From Aspatial to Spatial, from Global to Local and Individual: Are We on the Right Track to Spatialize Segregation Measures?
01 Jan 2016-pp 77-98
TL;DR: In this paper, the authors provide an assessment of the state of the art in measuring spatial segregation and assess new challenges, in particular due to the increasing importance of survey data such as the ACS.
Abstract: Today, numerous segregation measures have been proposed, partly reflecting the multi-facet or multi-dimensional nature of segregation, but also the fact that we have different opinions of what segregation entails. Despite many spatial measures have been introduced, approaches to spatialize segregation measures are limited to a few. Therefore, this chapter reflects systemically upon how space was (re)introduced into measuring segregation from multiple fronts. After summarizing the lines of discussion on the dimensions of segregation, the practice of cluster determination using spatial autocorrelation/association measures and their relationships with segregation along the spatial dimension will be critically reviewed. The objective is to identify the critical elements that form the foundation of measuring spatial segregation to be used to develop meaningful spatial segregation measures in the future or to evaluate if a particular spatial measure is effective in capturing the essence of segregation. Thus, using these fundamental characteristics, this chapter also provides an assessment of the state of measuring spatial segregation. Early development of segregation measures was partly driven by the availability of census data. The chapter also assesses new challenges, in particular due to the increasing importance of survey data such as the ACS. On the other hand, individual-level data are becoming more accessible. Distances between individuals may play a more important role in measuring segregation in the future. A brief example of distance-based measure is provided at the end of the chapter.
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TL;DR: A new term is proposed, multi-contextual segregation, to define segregation in various space-time contexts in daily life.
59 citations
TL;DR: This paper reviews the development of spatial segregation measures, particularly focusing on the mathematical formulation of spatial arrangement/relations, and presents an overview of existing software tools that are readily available for calculating some of the reviewed measures.
Abstract: 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.
54 citations
Cites background from "From Aspatial to Spatial, from Glob..."
...Some of these issues have been touched upon in a recent review (Wong, 2016)....
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...Wong (2016) argues that despite decades of research on measuring segregation, a consistent and generally agreeable definition of segregation has not yet been formulated....
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...The uneven distribution of groups across urban space has long been a fundamental characteristic of modern cities (Lloyd et al., 2015; Wong, 2016)....
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TL;DR: This article presents and demonstrates a multiscalar approach for studying segregation and clustering that avoids the modifiable areal unit problem, including the part of the problem related to the area of reference.
Abstract: One problem encountered in analyses based on data aggregated into areal units is that the results can depend on the delineation of the areal units. Therefore, a particular aggregation at a specific...
49 citations
TL;DR: The successful integration of a comprehensive concept of segregation, high-resolution data and fine-grained spatiotemporal approaches to assessing segregation and environmental exposure would provide more nuanced and robust findings on the associations between segregation and disparities in environmental exposure and their health impacts.
Abstract: Many environmental justice studies have sought to examine the effect of residential segregation on unequal exposure to environmental factors among different social groups, but little is known about how segregation in non-residential contexts affects such disparity. Based on a review of the relevant literature, this paper discusses the limitations of traditional residence-based approaches in examining the association between socioeconomic or racial/ethnic segregation and unequal environmental exposure in environmental justice research. It emphasizes that future research needs to go beyond residential segregation by considering the full spectrum of segregation experienced by people in various geographic and temporal contexts of everyday life. Along with this comprehensive understanding of segregation, the paper also highlights the importance of assessing environmental exposure at a high spatiotemporal resolution in environmental justice research. The successful integration of a comprehensive concept of segregation, high-resolution data and fine-grained spatiotemporal approaches to assessing segregation and environmental exposure would provide more nuanced and robust findings on the associations between segregation and disparities in environmental exposure and their health impacts. Moreover, it would also contribute to significantly expanding the scope of environmental justice research.
48 citations
Cites background from "From Aspatial to Spatial, from Glob..."
...However, some scholars have been skeptical about whether spatial autocorrelation and local spatial statistical approaches can improve the measurement of segregation levels [35,67], arguing that a high degree of positive spatial autocorrelation does not always indicate a high level of segregation....
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...There has been a long history of measuring residential segregation by developing indices in sociology and demography [35], but it was only in the early 21st century that researchers began to use measures of residential segregation for environmental justice or environmental health issues [22–26,28,29,32,36–38]....
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...Although this index has been the most commonly used, even until recently due to its easy calculation [35,43], it has long been criticized as an “aspatial” measure that only considers the racial/socioeconomic composition within each areal unit and is thus incapable of capturing spatial relationships between areal units or population groups [43,48]....
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...research and whether they involve a conceptually and theoretically agreeable meaning of segregation [35,42]....
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...However, some scholars have been skeptical about whether spatial autocorrelation and local spatial statistical approaches can improve the measurement of segregation levels [35,67], arguing that a high degree of positive spatial autocorrelation does not always indicate a high level of segregation....
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TL;DR: This article analyzed small area ethnic segregation in England and Wales using a spatially-weighted approach for 2011 Census data across all (small) areas, rather than just cities, and demonstrated the non-uniform scale effects of segregation between each ethnic group.
