Output Area Classification in Hampshire Introduction and Profile

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1 Output Area Classification in Hampshire Introduction and Profile The Output Area classification (OAC) distills key results from the 2001 Census for the whole of the UK at a fine grain to indicate the character of local areas. It was created in a collaboration between the Office for National Statistics (ONS) and the University of Leeds using the same well established methods as the related classifications of local authorities and wards. Like those it is freely available from ONS and other sources for all to use, and complements commercially available classifications. Among the wide ranging applications for OAC are the profiling of populations, structuring other data, and the targeting of resources. (Alexander Singleton, OAC User Group). Content Page Part 1: Overview of OAC...2 Part 2: Supergroup Description and Hampshire Profile 4 Part 3: OAC Online Tool Guide.14 Part 4: OAC and British Population Survey...15 Part 5: OAC and Household Spending...19 Part 6: OAC and Wealth and Assets Survey...23 Appendix.32 1

2 Part 1: Overview of OAC The Output Area Classification (OAC) was designed to describe neighbourhoods by providing summary characteristics about those people living within those neighbourhoods. OAC consists of a number of hierarchical typologies from broad to more detailed descriptions, from the top level seven Supergroups, down to 21 Groups and lastly 52 Supergroups (Table 1). The OAC spatial unit is the Output Area (OA) developed for the 2001 Census as an method to derive populations quite tightly distributed around a population of 125 households. In that respect the OAC covers a larger area than many of the postcode based commercial models, such as Mosaic or Acorn. Nonetheless, the OA is good enough to provide a broad description of an area. For a more detailed account of OAC, Dan Vickers and Phil Rees 1 provide an in-depth description of how the UK National Statistics Output Area Classification was built. However, in essence an area classification looks at spatial patterns resulting from a cluster analysis. This is a statistical methodology that brings together a range of variables e.g. age, social class, employment status, ethnicity etc. to determine measures of association between them. The cluster analysis looks for similarities and dissimilarities between the variables to derive a number of cluster groups; in effect a form of data reduction. The choice and number of variables used are important factors any cluster process and whether the model is for a specific or general purpose. The OAC is a general purpose model and as such is based on a wide range of variables. The final model was based on 41 variables from the 2001 Census, under five broad headings: Demographic (age, ethnicity, population density) Household composition (marital status, children etc) Housing (tenure and type) Socio-economic (HE qualifications, car ownership, health etc) Employment (unemployment, full-time/part-time, industry) A full list of variables used in the cluster model are listed in the Appendix. There are more census variables that might have been used, but highly correlated variables are excluded as they either cancel each other out or duplicate the same effects. 1 Vickers, D and Rees, P. (2007) Creating the UK National Statistics 2001 output area classification, Journal of Statistical Society A, 170, Part 2, pp , Royal Statistical Society 2

3 Table 1: OAC Typology Supergroup Group Subgroup 1 Blue collar communities 2. City living 3. Countryside 4. Prospering suburbs 5. Constrained by circumstances 6. Typical traits 7. Multicultural 1a Terraced blue collar 1b. Younger blue collar 1c. Older blue collar 2a. Transient communities 2b. Settle in the city 3a. Village life 3b. Agricultural 3c. Accessible countryside 4a. Prospering younger families 4b. Prospering older families 4c. Prosperous semis 4d. Thriving suburbs 5a. Senior communities 5b. Older workers 5c. Public housing 6a. Settled households 6b. Least divergent 6c. Young families in terraced housing 6d. Aspiring households 7a. Asian communities 7b. Afro-Caribbean communities 1a1 1a2 1a3 1b1 1b2 1c1 1c2 1c3 2a1 2a2 2b1 2b2 3a1 3a2 3b1 3b2 3c1 3c2 4a1 4a2 4b1 4b2 4b3 4b4 4c1 4c2 4c3 4d1 4d2 5a1 5a2 5b1 5b2 5b3 5c1 5c2 5c3 6a1 6a2 6b1 6b2 6b3 6c1 6c2 6d1 6d2 7a1 7a2 7a3 7b1 7b2 3

