Implementing Spatial Segregation Measures in R

Abstract

Reliable and accurate estimation of residential segregation between population groups is important for understanding the extent of social cohesion and integration in our society. Although there have been considerable methodological advances in the measurement of segregation over the last several decades, the recently developed measures have not been widely used in the literature, in part due to their complex calculation. To address this problem, we have implemented several newly proposed segregation indices in R, an open source software environment for statistical computing and graphics, as a package called seg. Although there are already a few standalone applications and add-on packages that provide access to similar methods, our implementation has a number of advantages over the existing tools. First, our implementation is flexible in the sense that it provides detailed control over the calculation process with a wide range of input parameters. Most of the parameters have carefully chosen defaults, which perform acceptably in many situations, so less experienced users can also use the implemented functions without too much difficulty. Second, there is no need to export results to other software programs for further analysis. We provide coercion methods that enable the transformation of our output classes into general R classes, so the user can use thousands of standard and modern statistical techniques, which are already available in R, for the post-processing of the results. Third, our implementation does not require commercial software to operate, so it is accessible to a wider group of people.

The Spatial Scale and Spatial Configuration of Residential Settlement: Measuring Segregation in the Postbellum South

“…The other method used the geocoded point data directly, so that segregation at finer scales could be detected. This method used R code described in Hong, O’Sullivan and Sadahiro (2014). SpatialSeg includes a kernel density function, but it is not documented so we cannot reproduce it in R. Therefore our results for distance-based segregation using point data do not include a kernel density function.…”

Multidimensional urban segregation: toward a neural network measure

“…Based on information theoretic entropy, they measure the difference between the entropy of the global distribution at the city scale and local distributions (we shall consider here each IRIS within its administrative neighbourhoodwith 91 such neighbourhoods in Paris). A standard information theory index is the H index defined in [34,35], implemented in the R package seg [26]. Other standard segregation indices include the R-index (relative diversity) and the D-index (dissimilarity).…”

Implementing a Multilevel Index of Dissimilarity in R with a case study of the changing scales of residential ethnic segregation in England and Wales

“…We are not the first to see the advantages of using R to promote the ease of access to and reproducibility of geographical analysis (Brunsdon, 2016), nor to measure segregation: seg is a package that provides functions for measuring spatial segregation (Hong et al, 2014).…”