial pattern; a degree of spatial autocorrelation of this pattern is a measure of segregation that does not require using subdivisions. We shortly describe the RL method and its application to Cook County, IL. We also describe here its implementation in the R computational environment