4 Part 2: Supergroup Description and Hampshire Profile OAC Supergroup Variables with proportions above the national average Variables with proportions below the national average 1. Blue collar communities 2. City living 3. Countryside 4. Prospering suburbs 5. Constrained by circumstances 6. Typical traits 7. Multicultural Age 5-14 Lone parent households Households with non-dependent children Terraced Housing Routine/Semi-routine Employment Mining/Quarrying/Construction employment Manufacturing Employment Indian, Pakistani and Bangladeshi Black Born Outside the UK Rent (Private) Flats HE Qualification Financial Intermediation Employment Retail Trade Employment Lone parent households Age Ages 0-4, 5-14, and 65+ Born Outside UK Single Parent Household Population Density Households with non-dependent children Single person household Rooms per household Rent (Private) Provide unpaid care Flats Economically inactive looking after family No Central Heating General employment HE Qualification Students Financial Intermediation Employment Ages and 65+ Indian, Pakistani and Bangladeshi Detached Housing Black Rooms per Household Population Density 2+ Car Households Single Person Household Work from Home Flats Provide Unpaid Care People Per Room Agricultural Employment Public Transport to Work Unemployment Age Indian, Pakistani and Bangladeshi Two adults no children Black Households with non-dependent children Divorced/Separated Detached housing Single Person Household Single Pensioner Households Rooms per household Renting Public and Private 2+ Car households Terraced Housing Provide unpaid care Flats No Central Heating LLTI Unemployment Age 65+ Two adults no children Divorced/separated Rent (Private) Single Pensioner Households Detached Housing Lone Parent Households Rooms per household Rent (Public) HE Qualifications Flats 2+ Car Households People per Room Work From Home Routine/Semi-routine employment LLTI Unemployment Work Part Time Age 65+ Terraced Housing Rent (Public) Ages 0-4 and 5-15 Ages and 65+ Indian, Pakistani and Bangladeshi Single Pensioner Households Black Two adults No Children Born Outside UK Economically inactive/ looking after family/home Population Density No Central Heating Source: Family Spending: 2010 edition. Adapted from Vickers, D., Rees,P. & Birkin,M (2005) Creating the national classification of Census output areas: data, methods and results 4

5 1. Blue Collar Communities Housing in these areas is more likely to be terraced housing rather than flats and residents mainly rent from the public sector (social housing). There is a high proportion of young people, especially children. This group tends to be lower qualified with fewer higher educational qualifications than the national average. A higher proportion work in manufacturing, retail or construction. Around one in four Output Areas in Havant are Blue Collar Communities. Basingstoke and Deane and Gosport both have above the UK average for this Supergroup reflecting manufacturing and semi-routine occupations in the districts. Table 2: Proportion of Blue Collar Communities Output Areas 1 - Blue Collar Communities United Kingdom Hampshire Economic Area Hampshire County Council area East Hampshire Basingstoke and Deane Eastleigh Fareham Gosport Hart Havant New Forest Rushmoor Test Valley Winchester Portsmouth Southampton Table 3: Census 2001 Population Proportion by Blue Collar Communities Hampshire Economic Area 1 - Blue Collar Communities Basingstoke and Deane Hampshire County Council area East Hampshire Eastleigh Fareham Gosport Hart Havant New Forest Rushmoor Test Valley Winchester Portsmouth Southampton 5

6 2. City Living These people are urban residents who are more likely to be single and living alone. They are more likely to be students, recent graduates or young professionals aged 24-44). They are more likely to hold higher educational qualifications, and are often students and or first generation immigrants the UK and students. Housing is often made up of flats and residents typically rent from the private sector. Naturally the two city authorities have the greatest concentration of City Living Supergroup residents. Across the rest of Hampshire these people will be located in town centre locations. Table 4: Proportion of City Living Output Areas 2 - City Living United Kingdom Hampshire Economic Area Basingstoke and Deane Hampshire County Council area East Hampshire Eastleigh Fareham Gosport Hart Havant New Forest Rushmoor Test Valley Winchester Portsmouth Southampton Table 5: Census 2001 Population Proportion by City Living 2 - City Living Hampshire Economic Area Basingstoke and Deane Hampshire County Council area East Hampshire 2.0 Eastleigh Fareham Gosport Hart Havant New Forest Rushmoor Test Valley Winchester Portsmouth Southampton 6

