New paper by Renan Cortes and the Python Spatial Analysis Library (PySAL) team, giving a great contribution to the study of segregation. Eli Knap, one of the co-authors of the paper, has a great blog post summarizing some of the key contributions of the package.
Cortes, R. X., Rey, S., Knaap, E., & Wolf, L. J. (2019). An open-source framework for non-spatial and spatial segregation measures: the PySAL segregation module. Journal of Computational Social Science, 1-32.
Abstract:
In human geography and the urban social sciences, the segregation literature typically engages with five conceptual dimensions along which a given society may be considered segregated: evenness, isolation, clustering, concentration and centralization (all of which can incorporate or omit spatial context). Over the last several decades, dozens of segregation indices have been proposed and studied in the literature, each of which is designed to focus on the nuances of a particular dimension, or correct an oversight in earlier work. Despite their increasing proliferation, however, few of these indices remain used in practice beyond their original conception, due in part to complex formulae and data requirements, particularly for indices that incorporate spatial context. Furthermore, existing segregation software typically fails to provide inferential frameworks for either single-value or comparative hypothesis testing. To fill this gap, we develop an open-source Python package designed as a submodule for the Python Spatial Analysis Library, PySAL. This new module tackles the problem of segregation point estimation for a wide variety of spatial and aspatial segregation indices, while providing a computationally based hypothesis testing framework that relies on simulations under the null hypothesis. We illustrate the use of this new library using tract-level census data in two American cities.
image credit: Cortes et al 2019
