Adding Value to Census Data: Public Sector Applications of the Super Profiles Geodemographic Typology

Transcription

1 Adding Value to Census Data: Public Sector Applications of the Super Profiles Geodemographic Typology Peter J.B. Brown, Alex F.G. Hirschfield and Peter W.J. Batey Abstract This paper reviews a series of public sector applications of the Super Profiles geodemographic typology that was produced using data derived from the 1991 Census. The applications, often in combination with the use of GIS software, have emerged in work undertaken by the staff of the Urban Research and Policy Evaluation Regional Research Laboratory (URPERRL). Examples of applications are drawn from the fields of health care, crime pattern analysis and the monitoring and evaluation of community safety programmes, plus an examination of higher education participation rate variation. The paper concludes with brief discussion of prospects for the development of further enhancements and applications of the Super Profiles typology as a means of adding value to both the original census data from which it was derived and datasets with which the classification can be linked. Peter J.B. Brown, Alex F.G. Hirschfield and Peter W.J. Batey Urban Research and Policy Evaluation Regional Research Laboratory (URPERRL). Department of Civic Design, University of Liverpool, The Gordon Stephenson Building, 74 Bedford Street South, Liverpool L69 7ZQ, UK pjbbrown@liv.ac.uk hirsch@liv.ac.uk pwjbatey@liv.ac.uk 19

2 Adding Value to Census Data: Public Sector Applications of the Super Profiles Geodemographic Typology INTRODUCTION An aim of this paper is to illustrate some of the ways in which a geodemographic typology can provide a convenient means of adding further value to the body of census data from which such typologies are initially derived. Much of the pressure for the development of typologies of this type has come from the commercial world as part of an effort to improve methods of target marketing and product placement. However, in this paper we are able to demonstrate that, in the public sector, there is also considerable scope to exploit the discriminatory power of small area classifications in the development of applications that can provide fresh insight into familiar issues of priority identification and resource targeting. The examples used for this purpose are drawn from the wide range of research council funded and contract research projects undertaken in recent years by staff associated with the Urban Research and Policy Evaluation Regional Research Laboratory (URPERRL) which have involved the use of the Super Profiles typology. To provide a context in which to set the subsequent review of applications, the next section outlines features of the development of the Super Profiles small area classification. The main part of the paper then describes a series of examples of public sector applications of geodemographic methods. THE DEVELOPMENT OF SUPER PROFILES The Emergence of Geodemographics In Great Britain the field of geodemographics has progressed dramatically since very early efforts to develop small area typologies of the entire country that were based on information derived from the 1971 Census (see, for example, Brown, 1991 and Batey and Brown, 1995). The initial version of the ACORN typology had its origins in work undertaken in the public sector (Webber, 1977, 1985) but found a growing range of commercial applications when brought together with data relating to consumer behaviour and media preferences (Webber, 1989). Following the release of data from the 1981 Census, a number of further proprietary geodemographic systems emerged in the mid-1980s, including MOSAIC, PiN and Super Profiles. Their appearance was accompanied by a surge of interest in private sector applications, prompted by recognition of the commercial value of linking postcoded client/customer records to such typologies (see Birkin 1995, Brown 1991, Sleight, 1993). A similar wave of typology development activity was witnessed in the more recent phase of post-1991 Census analysis (Evans and Webber, 1994). A major virtue of all of these census-based classifications is that they provide simplified representations of what can be seen as the otherwise impenetrable mass of detailed raw count data held in the Small Area Statistics (SAS). They serve as an effective and efficient method of capturing the principal dimensions of variation in demographic, socio-economic, dwelling stock and tenure, etc characteristics of enumeration 20

