Segregation, desegregation, and racial balance: Status implications of these concepts

Newby, Robert G.

doi:10.1007/bf02173962

Cited by 26 publications

(10 citation statements)

References 2 publications

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“…Based upon Newby's (1982) conceptualization of spatial segregation, Morrill (1991) suggested to modify D with a term to compare the ethnic mixes of neighboring units. This measure is denoted at D(adj).…”

Section: Spatial Segregation Measuresmentioning

confidence: 99%

Comparing Traditional and Spatial Segregation Measures: A Spatial Scale Perspective¹

Wong

2004

Urban Geography

110

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Measuring the level of segregation often encounters two methodological issues: measures are sensitive to changes in the geographical scale of the data and the effectiveness of the measure in reflecting spatial segregation. Several spatial measures have been suggested to measure spatial segregation, but whether they are more or less sensitive to changes in spatial scale has not been investigated, while some spatial measures are relatively scale-insensitive. Using the 1990 Census data of 30 selected U.S. metropolitan areas, this paper demonstrates that these spatial measures, similar to the aspatial measure, report higher levels of segregation when smaller areal units are used in the analysis. Some spatial measures are even more sensitive to scale changes than aspatial measures. Certain patterns of the scale sensitivity were identified, but no general rules can be formulated. A preliminary explanation of the scale effect on spatial segregation measures is offered. [Key words: the MAUP, scale effect, dissimilarity index, spatial segregation measures, multigroup comparison.]

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Section: Spatial Segregation Measuresmentioning

confidence: 99%

Comparing Traditional and Spatial Segregation Measures: A Spatial Scale Perspective¹

Wong

2004

Urban Geography

110

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“…The calculation of D effectively assumes that people in different areal units as defined by some zoning scheme do not mix or interact. The spatial separation of population subgroups that is one aspect of the conceptualization of spatial segregation (Newby 1982) is thus completely determined by the boundary system. This drawback of D is shared by most popular measures of segregation due to the aspatial formulation of those measures.…”

Section: Measuring Segregation: Approaches and Measuresmentioning

confidence: 99%

A Surface‐Based Approach to Measuring Spatial Segregation

O’Sullivan

Wong

2007

Geographical Analysis

117

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Quantitative indices of residential segregation have been with us for half a century, but suffer significant limitations. While useful for comparison among regions, summary indices fail to reveal spatial aspects of segregation. Such measures generally consider only the population mix within zones, not between them. Zone boundaries are treated as impenetrable barriers to interaction between population subgroups, so that measurement of segregation is constrained by the zoning system, which bears no necessary relation to interaction among population subgroups. A segregation measurement approach less constrained by the chosen zoning system, which enables visualization of segregation levels at the local scale and accounts for the spatial dimension of segregation, is required. We propose a kernel density estimation approach to model spatial aspects of segregation. This provides an explicitly geographical framework for modeling and visualizing local spatial segregation. The density estimation approach lends itself to development of an index of spatial segregation with the advantage of functional compatibility with the most widely used index of segregation (the dissimilarity index D). We provide a short review of the literature on measuring segregation, briefly describe the kernel density estimation method, and illustrate how the method can be used for measuring segregation. Examples using a simulated landscape and two empirical cases in Washington, DC and Philadelphia, PA are presented.

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“…Several formulations of spatial segregation measures are based upon the spatial separation argument advocated by Newby (1982). Newby, later Morrill (1991) and Wong (1993) adopted the notion that segregation involves the spatial separation of population groups.…”

Section: Measuring Spatial Dimensions Of Segregationmentioning

confidence: 99%

“…The D index is regarded as a better index because it best captures the evenness dimension which is regarded as the most important dimension. The D index, however, has been criticized for its aspatial nature because it fails to detect changes in segregation levels when population groups in diVerent areal units are swapped (Wong, 1993), while an important dimension of segregation involves the spatial separation among population groups (Newby, 1982). Despite this criticism of the D index, the wide adoptions of the index in numerous studies re¯ect the ongoing trend in segregation study that the spatial dimension has not been explored adequately.…”

Section: Introductionmentioning

confidence: 97%

Modeling Local Segregation: A Spatial Interaction Approach

Wong¹

2002

Geographical and Environmental Modelling

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Most measures of residential segregation can be regarded as global measures because they are summary indices for the entire study region. But the level of segregation is not uniform within the region and therefore local measures are warranted. The entropy-base d diversity index is a local measure, but it fails to account for interactions between population groups across areal boundaries. In this paper, the diversity index is modiWed to accommodate spatial interaction between population groups across areal units. In addition, a set of local segregation measures is introduced to model local segregation based upon the exposure concept in segregation studies and the spatial interaction concept in geographica l modeling. A simulation and an empirical study are used to demonstrate the utilities of this modeling approach.

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Segregation, desegregation, and racial balance: Status implications of these concepts

Cited by 26 publications

References 2 publications

Comparing Traditional and Spatial Segregation Measures: A Spatial Scale Perspective¹

Comparing Traditional and Spatial Segregation Measures: A Spatial Scale Perspective¹

A Surface‐Based Approach to Measuring Spatial Segregation

Modeling Local Segregation: A Spatial Interaction Approach

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Segregation, desegregation, and racial balance: Status implications of these concepts

Cited by 26 publications

References 2 publications

Comparing Traditional and Spatial Segregation Measures: A Spatial Scale Perspective1

Comparing Traditional and Spatial Segregation Measures: A Spatial Scale Perspective1

A Surface‐Based Approach to Measuring Spatial Segregation

Modeling Local Segregation: A Spatial Interaction Approach

Contact Info

Product

Resources

About

Comparing Traditional and Spatial Segregation Measures: A Spatial Scale Perspective¹

Comparing Traditional and Spatial Segregation Measures: A Spatial Scale Perspective¹