Racial landscapes – a pattern-based, zoneless method for analysis and visualization of racial topography


Quantifying and effectively communicating the spatio-racial distribution of urban residencies is important for taking the measure of how the multiracial society organizes itself in an urban environment. Most currently used approaches to this problem center around the calculation of segregation metrics; as such, they pertain to only a single pattern's feature and they lack a compelling visualization component. In this paper, we propose a reimagined approach to spatio-racial analysis based on the concept of landscape and landscape analysis. This approach unites quantification and visualization components of the analysis. It also quantifies the entire racial topography, not just segregation. Key novel concepts are the racial landscape (RL) and the exposure matrix. RL is a high-resolution grid in which each cell contains only inhabitants of a single race. The exposure matrix tabulates adjacencies between neighboring cells weighted by the local density of adjacent subpopulations; it provides a concise quantification of the RL pattern. Two information-theoretical metrics, derived from the exposure matrix, quantify diversity, and segregation of the RL. Segregation is quantified from cell adjacencies without the need for subdivision of the region of interest. Thus, the entire region, as well as its arbitrary subregions, are RLs quantified by their diversities and segregations. Coloring cells in RL according to combinations of their race and local densities provides a natural visualization of racial topography which serves as an “observation” that provides check on numerical metrics. The RL method is described and its application is demonstrated on Cook County, IL. An implementation of the RL method in R package accompanies this paper.

Dmowska, A., Stepinski T., Nowosad J. Racial landscapes – a pattern-based, zoneless method for analysis and visualization of racial topography. Applied Geography. 122:1-9, DOI:10.1016/j.apgeog.2020.102239