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Comparative Spatial Segregation Analytics

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Abstract

Comparative segregation analysis holds the potential to provide rich insights into urban socio-spatial dynamics. However, comparisons of the levels of segregation between two, or more, cities at the same point in time can be complicated by different spatial contexts as well as ethnic, racial, and class distributions. The extent to which differences in segregation between two cities is due to differences in spatial structure or to differences in composition remains an open question. This paper develops a framework to disentangle the contributions of spatial structure and composition in carrying out comparative segregation analysis. The approach uses spatially explicit counterfactuals embedded in a Shapley decomposition. We illustrate this framework in a case study of the 50 largest metropolitan statistical areas in the U.S.

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Notes

  1. http://www.censusscope.org/us/print_rank_dissimilarity_white_black.html.

  2. https://www.washingtonpost.com/graphics/2018/national/segregation-us-cities/?noredirect=on&utm_term=.b8e434f29177.

  3. A referee pointed out that a more general term might be “aggregation unit” rather than spatial structure. We agree that our framework can be generalized, but we see the concept of the aggregation unit as only one component of a city’s spatial structure.

  4. Multiscalar in this context refers to the fact that groups of people can be separated by residential housing patterns. Micro-level segregation may manifest when small pockets of self-similar neighborhoods are interspersed with one another, whereas macro-level segregation may manifest when residents of two different groups inhabit opposite sides of a large region. For further discussion the reader is directed toward Reardon et al. (2008).

  5. We note that all code data, figures, and tables necessary to recreate the full analysis are open-source and available upon request. The empirical analysis in this paper uses the segregation package (Cortes et al. 2019) from the Python Spatial Analysis Library PySAL.

  6. Since we have 1225 point estimates, supplementary materials with all comparisons is available upon request.

  7. Results for all 50 pairwise comparisons are available upon request.

  8. We note that higher-resolution images are available upon request and can be generated from the open-source code comprising this analysis.

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Authors and Affiliations

  1. Center for Geospatial Sciences, University of California, Riverside, California, 92521, USA

    Sergio Joseph Rey, Renan Cortes & Elijah Knaap

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  1. Sergio Joseph Rey
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  2. Renan Cortes
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Correspondence to Sergio Joseph Rey.

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Rey, S.J., Cortes, R. & Knaap, E. Comparative Spatial Segregation Analytics. Spat Demogr 9, 31–56 (2021). https://doi.org/10.1007/s40980-021-00075-w

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