7 3. Countryside Rural and semi rural residents, many working from home. Employment in agricultural and fishing (coastal areas) is higher than the national average. Residents often live in detached houses, and in households with more than one car. Reflecting the rural landscape that defines much of Hampshire, it is unsurprising that seven out of 13 local authorities have above the national average for Countryside living. Almost one in three Winchester OA s fall in this Supergroup. Table 6: Proportion of Countryside Output Areas 3 - Countryside United Kingdom Hampshire Economic Area Basingstoke and Deane Hampshire County Council area East Hampshire Eastleigh 5.0 Fareham Gosport Hart Havant New Forest Rushmoor Test Valley Winchester Portsmouth Southampton Table 7: Census 2001 Population Proportion by Countryside Hampshire Economic Area 3 - Countryside Basingstoke and Deane Hampshire County Council area East Hampshire Eastleigh Fareham Gosport Hart Havant New Forest Rushmoor Test Valley Winchester Portsmouth Southampton 7

8 4. Prospering semis These are prosperous people who are have established themselves in the workplace. They often live in detached houses, most privately owned, with either older children who have left home. These households have access to more than one cars. Hampshire is generally recognised as a prosperous area, and this is borne out by ten out of 13 local authorities scoring above the national average for this Supergroup. Fareham, Hart and Eastleigh districts have particularly high proportions of Prospering semis, reflecting for example, well-to-do suburbs in Fareham/Locks Heath, Chandler s Ford and Fleet. The two cities and Gosport have a much lower representation in this Supergroup. Table 8: Proportion of Prospering semis Output Areas 4 - Prospering suburbs United Kingdom Hampshire Economic Area Hampshire County Council area Basingstoke and Deane East Hampshire Eastleigh Fareham Gosport Hart Havant New Forest Rushmoor Test Valley Winchester Portsmouth Southampton Table 9: Census 2001 Population Proportion by Prospering semis 4 - Prospering suburbs Hampshire Economic Area Hampshire County Council area Basingstoke and Deane East Hampshire Eastleigh Fareham Gosport Hart Havant New Forest Rushmoor Test Valley Winchester Portsmouth Southampton 8

9 5. Constrained by circumstances Residents in this group are more likely to be marginalised or on welfare benefits. These people typically live in social housing, mostly flats. They likely to few qualifications. Many will be older workers or pensioners or those on benefits. While Hampshire is a prosperous there are areas more constrained by circumstances. Five out of 13 local authorities have above the national average for this Supergroup, notably in the two cities, Gosport and Havant, and to a lesser extent in Rushmoor. However, both Havant and Rushmoor contain sizeable proportions of prosperous and constrained areas, often in close proximity to neighbourhoods with socio-economic problems. Table 10: Proportion of Constrained by circumstances Output Areas 5 - Constrained by circumstances United Kingdom Hampshire Economic Area Hampshire County Council area Basingstoke and Deane East Hampshire Eastleigh Fareham Gosport Hart Havant New Forest Rushmoor Test Valley Winchester Portsmouth Southampton Table 11: Census 2001 Population Proportion by Constrained by circumstances 5 - Constrained by circumstances Hampshire Economic Area Hampshire County Council area Basingstoke and Deane East Hampshire Eastleigh Fareham Gosport Hart Havant New Forest Rushmoor Test Valley Winchester Portsmouth Southampton 9

10 6. Typical traits This is your average person, likely to own a home and come from a mix of households. They are more likely to be younger families and aspiring households, younger less established versions of Prospering semis. They don t work in any particular industry. Portsmouth and Gosport have notably higher proportions of OA s with Typical traits, whereas other local authority profiles are broadly similar. To understand why would probably require drilling down to the Group or Subgroup, although higher proportions of terraced housing and young families are likely to play a significant part. Table 12: Proportion of Typical traits Output Areas 6 - Typical traits United Kingdom Hampshire Economic Area Hampshire County Council area Basingstoke and Deane East Hampshire Eastleigh Fareham Gosport Hart Havant New Forest Rushmoor Test Valley Winchester Portsmouth Southampton Table 13: Census 2001 Population Proportion by Typical traits 6 - Typical traits Hampshire Economic Area Hampshire County Council area Basingstoke and Deane East Hampshire Eastleigh Fareham Gosport Hart Havant New Forest Rushmoor Test Valley Winchester Portsmouth Southampton 10

