An Open-Source Software for Calculating Indices of Urban Residential Segregation

Apparicio, Philippe; Martori, Joan Carles; Pearson, Amber L.; Fournier, Éric; Apparicio, Denis

doi:10.1177/0894439313504539

Cited by 49 publications

(37 citation statements)

References 49 publications

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“…The ‘CARBayes’ (https://cran.r-project.org/web/packages/CARBayes/index.html) R package implemented the inference by Lee et al () for the index of dissimilarity and for the local and global relative centralisation index. The stand‐alone software, Geo‐Segregation Analyzer (Apparicio et al ) is a multi‐platform application that can calculate more than forty residential segregation indices. Particularly, the results of local measures can be mapped in the built‐in viewer, enabling exploration of spatial variations in segregation.…”

Section: Discussionmentioning

confidence: 99%

Spatial Segregation Measures: A Methodological Review

Yao

Wong

Bailey

et al. 2018

Tijd voor Econ & Soc Geog

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Quantitative indices of segregation are powerful tools for summarising the spatial relationships between population groups and thereby providing the basis for analysis and public policy intervention. While the broad concept of segregation may be intuitive, measurement is challenging because of the complexity of varied dimensions and spatial arrangements. Many traditional measures can be criticised for over‐simplification or over‐reduction, not least in their treatment of geographical space. Over the last several decades, however, a series of measures has been developed to explicitly incorporate the spatial arrangement of population groups as well as their interactions. This paper reviews the development of spatial segregation measures, particularly focusing on the mathematical formulation of spatial arrangement/relations. In addition, several related issues are discussed, including representation of spatial interaction, spatial scale and statistical inferences. Also, this paper presents an overview of existing software tools that are readily available for calculating some of the reviewed measures. Finally, discussions on challenges and future research are provided.

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Section: Discussionmentioning

confidence: 99%

Spatial Segregation Measures: A Methodological Review

Yao

Wong

Bailey

et al. 2018

Tijd voor Econ & Soc Geog

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“…In terms of the methods used to calculate diversity, we have used Theil's (Theil & Finizza, 1971) relative entropy index, which provides a measure of the homogeneity (a single, ethnic, group dominates) or heterogeneity (all groups are evenly balanced) of populations across different geographies and institutions. The score varies between 0 (totally homogeneous) and 1 (totally heterogeneous) and was calculated using the open source geosegregation analyser tool (Apparicio, Martori, Pearson, Fournier, & Apparicio, 2014). There are various measures of diversity and segregation, and there is considerable debate about the advantages and disadvantages of various methods in measuring school-level segregation (Allen & Vignoles, 2007;Gorard & Taylor, 2002) and at the neighbourhood scale (Simpson, 2007).…”

Section: Methodsmentioning

confidence: 99%

The spatial dynamics of race in the transition to university: Diverse cities and White campuses in U.K. higher education

Gamsu

Donnelly

Harris

2018

Population Space and Place

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Using exceptionally detailed administrative data on all 412,000 students attending university in the United Kingdom in 2014–2015 combined with spatial census data from 2011, we explore for the first time how the ethnic composition of where students grow up is linked to where they attend university. We calculate a “diversity score” for every U.K. university, which is then compared with the ethnic diversity of the surrounding area, allowing us to explore the institutional geography of ethnicity in U.K. universities. These scores provide the basis for a multilevel analysis of factors influencing whether students move towards more or less ethnically diverse universities than where they have grown up. White students are more likely than their ethnic‐minority peers to move towards a university that is more diverse than their home neighbourhood. We thus explore how students' mobility decisions for university are influenced by the uneven geography of race in U.K. cities and universities.

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“…Although there are a few recently developed standalone applications and add-on packages that provide access to and its spatial associates [7], [8], and the spatial segregation measures [9], the present implementation has a number of advantages over the existing tools.…”

Section: Discussionmentioning

confidence: 99%

Implementing Spatial Segregation Measures in R

2014

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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.

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An Open-Source Software for Calculating Indices of Urban Residential Segregation

Cited by 49 publications

References 49 publications

Spatial Segregation Measures: A Methodological Review

Spatial Segregation Measures: A Methodological Review

The spatial dynamics of race in the transition to university: Diverse cities and White campuses in U.K. higher education

Implementing Spatial Segregation Measures in R

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