3 districts as the geographical building blocks of the census. They thus facilitate the processes of better understanding the social geography of small areas and adding value to the underlying data. The latest version of the Super Profiles typology, based primarily upon information derived from the 1991 Census, has been used in the development of a wide variety of applications. Before describing some examples, it is appropriate to outline briefly how the typology was derived and to identify some of the distinguishing features of the Super Profiles system. Super Profiles 1994 The Super Profiles typology was developed by Batey and Brown (1994) on behalf of, and in close collaboration with, CDMS Limited, part of the Liverpool-based Littlewoods Organisation, the largest privately owned company in the UK. At that time, the latter included interests in mail order home shopping, personal finance and credit rating as well as its chain of department stores and football pools. Super Profiles is a general purpose classification of the 140,000 census enumeration districts (EDs) (England and Wales) and output areas (OAs) (Scotland) for which 1991 Census data were reported. It features three levels of description including a very detailed 160 Super Profile cluster level, an intermediate 40 Target Market level and a much broader level at which 10 Lifestyles are distinguished. The background to the development of the most recently produced version of Super Profiles is set out in Batey and Brown (1994, 1995). The stages of development of the Super Profiles area typology are described in greater detail in Brown and Batey (1994). In outline, the starting point for the exercise was the assembly of 120 census variables (85 with 100 percent coverage of households, 35 with 10 percent) from 1991 Census information at the ED level (OA, in Scotland). The mean size of English and Welsh EDs was 180 households (about 450 people) and in Scotland the mean size of the OAs was 53 households (about 130 people). Small EDs and OAs (less than 100 households) were found to exhibit different characteristics from the more populous units especially with respect to the above 10 percent variables. To avoid forcing together areas that are markedly different, the small EDs and Scottish OAs were treated separately. The extracted variables were examined and 79 were selected. Principal component analysis was employed to identify 11 dimensions of the data that could explain most of the observed variation (72 percent explained by the first six components, 25 percent by the first component). Separate cluster analyses were carried out for each of the three data sets (large English and Welsh EDs, small English and Welsh EDs and Scottish OAs) and the results brought together to produce a total of 590 so-called invisible clusters invisible as they are not made available to users. After the initial clustering stage, further layers of information were added at the 590-cluster level. This included information from the Electoral Roll (the annual register of electors eligible to vote in local and national elections), commercial trading data (Littlewoods home shopping) and the Target Group Index (TGI, produced by the British Market Research Bureau, derived from a regular survey of 24,000 respondents concerned mainly with patterns of consumption behaviour and media preferences). Variation in these 21

4 variables across the 590 clusters was examined in a similar way to the census variables. Five were considered suitable for use in further clustering (three Electoral Roll, two trading data and no TGI variables). The other variables were retained for use in describing the characteristics of individual clusters. Cluster analysis was repeated to reduce the 590 clusters to the adopted 160 Super Profile clusters, and successively to the 40 and 10 cluster levels noted above. The process of cluster description then followed in which, for example, at the 10 Lifestyle level, use was made of an index table in which the mean value of each variable for EDs in a particular cluster is compared with the national mean, with the latter set to 100. The resultant pen pictures are able to benefit from the availability of this information relating to both census and the extensive range of market-relevant TGI indicators to which reference is made above. PUBLIC SECTOR AND/OR RESEARCH APPLICATIONS OF SUPER PROFILES Introduction In recent years, staff associated with URPERRL have been commissioned by public agencies and other bodies to undertake projects in which geodemographic typologies have played a central role. In order to illustrate the variety of applications of geodemographic methods that are possible in this public sector and/or research area, we present brief descriptions of projects undertaken under the headings of health, community safety and higher education participation. Health URPERRL first became involved in the direct application of geodemographic methods in the health field. Fundamental to the development of such applications is the assembly of relevant data relating to a health condition of interest, in the form of morbidity or mortality counts by area type, which can then be related to an appropriate denominator in order to derive a rate of incidence or prevalence. This is typically achieved by analysing postcoded records of patients or deaths and assigning individual records to the appropriate Super Profile cluster, Target Market or Lifestyle. This is done most effectively with the aid of commercially available software that assigns the relevant codes to individual postcoded records by establishing a link to an underlying postcode database. Wider interest in health-related applications of geodemographic methods has been prompted by the negotiation of a licensing arrangement by the Department of Health with the suppliers, CDMS Limited, whereby the RHA Super Profile utility program (referred to here as RHASP) was made available to all Regional Health Authorities, and via them, to all District Health Authorities. The types of application made possible by these means can be exemplified with reference to work undertaken by URPERRL in collaboration with local health authorities and an application carried out by Yorkshire Health Authority. In 1991, URPERRL was first commissioned by Wirral Health to undertake a series of such projects. Several involved the use of a combination of geodemographics and geographical information system (GIS) software to assist in planning health services on the Wirral (see, for example, Brown, et al, 1994, 1995; Hirschfield, et al, 1995; 22