11 7. Multicultural Residents are often non-white, mainly Asian and Black ethnic groups. Many are first time immigrants. Housing is a mix of social housing and private landlords, often in flats. This group is more reliant on public transport. Unsurprisingly, the two cities have the largest Multicultural representation, but this is well below the UK average. Eight local authorities have no Multicultural OA s, which only means there are no significant clusters rather than no multicultural areas. The OAC model would not have picked up on any significant immigration from the EU Accession states post Table 14: Proportion of Multicultural Output Areas 7 - Multicultural United Kingdom Hampshire Economic Area Hampshire County Council area Basingstoke and Deane East Hampshire Eas tleigh Fareham Gosport Hart Havant New Forest Rushmoor Test Valley Winchester Portsmouth Southampton Table 15: Census 2001 Population Proportion by Multicultural 7 - Multicultural Hampshire Economic Area Hampshire County Council area Basingstoke and Deane East Hampshire Eastleigh Fareham Gosport Hart Havant New Forest Rushmoor Test Valley Winchester Portsmouth Southampton 11

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14 Part 3 OAC Online Tool Guide To understand OAC and make full use of the classification tool there are additional free software for profiling and studies. These can be accessed from the OAC User Group webpage all software needs to be downloaded from this site. Alternatively, many of the documents and reports can be found on the shared I: Drive by navigating to I: Drive/shared/OAC Postcode Profiler (from OAC User Group website) The OAC classification has previously been disseminated by the ONS at the Output Area Level. For a lot of users this has been problematic given that address records typically consist of lists of unit postcodes. A recent development at the ONS has created an open licence version of the National Statistics Postcode Directory (NSPD), which usefully includes an OAC Code for each unit postcode in the UK. Researchers at University College London and University of Liverpool have created a free tool which uses this file and can read a CSV list of postcodes, and then append the corresponding OAC code for each of these addresses. Please note you will need access to a hard-drive to download this software. Grand Index British Population Survey (From OAC User Group website) The Grand Index (Excel file) is a simple spreadsheet of index scores for a series of variables cross tabulated with OAC Sub Groups, Groups and Super Groups. These index scores can be used to target specific OAC clusters in your profiling work. For example, you could ask which neighbourhoods in your local area are likely to contain users of social networking websites? HantsMap There are two layers (Shape) files currently for OAC. These have been built locally by combining ONS data with other surveys, notably the Wealth and Assets Survey and Family Spending Survey to provide additional variables. These are discussed in the next section. For further information on any of the above please contact Gareth Henry:

15 Part 4: OAC and British Population Survey The British Population Survey (BPS) was launched in its current format in 2010, with geodemographic data added in June The BPS is a quota sample of 6-8,000 respondents per month (approx 80,000 per year), who are interviewed face-to-face to provide data about: family circumstances, household composition, economic situation, education and employment, access and use of the internet, ownership of consumer durables, newspaper readership The data is updated every month by personal face to face interviews, (over 80,000 individuals per year) and each wave is fully representative of the adult population for Great Britain and at the regional level. Older Data can be downloaded for free, or it is possible to subscribe to latest datasets released quarterly. Click here for further information So how is the BPS useful to OAC and local use? The BPS in collaboration with the OAC User Group have created a Grand Index, which takes the national survey data and links this to the geodemographic data. Using a segmentation index, the OAC Supergroup, Group and Subgroup mean averages against each variable can be compared to the national global average to derive further knowledge of likely behaviour or traits. Data Explorer This is an interactive database of national and regional data. You will need access to a harddrive to download the software required to run the free data explorer datasets, or more recent ones if you choose to subscribe. Grand Index The Grand Index is a simple spreadsheet of index scores for a series of variables cross tabulated by OAC Supergroups, Groups and Subgroups. These index scores can be used to target specific OAC clusters in your profiling work. The Grand Index also includes DVLA data on vehicles e.g. type of car etc, and when combined with the BPS the Grand Index has several hundred variables. An initial in-house study by HCC of BPS and Household Income gives just one example of how BPS might be used. Naturally, the data is indicative of the Group behaviour, not actual individuals. To provide a greater range of income the example used the Group classification rather than the Supergroup.. 15

