Inclusion and democracy.

Background Neighbourhood socioeconomic status (SES) may affect rich and poor residents differentially. Two models are proposed. Model 1: living in a non-deprived neighbourhood is better for health because better collective material and social resources are available. Model 2: being poor (rich) relative to the neighbourhood average is associated with worse (better) health because of the discrepancy between an individual's situation and those around them. Methods Individual data from the Whitehall II study covering health, SES, and perceived status were linked to census data on neighbourhood deprivation. Results Both individual and neighbourhood deprivation increased the risk of poor general and mental health. There was a suggestion that the effect of living in a deprived area was more marked for poorer individuals, although interactions were not statistically significant. Poor people in poor neighbourhoods reported more financial and neighbourhood problems and rated themselves lowest on the ladder of society. Conclusions We found no evidence that personal poverty combined with affluent neighbourhood had negative health consequences. Rather, living in a deprived neighbourhood may have the most negative health effects on poorer individuals, possibly because they are more dependent on collective resources in the neighbourhood.

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Neighbourhood deprivation and health: does it affect us all equally?

Subsequent to riots in UK northern cities, claims of self-segregation and polarised communities are examined with data unique to the city of Bradford and first results from the UK population census. Statistics relating to race often reinforce misleading stereotypes that are unhelpful to the development of appropriate social policy. Previous studies of indices of segregation are shown to be inadequate through lack of consideration of change over time and the confounding of population change with migration. The separation of natural change and migration supports survey evidence that dispersal of South Asian populations has taken place at the same time as absolute and relative growth. Social policy will do well to take on board these demographic facts in a positive inclusive approach to all residents in all areas.

Statistics of Racial Segregation: Measures, Evidence and Policy:

Summary. The paper describes the creation of the Office for National Statistics 2001 output area classification, which was created in collaboration with the authors.The classification places each 2001 census output area into one of seven clusters based on the socio-economic attributes of the residents of each area. The classification uses cluster analysis to reduce 41 census variables to a single socio-economic indicator.The classification was made available with a host of supporting and descriptive information as a National Statistic via National Statistics on line. The classification forms part of a suite of area classifications that were produced by the Office for National Statistics from 2001 census data. Classifications of local authorities, statistical wards and health areas are also available.

https://www.jstor.org/stable/pdfplus/4623165.pdf

Creating the UK National Statistics 2001 output area classification

The residential segregation of ethnic groups in urban areas remains an issue of importance for policy-making in multicultural societies, such as England's, with levels of segregation frequently lin...

Are There Ethnic Enclaves/Ghettos in English Cities?.

Population microdata comprise a list of households and individuals each with an associated list of characteristics. Unfortunately for Britain no small-area microdata exist that offers a broad range of demographic and socioeconomic variables contained in the national census. There are a number of approaches to the reconstruction of such spatially detailed microdata including data fusion synthetic reconstruction (chain imputation) and reweighting. One variant of the reweighting approach involves the selection of a combination of households from the 1% household Samples of Anonymized Records that best fits known small-area are constraints (the published census tabulations). In this paper the implementation of this "combinatorial optimization" technique is more thoroughly examined. First the combinatorial optimization process is reviewed. Then a number of methodological innovations designed to improve the accuracy and consistency of resulting outputs are reported. Subsequently the problems of evaluating the outputs are discussed and a new strategy is outlined for assessing the quality of synthetic microdata. The strategy proposed is generally applicable to all such data irrespective of their means of generation. The paper goes on to provide an extensive assessment of the quality of synthetic microdata produced using the combinatorial optimization approach. This represents the first time that such an evaluation has ever been undertaken. The results highlight the degree to which such data may be able to meet specific user needs. The paper concludes by offering an illustration of the "added value" that can be obtained by combining information from public-use microdata with published small-area tabulations. (authors)

An evaluation of the combinatorial optimisation approach to the creation of synthetic microdata

Goodness-of-fit tests are widely used by geographers, but choice remains difficult. Our overview starts with a conceptual examination of the nature of fit. Three important approaches to assessing the goodness-of-fit of categorical data are reviewed and appraised: statistics tested against the h ² distribution, the normal Z score and its variants, and measures derived from information theory. The forms of the phi and psi statistics highlighted in earlier geographical work are shown to be closely approximated by a simple measure of absolute error. Empirical examples help to illustrate the relative utility of these tests for a range of purposes.

Evaluating Goodness-of-Fit Measures for Synthetic Microdata

We report on an examination of two geodemographic classification systems based on an analysis of 1991 census variables, for districts, wards and census enumeration districts in England and Wales. We also review the associations among the variables examined, the extent to which certain underlying components might account for the overall variation and the types of areas that are least typical. The results show that small areas are different in many different ways; a few dimensions cannot provide enough information to describe an area fully. Diversity on most scales remains even after geodemographicclassification, emphasizing the advantages of task-specific classification.

The diversity of diversity: a critique of geodemographic classification

The paper considers the scale – the measure, extent, and dimension – of uneven distributions in space for a wide range of census variables. While the traditional ‘index of dissimilarity’ is affected by random as well as social factors, a solution presented here allows the index to be calculated even for very small populations. Small areas across England and Wales tend to be fairly similar demographically but quite diverse on ethnic and socio-economic measures. Differences between areas become more noticeable as we move from districts, to wards, to enumeration districts, but the rate of differentiation depends heavily on the variables considered.

The Scale of Dissimilarity: Concepts, Measurement and an Application to Socio‐Economic Variation Across England and Wales

The measurement of spatial pattern is often undertaken using one of a number of single-number indices, such as the Gini coefficient, which may not illuminate certain aspects of the pattern involved—especially the degree to which the members of the reference group are spatially concentrated. We suggest an alternative approach based on a concentration profile which shows the degree to which a group is spatially concentrated according to a range of thresholds. This is illustrated with data on male unemployment in England and Wales in 1991, which also shows the importance of spatial scale to the study of concentrations and, potentially, to the formulation of spatially focused public policies.

David Voas

Papers

An evaluation of the combinatorial optimisation approach to the creation of synthetic microdata

Evaluating Goodness-of-Fit Measures for Synthetic Microdata

The diversity of diversity: a critique of geodemographic classification

The Scale of Dissimilarity: Concepts, Measurement and an Application to Socio‐Economic Variation Across England and Wales

Measuring spatial concentration: the use of threshold profiles