The United Kingdom National Area Classification of Output Areas

1. The United Kingdom National Area Classification of Output Areas Daniel Vickers with Phil Rees & Mark Birkin School of Geography, University of Leeds

2. What will I be talking about today? • Introduction to Area Classification and Output Areas • How the Classification system was made including: • What data goes in? • Methods of standardisation • Issues of cluster number selection • Cluster selection • Cluster Creation • Naming the clusters • How well does the classification discriminate: • Census data • Comparing the Core cities • Voting patterns • Deconstructing Rural England • Mapping the Classification • Focus on Leeds • Focus on Fife • A look around the country 2

3. What is an Area Classification? A segmentation system which groups similar neighbourhoods into categories, based on the characteristics of their residents: a simplification of complex datasets. What is an Output Area? • The smallest area for census output • 223, 060 in the UK • E&W 174,434 min size 40 hholds 100 people • Scotland 42,604 min size 20 hholds 50 people • NI 5,022 min size 40 hholds 100 people 3

4. What Goes In? 41 Census Variables covering: • Demographic attributes • Including - age, ethnicity, country of birth and population density • Household composition • Including - living arrangements, family type and family size. • Housing characteristics • Including - tenure , type & size, and quality/overcrowding • Socio-economic traits • Including - education, socio-economic class, car ownership & commuting and health & care. • Employment attributes • Including - level of economic activity and employment class type. How many data inputs are involved? 223,060 Output Areas, 41 Variables = 9,145,460 data points 4

5. Standardising the Data Why? Log Transformation Reduces the effect of extreme values (outliers) Why? Range standardisation between 0 -1 Problems will occur if there are differing scales or magnitudes among the variables. In general, variables with larger values and greater variation will have more impact on the final similarity measure. It is therefore necessary to make each variable equally represented in the distance measure by standardising the data. 5

6. Issues of Cluster Number Selection • When choosing the number of clusters to have in the classification there were three main issues which need to be considered. • Issue 1: Analysis of average distance from cluster centres for each cluster number option. The ideal solution would be the number of clusters which gives smallest average distance from the cluster centre across all clusters. • Issue 2: Analysis of cluster size homogeneity for each cluster number option. It would be useful, where possible, to have clusters of as similar size as possible in terms of the number of members within each. 6

7. Issues of Cluster Number Selection • Issue 3: The number of clusters produced should be as close to the perceived ideal as possible. This means that the number of clusters needs to be of a size that is useful for further analysis. • “At the highest level of aggregation, the cluster groups should be about 6 in number to enable good visualisation and these clusters should also be given descriptive names. • At the next level of aggregation, the number of groups should be about 20. This would be good for conceptual customer profiling. • At the next level of aggregation, the number of groups should be about 50. This can be used for market propensity measures from the larger commercial surveys.” (Personal Communication 2003, from Martin Callingham, Independent Market Research Consultant and Birkbeck College, co-editor of Qualitative Market Research: Principle and Practice, Sage, 2003) 7

8. Cluster Selection • First Level target 6, 7 selected based on analysis of, average distance from cluster centre and size of each cluster. • Second Level target 20, 21 selected based on analysis of, average distance from cluster centre and size of each cluster. • Third Level target 50, 52 selected based on size of each cluster. Split into either 2 or 3 groups A three tier hierarchy 7, 21 & 52 clusters 8

9. Cluster Creation • Modified K-means clustering • First level run as standard k-means • Second level, first level is split into separate files and each file is clustered separately • Third level, second level is split into separate files and each file is clustered separately 9

10. Cluster Creation 10

11. Naming the Clusters The naming of the clusters is a near impossible task and one that always provokes much debate. However, the task is very important, as if it is done wrongly it can create a false impression of the people within a cluster. The naming must follow two general principles: 1. Must not offend residents 2. Must not contradict other classifications or use already established names. 11

12. Inner City Multicultural Blend Comfortable Suburban Estates City Centre Melting Pot Blue Collar Communities Idyllic Countryside Constraints of Circumstance Typical Traits How Well Does It Discriminate? 12

13. Inner City Multicultural Blend Comfortable Suburban Estates City Centre Melting Pot Blue Collar Communities Idyllic Countryside Constraints of Circumstance Typical Traits How Well Does It Discriminate? 13

14. Inner City Multicultural Blend Comfortable Suburban Estates City Centre Melting Pot Blue Collar Communities Idyllic Countryside Constraints of Circumstance Typical Traits How Well Does It Discriminate? 14

15. Inner City Multicultural Blend Comfortable Suburban Estates City Centre Melting Pot Blue Collar Communities Idyllic Countryside Constraints of Circumstance Typical Traits How Well Does It Discriminate? 15

16. Comparing the Core Cities (and Fife) 16

17. Who do Each Type Vote for? 17 2001 Election Data courtesy of Ed Fieldhouse, CCSR, University of Manchester

18. Percentage Super Group 5 Idyllic Countryside 3 – 16 16 – 39 39 – 51 51 – 74 Deconstructing Rural England (Devon case study) Devon Average 31% UK Average 12.5% 18

19. Focus On Leeds Map appears in forthcoming book “Twenty-First Century Leeds: Geographies of a Regional City” edited by Rachael Unsworth & John Stillwell Boundaries: Community Areas, as defined by Pete Shepherd, School of Geography, University of Leeds (built from Output Areas) 19

20. Focus on Fife 20

21. City Centre Melting Pot Typical Traits Blue Collar Communities Idyllic Countryside Constraints of Circumstance Comfortable Suburban Estates Inner City Multicultural Blend Focus on Fife 31.6% Total number of OAs in Fife: 2882 25.8% 19.7% 8.9% 8.6% 5.4% 0% 21

22. Focus on Fife Total number of OAs in Fife: 2882 16.8% 22

23. Focus on Fife Total number of OAs in Fife: 2882 11.7% 23

24. Consultation 55 respondents so far, 29 Academics, 26 Local Government Two most confused types: 4 Blue Collar Communities & 6 Constraints of Circumstance Easiest type to identify: 5 Idyllic Countryside 24

25. Where would you like to go? Belfast Birmingham Bradford Bristol Cardiff Dundee Edinburgh Glasgow Liverpool London Manchester Newcastle Norwich Nottingham Southampton St-Andrews Thank you for listening Any Questions? 25