16 Table 16: Grand Index Household Income Index Scores Select Variable: HOUSEHOLD INCOME :UNDER 4,499 1a: Terraced Blue Collar 1b: Younger Blue Collar 1c: Older Blue Collar Upper Index Lower Select Variable: HOUSEHOLD INCOME :75,000-99,999 1a: Terraced Blue Collar 1b: Younger Blue Collar 1c: Older Blue Collar Upper Index Lower Source: Grand Index (OAC) The variables used in the example are Household Income Under 4,499 and Household Income 75,000 to 99,999, although there are other income groups in between that would need to be looked at to get the Income band most closely associated with each Group. This data are presented as an Index value where 100 = the national (GB) average. This is derived by dividing the mean cluster value for a Group and the national mean value, and multiplying by 100. Scores over 100 suggest above average likelihood the Group exhibit that trait, and vice versa if the value were lower than 100. So a score of 120 more or less equates to that Group having a 20% higher likelihood of that behaviour than the national norm. Index scores of 200+ suggest very high probabilities, in effect twice as likely. Low scores of around 50 or less suggest that particular behaviour is unlikely to be exhibited by that Group. The Upper and Lower values are statistical measures of reliability known as confidence intervals, and this is included to help the user decide have much faith to put in the estimate or Index value. There is a lower and upper range, and the larger that range the less reliable the data. In the example the Index value of Terraced Blue Collar for Household Income Under 4,499 could be as high as 120 (Upper) or as low as 103 (Lower). As the Lower Index value is still above 100 this Group is more likely than the average to be in this income band, but it is not a strong association given the possible proximity to 100. However, there is a much stronger likelihood from the other variable that Terraced Blue Collar types will not have a Household Income between 75,000 and 99,999 given the scores are well below 100. To determine the most likely income group will require looking at all the income bands to find the highest index score. The Household Income category consists of several variables and will take longer to pull out the information needed, whereas categories will have fewer variables. It is also worth looking at how the variables lend themselves to Index scores to derive character traits and some common sense needs to be applied. Taking the Income example further, you might want to be less specific given the data is based on a national survey. In this case rather than have all the income bands you might want to split income bands into five indicative groups: 16

17 (5) Index scores above 130 = well off (in effect a 30% high than national concentration) (4) Index scores = Good income (3) Index scores = Average income (there is a confidence interval around the Index score so 10 points each side allows for this) (2) Index scores = Low income (1) Index scores less than 70 = Very low income To map this you would need to attribute the most probable income band to each Group as in Table 17. Table 17: OAC Groups and most likely Household Income Bands 1a: Terraced Blue Collar 13,500-17,500 1b: Younger Blue Collar 6,500-13,500 1c: Older Blue Collar 9,500-11,500 2a: Transient Communities 75,000+ under 4,499 extremes 2b: Settled in the City under 4,499 extremes 3a: Village Life 75,000+ 3b: Agricultural 15,500-25,000 3c: Accessible Countryside 40,000+ 4a: Prospering Younger Families 40,000+ 4b: Prospering Older Families 75,000+ 4c: Prospering Semis 30,000-49,999 4d: Thriving Suburbs a: Senior Communities Under 7,500 11,500-17,499 Mostly lower 5b: Older Workers Under 11,500 5c: Public Housing Under 11,500 6a: Settled Households 40,000-74,999 6b: Least Divergent 13,500-15,499 6c: Young Families in Terraced Homes 4,500-13,499 6d: Aspiring Households 50,000+ 7a: Asian Communities Under 6,499 7b: Afro-Caribbean Communities Under 6,499 Some Groups had extremes values at each end of the income spectrum, such as Transient Communities or Settled in the City. However, only one income code can be applied to an Output Area, so in this case the data needed to be compared to the 2004 Output Area deprivation data and some local knowledge, to determine if the area was more likely to be high or low income households. The important point to remember is that these are the most likely income bands, not actual income, and that not everyone in the Output Area will necessarily be in that income band. So if you re targeting the well off, the OAC will indicate the most likely locations where these people will live, but you will naturally miss others you wanted to target, and likewise capture people you weren t interested in. OAC should be used in this context as an indicative tool that can focus your area of interest. The best way to interpret OAC is spatially on a map. The example overleaf takes the household income data 17

