Using Secondary Data to Measure, Monitor and Visualise Spatio-Temporal Uncertainties in Geodemographics

Geodemographic classifications are categorical summary measures of the built, social and economic characteristics of small geographical areas. There are many geodemographic classifications supplied by both the commercial and non-commercial sectors; however, the most widely used non-commercial geodemographic is the Office for National Statistics (ONS) Output Area Classification (OAC) which was constructed entirely from the 2001 Census data. This classification has generated great impact with a user community drawn principally from across the public sector (www.areaclassification.org). The success and growing user base of 2001 OAC has led to the ONS supporting the construction of a new classification utilising the 2011 Census data once released.

A common criticism levied at classifications which are built entirely from Census data is that they are insufficiently contemporary given that collated data are not available for some years after each Census, and, that over time, these data may become increasingly unrepresentative of current population and built structures within small areas. The 2001 OAC methodology contained no mechanism through which these errors could be assessed over time, or for the classification to be reviewed and possibly updated utilising more recent data.

As such, within the historical context of the 2001 OAC, this project will integrate secondary data to implement a methodology of screening of small area residential structures over time, and create temporal measures of uncertainty that can be both communicated to policy-practitioner audiences, and furthermore, be utilised to provide updates to the 2001 OAC. This work will establish a methodology which can then be used prospectively in the construction of the 2011 OAC.

Who will benefit from this research?

This research project will directly benefit users of the current 2001 OAC classification; and furthermore, through future implementation of the methodological framework that will be developed by this research project, any prospective users of the 2011 classification once developed. As such, specific beneficiaries of this project will principally be drawn from the public sector where the main user base of OAC resides. This includes numerous local authorities, health sector analysts, education policy makers and the police and justice sector. There is likely more limited use within the private sector; however, this is likely to grow with OAC 2011 given the more user friendly outputs and associated profiling tools that are planned.
A by no means comprehensive list of end users for this research is suggested by the case study section on the OACUG website: http://areaclassification.org.uk/case-studies/. The OACUG mailing list comprises around 200 registered addresses and the events held annually usually attract around 100 people. Subscribers to the OACoding software now total 288 users. This is not the full extent of OAC users, which is much higher given that the classification can be downloaded without registration from a number of sources.

How will they benefit from this research?

Geodemographic classifications are used principally by the public sector to improve service delivery through generalised mapping of the characteristics of small areas. Typically the use of these representations concern policy targeting and intervention strategies that are designed to improve living conditions of local populations. For example, these types of classification may be used to target health screening to "at risk" populations; or, to select schools which might be most appropriately visited by a university as part of widening participation initiatives.
Given that current Census based geodemographics do not capture the dynamics of change in small areas over time, these representations may eventually become less effective for describing the characteristics of areas; and as such, erroneous decisions could be made. For applications in the public sector, this has significant implications as the outcome targeting could result in changes in life chances for populations. For example, local population change might result in the aggregate characteristics of an area moving from a low to high risk for some negative health outcome. Targeting without a classification that incorporates these temporal changes may result in the populations of these areas not being offered an appropriate health intervention strategy. As such, the methodology that will be implemented by this research is critically important for public sector uses of geodemographics influencing life chances, as it provides the critical temporal review and potential update of a classification, and additionally a spatially located measure of uncertainty over time.