Geodemographics as the “analysis of people by where they live” has origins in urban sociology and social mapping, and is experiencing a renaissance in applied spatial demography. However, some commentators have expressed reservations about the statistical limitations of common geodemographic practices, especially focusing on the potential internal heterogeneity of the geodemographic groupings, as well as the problem of clearly identifying predictor variables that might account for or explain the socioeconomic patterns revealed by geodemographic analyses. In this paper we argue that geodemographic typologies are structured methods for making sense of the spatial and socioeconomic patterns encoded within complex datasets such as national census data. By treating geodemographics as more a framework than a tool for analysis in its own right we are able to integrate it with the flexibility and statistical conventions offered by multilevel modeling. We demonstrate this with a case study of whether pupils from different types of neighborhood in Birmingham, England are more or less likely to attend their nearest state-funded secondary school and how that likelihood varies with the ethnic composition of the neighborhood. In so doing we build on previous research suggesting that ethnic segregation between schools is at least equal to that between neighborhoods in England and speculate in this regard on the consequences of current government plans to extend choice to parents within a schools market.
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How well geodemographic classifications “capture” the geographical patterning of society (e.g., patterns of demography or of consumption) depends not only on the base units of analysis—such as postal or census zones—but also the number of clusters those units are grouped into, on a “like-with-like” basis, to form the geodemographic classification. In fact, Callingham (2006) has suggested that there is little difference in precision between classifications based on census small areas or those based on even finer postal geographies; what matters more is the number of geodemographic clusters used for analysis.
Geodemographic classifications are sometimes portrayed as “black boxes” because the exact choice of variables used to profile small areas, and the weightings attached to those variables, are not usually published (for commercial reasons). “Open geodemographics” has emerged in response to this in the U.K. (Vickers and Rees 2007; Vickers et al. 2005).
See also http://www.cmm.bristol.ac.uk/research/Lemma/ where there is a range of papers about multilevel modeling, as well as access to multilevel software and tutorials.
Geodemographic practices of labeling places and people may be far from harmless (see Burrows et al. 2005), although the supposed negative impacts largely are conjecture.
In England, 93% of the school age population attend a state-funded school.
Specifically we have excluded pupils living in Lower Layer Super Output Areas that touch the metropolitan boundary of Birmingham. See http://www.neighbourhood.statistics.gov.uk for more information about this aggregated census geography of England and Wales.
A likely, although not deliberately intended consequence of excluding the more suburban areas of Birmingham LEA from the analysis, is that straight-line distances to school are likely to approximate the actual distances, given the higher density of road and pedestrian routes within inner city areas.
Another and perhaps more relevant question is whether pupils of a given ethnic group are less likely to attend schools that go beyond a certain threshold proportion of other ethnic groups within them—that it is the ethnic composition of schools, not neighborhoods, that discourages applications. Unfortunately this is not straightforward to model because we are analyzing school choices after the event. If any one school is predominantly “nonwhite” then, by definition, not many white pupils can be attending it. To fit what is essentially the same information to both sides of the regression equation (i.e., as both the Y and an X) is to create a tautology. How to avoid this is commented upon in the section “Measuring ‘Neighborhood Effects’” of the paper.
See MLwiN Help file, version 2.03.03.
This implies a criticism of geodemographics which may, itself, be unfair: geodemographics usefully can identify places that do have high crime rates without it being necessary to identify quite why they are high. But, in terms of policing crime proactively rather than reactively, and in terms of addressing the policy question of what causes crime, then geodemographics alone is not sufficient (see Farr 2006 for an interesting example of how geodemographic methodologies can be combined with qualitative ones for managing health outcomes).
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We would like to thank three anonymous referees for their helpful and insightful comments upon a previous version of this manuscript.
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Harris, R., Johnston, R. & Burgess, S. Neighborhoods, Ethnicity and School Choice: Developing a Statistical Framework for Geodemographic Analysis. Popul Res Policy Rev 26, 553–579 (2007). https://doi.org/10.1007/s11113-007-9042-9