5 Todd, et al, 1994). The use of GIS to handle the spatial dimension of health condition variation and health care provision enabled further value to be added to health care data routinely collected within the Health Authority. It provides a flexible framework within which to study interactions between illness and a variety of social and environmental factors. Figure Super Profile Lifestyle Analysis of Standardised Mortality Ratio (SMR) for Lung Cancer for Males and Females for the (former) Yorkshire Regional Health Authority Region 1991 Note: 95% confidence limits for population aged under 75 compared with SMR for England & Wales set to Affluent Achieve Country Life Thriving Greys Settled Suburbans Nest Builders Producers Senior Citizens Urban Venturers Hard-Pressed Families Have-Nots Affluent Achievers Country Life Thriving Greys Settled Suburbans Nest Builders Producers Senior Citizens Urban Venturers Hard-Pressed Families Have-Nots Males Females The existence and/or extent of spatial patterning in health condition incidence can often be revealed more vividly in choropleth map form with the aid of a GIS. Basic GIS functions can help to reduce the misleading impression that can be conveyed by conventional choropleth maps in which all sub-areas are fully shaded, thus giving undue emphasis to larger units which are often thinly populated. The restriction of shading to within the digitally defined boundary of built-up areas likely to contain population can go some way towards overcoming this problem (see, Brown, et al, 1995). Another widely adopted method of analysis involves the production of a form of penetration ranking report. This is commonly used in commercial applications, in which a table is assembled in which, for example, the 40 Super Profile Target Markets are ranked in terms of the rate of incidence or prevalence of a particular health condition. This is illustrated below in an application relating to crime which exemplifies how the presentation of the results of such an analysis can be enhanced through the use of a special purpose program which enables the plotting of a Lorenz curve (see below). The curve is based on a comparison of the values of the cumulative percentage of condition against the corresponding cumulative percentage of the population at risk. The data upon which such analyses are based may contain relatively large counts of the numerator used in deriving the measure of interest. In these circumstances it is possible to estimate Target Market incidence rates without making an allowance for the effect of very small numbers. In the analysis of relatively rare health conditions, the effect of small numbers can be taken into account by drawing upon appropriate statistical methods. For example, Brown, et al, (1992) illustrate how a Poisson Chi-Square approach can be employed for this purpose. 23

6 A further example of the use of Super Profiles in analysing health data is taken from work undertaken by Yorkshire Regional Health Authority (1994). Figure 1 indicates the 95% confidence bands, for each Super Profile Lifestyle, within which fall the estimates of the Standardised Mortality Ratio (SMR) for Lung Cancer among male and female residents of the Yorkshire region. In both cases, a fairly consistent pattern or social gradient is revealed that highlights the degree to which rates are significantly higher among those resident in the less affluent and lower in more affluent Lifestyles. Finally, we note a further novel geodemographic application in the medical field in which the typology has been used to identify suitable control sample and contrast sample areas from which to examine cancer records as part of a study of the effect of a particular course of preventative diagnostic intervention being carried out by a stomach cancer surgeon (see, Brown, et al, 1998 and forthcoming). Community Safety In recent years there has been considerable growth in interest in the field of community safety. This embraces a range of areas of concern, including various aspects of the analysis and interpretation of patterns in criminal and anti-social behaviour and the identification and evaluation of the effectiveness of appropriate responses to be adopted by the police, other emergency services, notably the fire service, and other agencies, such as local authorities. Figure 2 Super Profile Target Market Ranking Report: Burglary Dwelling 24

7 Figure 3 Lorenz Curve : Burglary Dwelling Calls vs Residential Properties Figure 4 Super Profile Target Market Ranking Report: Sexual Offences 25

8 Figure 5: Lorenz Curve: Burglary Dwelling Calls vs Mean Total Persons Present URPERRL s involvement in this field stems from an initial joint project (Brown, et al, 1993), undertaken on behalf of Merseyside Police in collaboration with the Henry Fielding Centre for Police Studies and Crime Risk Management at the University of Manchester. The project was concerned with monitoring the effectiveness of approximately 60 projects which were undertaken, over a 12-month period, as part of the Urban Crime Fund programme. Subsequently, Economic and Social Research Council funding was secured to enable the study of links between crime and social disadvantage on Merseyside that was funded as part of the ESRC s Crime and Social Order. The principal study objectives were: a) to investigate relationships between crime and the spatial segregation of deprived people; b) to examine the extent to which crime risks (i.e. in terms of being a victim or an offender) are greater where disadvantaged areas either directly border, or are in close proximity to, affluent areas; and c) to identify the extent to which crime in disadvantaged areas is attributable to lack of social cohesion. The principal findings are discussed, for example, in Hirschfield and Bowers, 1995, 1996, 1997; Hirschfield, et al, 1995a, 1995b, 1996, and Johnson, et al, Geodemographics played an important role in the pursuit of all three objectives and provided a backdrop again which a series of issues were examined using data obtained from Merseyside Police. The other principal sources of data drawn upon in the study were 2 million command and control records (all calls to the police over a three-year period) and records of recorded crime obtained from the Integrated Criminal Justice System (ICJS). Both sets of data had a spatial reference attached to individual records, the latter to a resolution of 1 metre, which considerably facilitated their analysis within a GIS framework. Typical forms of analysis will be illustrated below which have been built upon in more recent work on behalf of the Safer Merseyside Partnership, funded in part from Single Regeneration Budget sources, in monitoring and evaluating the effectiveness of projects undertaken as part of this initiative. This work has led to a major Home Office contract (with colleagues from the Universities of Huddersfield and Hull) involving the monitoring of domestic burglary projects throughout the North of England. Here we illustrate one of the principal ways in which a geodemographic analysis can contribute to better understanding of the degree to which particular types of crime tend to be concentrated in certain types of neighbourhood or residential sector. This can be achieved using the form of penetration ranking report to which reference was made above in relation to health applications. Command and control records relating to two categories of call (domestic burglary and sexual offences) are analysed to reveal contrasting patterns of concentration in different types of area. The analyses are presented with respect to the 40 26