Abstract: Ethnic residential geographies have become increasingly spatially complex. While urban diversity is by far the dominant pattern in the UK, over the last two decades suburban and rural areas have experienced a modest but steady growth of ethnic minority populations. Yet despite these emerging patterns, a bias persists whereby most studies of ethnic residential segregation are concerned solely with metropolitan places. While spatial and local measures enable a more sophisticated analysis of the intricate geographical and scalar variations in residential segregation than traditional ‘global’ approaches, there have been surprisingly few analyses of the local dimensions of ethnic residential patterning, and these have tended to be metro-focused. This study analyses small area ethnic segregation in England and Wales using a spatially-weighted approach for 2011 Census data across all (small) areas, rather than just cities. To briefly summarise, the results demonstrate (i) the non-uniform scale effects of segregation between each ethnic group; (ii) spatial ‘thresholds’ at which segregation can be found, which challenge established wisdom about the relative levels of segregation between ethnic groups; (iii) the high spatial variability in segregation levels; and (iv) how segregation dimensions and group proportions are not strongly related in all neighbourhoods, providing justification for their use in conjunction. Exploring segregation across a national context, the research develops understandings of ethnic group interactions between spaces and across scales, and advances hitherto underdeveloped debates about the complexity of the conceptual and empirical distinctions that can be made between the dimensions of segregation.
33 citations
Cites background from "From Aspatial to Spatial, from Glob..."
...An adaptable approach to defining “neighbourhood” as offered by local measurement overcomes the assumptions made via boundary definitions (reflected in the Modifiable Areal Unit Problem; Wong, 2016), and allows for a more flexible exploration of multiscale segregation....
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...…Feitosa, Câmara, Monteiro, Koschitzki, & Silva, 2007; Harris, 2014; Morrill, 1991; Reardon & O’Sullivan, 2004; Wong, 1997; and see the overview by Wong, 2016), demonstrating the ways in which segregation can vary across space and by scale, and hence the importance of accounting for its local…...
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References
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TL;DR: In this paper, a new general class of local indicators of spatial association (LISA) is proposed, which allow for the decomposition of global indicators, such as Moran's I, into the contribution of each observation.
Abstract: The capabilities for visualization, rapid data retrieval, and manipulation in geographic information systems (GIS) have created the need for new techniques of exploratory data analysis that focus on the “spatial” aspects of the data. The identification of local patterns of spatial association is an important concern in this respect. In this paper, I outline a new general class of local indicators of spatial association (LISA) and show how they allow for the decomposition of global indicators, such as Moran's I, into the contribution of each observation. The LISA statistics serve two purposes. On one hand, they may be interpreted as indicators of local pockets of nonstationarity, or hot spots, similar to the Gi and G*i statistics of Getis and Ord (1992). On the other hand, they may be used to assess the influence of individual locations on the magnitude of the global statistic and to identify “outliers,” as in Anselin's Moran scatterplot (1993a). An initial evaluation of the properties of a LISA statistic is carried out for the local Moran, which is applied in a study of the spatial pattern of conflict for African countries and in a number of Monte Carlo simulations.
8,933 citations
TL;DR: In this article, a family of statistics, G, is introduced to evaluate the spatial association of a variable within a specified distance of a single point, and a comparison is made between a general G statistic and Moran's I for similar hypothetical and empirical conditions.
Abstract: Introduced in this paper is a family of statistics, G, that can be used as a measure of spatial association in a number of circumstances. The basic statistic is derived, its properties are identified, and its advantages explained. Several of the G statistics make it possible to evaluate the spatial association of a variable within a specified distance of a single point. A comparison is made between a general G statistic and Moran’s I for similar hypothetical and empirical conditions. The empirical work includes studies of sudden infant death syndrome by county in North Carolina and dwelling unit prices in metropolitan San Diego by zip-code districts. Results indicate that G statistics should be used in conjunction with I in order to identify characteristics of patterns not revealed by the I statistic alone and, specifically, the G i and G i ∗ statistics enable us to detect local “pockets” of dependence that may not show up when using global statistics.
4,532 citations
TL;DR: In this article, residential segregation is viewed as a multidimensional phenomenon varying along five distinct axes of measurement: evenness exposure concentration centralization and clustering, and 20 indices of segregation are surveyed and related conceptually to 1 of the five dimensions.
Abstract: This paper conceives of residential segregation as a multidimensional phenomenon varying along 5 distinct axes of measurement: evenness exposure concentration centralization and clustering. 20 indices of segregation are surveyed and related conceptually to 1 of the 5 dimensions. Using data from a large set of US metropolitan areas the indices are intercorrelated and factor analyzed. Orthogonal and oblique rotations produce pattern matrices consistent with the postulated dimensional structure. Based on the factor analyses and other information 1 index was chosen to represent each of the 5 dimensions and these selections were confirmed with a principal components analysis. The paper recommends adopting these indices as standard indicators in future studies of segregation. (authors)
2,833 citations
2,323 citations
TL;DR: In this article, the authors derive and evaluate measures of multigroup segregation, such as the disproportionality in group proportions across organizational units, the strength of association between nominal variables indexing group and organizational unit membership, the ratio of between-unit diversity to total diversity, and the weighted average of two-group segregation indices.
Abstract: In this paper we derive and evaluate measures of multigroup segregation. After describing four ways to conceptualize the measurement of multigroup segregation—as the disproportionality in group (e.g., race) proportions across organizational units (e.g., schools or census tracts), as the strength of association between nominal variables indexing group and organizational unit membership, as the ratio of between-unit diversity to total diversity, and as the weighted average of two-group segregation indices—we derive six multigroup segregation indices: a dissimilarity index (D), a Gini index (G), an information theory index (H), a squared coefficient of variation index (C), a relative diversity index (R), and a normalized exposure index (P). We evaluate these six indices against a set of seven desirable properties of segregation indices. We conclude that the information theory index H is the most conceptually and mathematically satisfactory index, since it alone obeys the principle of transfers in the multigr...
742 citations