The Application of Zone-Design Methodology in the 2001 UK Census

Martin, David; Nolan, Abigail; Tranmer, Mark

doi:10.1068/a3497

Cited by 73 publications

(64 citation statements)

References 11 publications

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“…OAs were first used in the 2001 Census in England and Wales and they were constructed using clusters of adjacent unit postcodes; they were intended to have similar population sizes and to be as socially homogenous as possible according to tenure of household and dwelling type. 2 The automated zone design methodology used to generate OAs is detailed by Martin et al (2001). Super OAs are built from groups of OAs and they were developed so as to enable government departments to report statistics at a relatively fine spatial scale but with a small risk of disclosure of information about individuals.…”

Section: Datamentioning

confidence: 99%

Spatial scale and small area population statistics for England and Wales

Lloyd

2015

International Journal of Geographical Information Science

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It is well-known that the results of analyses of aggregate data, such as those provided as outputs from censuses, are dependent on the size and shape of the zones used to report the data. However, many users of aggregate census data do not consider how far the zones utilised in their analyses capture spatial information about the population sub-groups they are studying. In addition, future data collection strategies should account for such issues. This article takes as its focus England and Wales, and it seeks to assess how far output areas (OAs) and aggregations of OAs capture information in selected population sub-groups and, therefore, how important it might be to use zones of a particular size in order to properly analyse the geographies of these subgroups. The article uses the index of dissimilarity, D xy (for groups x and y), and variograms to assess spatial variation in population sub-groups as represented by counts for OAs, lower layer super output areas (LSOAs), middle layer super output areas (MSOAs) and local authority districts (LAs), all produced as outputs from the Census in England and Wales. The analyses show how much information is contained at each spatial scale for sub-categories relating to age, ethnic group, housing tenure, car or van availability, qualifications, employment, limiting long-term illness (LLTI) and National Statistics Socio-economic Classification (NS-SeC). The amount of variation contained by each level of the hierarchy of zones differs markedly by population sub-group. LAs capture most (83%) of the variation in the spread of the population by the binary categorisation of White/Black, Asian and Minority Ethnic (far more than for any other variable), but there remains considerable local variation. The results suggest that zones larger than OAs are not geographically detailed enough to enable meaningful analysis of local-level differences between places and thus any alternative to the Census in the United Kingdom (with England and Wales as a specific case) must provide zones equivalent in size to OAs. If estimates are available only for larger areas then much information will be lost and our ability to explore how sub-group characteristics, or the relationships between variables, differ between localities will be considerably diminished. The results also provide evidence on some of the ways in which the population of England and Wales was geographically distributed in 2011.ARTICLE HISTORY

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Section: Datamentioning

confidence: 99%

Spatial scale and small area population statistics for England and Wales

Lloyd

2015

International Journal of Geographical Information Science

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“…Firstly, we are looking for localized concentrations of investors and therefore the most local level provides the greatest resolution of neighborhood difference. Secondly, OAs were designed to maximize tenure homogeneity, and as we are explicitly interested in residential consumption, similarity of properties was deemed a useful characteristic (Martin et al, 2001). …”

Section: Methodsmentioning

confidence: 99%

Overseas investment into London: Imprint, impact and pied-à-terre urbanism

DeVerteuil

Manley

2017

Environ Plan A

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This paper focuses on the spatial imprint and social impacts of the emerging geographies of concentrated overseas investment into London's high-end real estate market, particularly the boroughs of Westminster and Kensington and Chelsea. Framed by literatures on the 1% and the super-rich, and based on a mixed methodological approach of qualitative interviews with intermediaries and a quantitative mapping of overseas investors using 2011 census data, the results speak to the pervasive nature of ''safe-haven'' seeking in London real estate and its attendant transnational provenance set within a laissez-faire regulatory framework. In so doing, it makes an important contribution to the geographies of the super-rich, the class geographies of London, and the broader sense that overseas investors are producing what we call ''pied-a`-terre'' urbanism which builds on a conventional gentrification framework (exclusionary displacement and a more affluent incoming group) but also exceeds it in several ways, leading to an increasingly socially attenuated landscape. This exceeding relates to: a different kind of rent gap, in that it is not speculative but safe-haven seeking, a guaranteed return on investment, and occurs without previous disinvestment; the agents are not traditional gentrifiers; the transnational nature of the process, with no attachment to particular places like in the traditional gentrification model; and a process focused on superprime areas and completely independent of the existing gentrification narrative in London. KeywordsSuper-rich, London, overseas investment, the 1%, pied-a`-terre urbanism

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“…For instance, families with children often favour greenspace and recreational opportunities nearby, while as a matter of fact, any other Census geography). OA borders were designed to minimise within-zone homogeneity in population characteristics (population normalisation), without regard to the geographical features of the area (Martin et al, 2001; see Figure 13.1). As such, for proximity based inputs there were challenges about how such measures might be calculated, and to which area they should be attributed.…”

Section: Consumers In Their Built Environment Contextmentioning

confidence: 99%