9 Target Market level of area type description and carried out using a widely available spreadsheet package (Microsoft Excel) that readily accommodates the required arithmetic manipulation of the initial counts of calls and population. The presentation of the results is enhanced through the use of a special purpose program that runs under Microsoft Windows to plot a Lorenz curve. The starting point for each analysis was a count of the number of calls to the police relating to domestic burglary (burglary dwelling) and sexual offence incidents, over a three-year period, for each of the 40 Target Market area types. In this case the counts were derived by first assigning grid referenced command and control records to 1991 Census EDs using Arc/Info to intersect the points with an ED boundary file. This enabled the required Super Profile Target Market and Lifestyle code of each census ED to be added to each record. It was then possible to determine the number of calls recorded among those who were resident in each of the area types. This count appears in the spreadsheet (see Figure 2) in column 4, corresponding to the Target Market indicated in column 1. Column 3 of Figure 2 contains the other crucial data item the denominator or population at risk. For this purpose, in the case of burglary dwelling, use was made of the number of residential properties to derive the information presented in other columns in the table. Firstly, the calls count enabled a rate per 1000 residential properties at risk to be derived that appears in column 5. In column 6, an index value is used to compare this rate with an overall mean rate for Merseyside that is set to 100. The table entries are then re-arranged by sorting all rows according to the rate per 1000 found in column 5. The derivation of a Lorenz curve enables an impression to be gained of the extent to which the rates of occurrence of a condition (in this case, calls to the police) are concentrated in a limited number of high response rate clusters. This is based on the estimation of the cumulative total number of cases (more precisely, the cumulative percentage) that correspond to the number (percentage) of the at-risk group as specified in columns 8 and 7 respectively. Thus, the percentages in these columns are used in deriving the cumulative percentage figures in columns 9 and 10 AFTER sorting the rows with respect to the annual calls rate. The Lorenz curve is drawn by plotting the cumulative percentage calls against the corresponding cumulative percentage of the at risk group - residential properties in this case (Figure 3). The figure indicates that an even distribution of calls between Target Markets would result in a diagonal line plot, i.e. the share of calls in any Target Market would equal the residential property share. However, after ranking, the first few Target Markets (e.g. 11%) account for a larger proportion of the calls (18%). Indeed, the curve is at first relatively steep, before tapering off and returning to a shape that closely approaches the diagonal line. The area between the curve and the diagonal provides a measure of the degree of discrimination achieved by the typology in isolating high rates (and thus the number of calls) in a relatively small number of clusters or area types. The area in the burglary dwelling case is 15.4 percent which suggests a modest level of performance or effectiveness. However, a glance at Figure 5 indicates that, in the case of sexual offences, a much higher level of discrimination was achieved with a measure of effectiveness estimated at 40.1 percent. This is consistent with the visual impression gained by comparing Figures 3 and 5 and illustrates how the effectiveness measure can serve as a means of quantifying the discriminatory power of a classification in the analysis of calls or actual incidents relating to different types of crime. For example, the first Target Market (E10) accounts for only 0.6% of the mean total persons present but as many as 12.4% of calls to the police. Similarly, the first six Target Markets account for only 4.2% of the population and over 29% of calls. It is evident that this is an unusually high level of discrimination. Higher Education Participation In the last ten years or so, the British higher education system has undergone a major transformation in terms of the number of students accommodated. In the past, the university system has traditionally catered for only a small minority of young people. Indeed, as recently 27