18 to show Household Income distributions. This data could be linked to HCC admin or other data to target resources. Since the example below was produced, a more reliable survey on wealth was produced using the Wealth and Assets Survey. HCC does have access to Mosaic, so in reality that geodemographic tool is likely to be used most often, especially in Customer Insight. However, OAC does not have any licence restrictions regarding access or sharing with non-hcc partners or the public. 18

19 Part 5: OAC and Household Spending The OAC data comes from a report on the 2009 Living Costs and Food Survey, published by the Office for National Statistics in 2010 a copy can be found on the I: Drive/shared/OAC. The part specific to OAC is in Chapter 5 (pp ). The aim of the article was to provide an overview that highlights key findings from the 2009 Living Costs and Food Survey (LCF) by OAC but not a comprehensive analysis of income and expenditure. The key findings from the report found: Supergroups with the highest expenditure are Supergroups 2 (city living) and 4 (prospering suburbs), spending an average of and per week, respectively, followed by Supergroup 3 (countryside) with expenditure of Supergroup 5 (constrained by circumstances) showed the lowest expenditure at , followed by Supergroup 1 (blue collar communities). The pattern of total expenditures for 2009 is similar to Since 2007 Supergroup 2 (city living) total average weekly household expenditure has gradually risen to exhibit the largest expenditure of all OAC Supergroups. Supergroup 5 (constrained by circumstances) remaining consistently the lowest. The differences in total expenditure between Supergroups can be considered in more detail by COICOP categories (Classification of Individual Consumption by Purpose): High expenditure by Supergroup 2, city living, was partially attributable to expenditure on housing, fuel and power ( 95.60), which was higher than for any other group. Expenditure on housing fuel and power showed high variability between Supergroups; after city living the next highest expenditure in this category was by Supergroup 7 multicultural ( 77.00). All other Supergroups spent considerably less, with Supergroup 1 (blue collar communities) spending the least at per week. Transport expenditure was highest for Supergroup 4 prospering suburbs ( 77.90). and Supergroup 3 countryside ( 72.20), and lowest for Supergroup 5 constrained by circumstances ( 32.70). Spending on restaurants and hotels was almost three times as much in the highestspending category, city living ( 62.20) than the lowest, constrained by circumstances ( 22.80). Average weekly household expenditure for alcoholic drinks, tobacco and narcotics, health and communication showed minimal differences between OAC Supergroups. 19

20 Table:18 Average weekly household expenditure by OAC Supergroup, 2009 based on weighted data and including children's expenditure Blue collar Communities City living Countryside Prospering suburbs Contstrained by circumstances Super- Super- Super- Super- Super- Super- Super- All group group group group group group group house holds Typical traits Multicultural Weighted number of households (thousands) 4,330 1,600 3,330 5,720 2,980 5,240 2,790 25,980 Total number of households in sample , , ,830 Total number of persons in sample 2, ,060 3,350 1,340 2,620 1,240 13,740 Total number of adults in sample 1, ,670 2,660 1,060 2, ,650 Weighted average number of persons per household Commodity or service Average weekly household expenditure ( ) 1 Food & non-alcoholic drinks Alcoholic drinks, tobacco & narcotics Clothing & footwear Housing (net) 1, fuel & power Household goods & services Health Transport Communication Recreation & culture Education Restaurants & hotels Miscellaneous goods & services All expenditure groups Excluding mortgage interest payments, council tax and Northern Ireland rates Source: Family Spending: 2010 edition (Table 5.4, p109) ONS, Family Spending 2009, Crown copyright

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