10 as 1989, only 16 percent of school leavers went on to university (Robertson and Hillman, 1997). However, a change in government policy in the late 1980s led to a dramatic expansion in university place provision, with the result that, by 1993, the national participation rate had risen to more than 30 percent. Higher education expansion has been firmly back on the political agenda, as is evident from the terms of reference of the Dearing Committee the National Committee of Inquiry into Higher Education, which refer to the fact that there should be maximum participation in initial higher education.... The RRL was commissioned by the Higher Education Funding Council for England (HEFCE) to undertake a study of the variation in participation rates by gender both by region and by area type using Super Profile Target Markets to distinguish different types of area. The results obtained were quite dramatic in terms of the contrasts in participation rates that they revealed - and provided the basis of evidence that was submitted to the Dearing Committee on behalf of HEFCE. Here we present an outline of the approach adopted in a study described as the first extensive analysis of student participation rates using a geodemographic classifier (see Batey, et al, 1999, p.280). It is based upon the analysis of postcoded records, supplied by the Higher Education Statistics Agency (HESA), of the home addresses of all 160,000 students who entered higher education in England in 1994/95. Each record was assigned a Super Profile code (using the RHASP software noted above) and then aggregated to Target Market and Lifestyle and by the geographical regions defined by the then government for its Regional Offices. An eligible 18 and 19 year-old population was then derived for these corresponding units of analysis, i.e. Target Markets (and Lifestyles) by region, using data from the 1991 Census by ageing the 15 and 16 year-old cohorts, so that appropriate rates of participation could be estimated. The results revealed wide variation in the Young Entrant Index (YEI, based on full-time entrants aged under 21 years). The three least affluent Lifestyles (H, I, and J) account for 30 percent of the eligible population in England and have YEI of 14 percent, less than half the English mean. In contrast, the two most affluent Lifestyles (A and B) account for 23 percent of the eligible population and have a YEI of 51 percent, two thirds higher than the English mean and over three times higher than the above grouping of Lifestyles. It is apparent that, at the individual Lifestyle level, still wider variation was revealed. Finally, Figure 6 illustrates how a regional effect could be identified which has an influence on the YEI values over and above that attributable to the Lifestyle area type breakdown. This is most evident with respect to the higher than average Lifestyle YEI rates recorded in the London and South East. More recent work undertaken on behalf of HEFCE has focused upon monitoring changes in participation rates and the derivation of appropriate denominator estimates using annual mid-year estimates of local authority units. Figure 6: HEFCE/URPERRL: Participation in Higher Education by Region, Gender and Social Class : HE Participation Rates by Super Profile Lifestyle by Region: MALES 28

11 CONCLUDING COMMENTS This paper has reviewed a number of applications of the Super Profiles geodemographic typology that have been developed, primarily, in projects undertaken by URPERRL staff. The emphasis in the examples chosen has been upon public sector and/or research oriented applications, rather than more conventional forms of private sector application. There is still enormous scope for extending the range of issues to which a geodemographic approach is likely to bring fresh insight. It has been demonstrated that a small area typology enables further value to be added to the knowledge gained by relating the study of aspects of our behaviour or patterns of events to information derived from the Census. It is often the combination of sector-specific information and the underlying structure represented by the typology that proves most effective in isolating key features of the behaviour, or event patterns, of interest that might otherwise go undetected. It is also apparent that, in the private sector, there is much scope to improve the performance of customer targeting and similar methods by continuing to build on the base provided by a census-based geodemographic typology. Much progress has been made in the development of individual-record based lifestyle databases (see, for example, Morris, 1993, Sleight, 1993, Birkin, 1995) and, for many purposes, it may be difficult to improve upon the very precise identification of prospective customers for a given product that this type of source provides. However, we do not think that we are dealing here with approaches to the identification of potentially useful patterns in behaviour that is mutually exclusive. Indeed, we are convinced that still greater benefits can be derived by employing methods of analysis and dataset exploration which enable the traditional geodemographic and lifestyle approaches to be employed in a complementary manner (see, for example, Birkin, 1995). We now eagerly await future opportunities to demonstrate how this potential can be realised initially using the 1991 version of Super Profiles as a sound foundation upon which to build. However, we also face the further tantalising prospect of finding a still wider range of opportunities to put this approach into practice by the time the 2001 Census-based version of Super Profiles emerges. References Batey, P.W.J. and Brown, P.J.B. (1994): Design and construction of geodemographic targeting systems: what have we learnt in the last ten years? Journal of Targeting, Measurement and Analysis for Marketing 3(2),

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