1. Creating a geodemographic system and more: OAC and the way forward
Centre for Advanced Spatial Analysis, UCL, 10th December 2008
Creating a geodemographic system and more: OAC and the way forward
Dr Dan Vickers
Social and Spatial Inequalities Group,
Department of Geography,
University of Sheffield

2. What are you in for? Creating the Output Area Classification (OAC)
Geodemographics over time
Measuring Segregation with geodemographics
15/04/2017
© The University of Sheffield

3. Why classify areas?
“All the real knowledge which we possess depends on methods by which we distinguish the similar from the dissimilar. The greater number of natural distinctions this method comprehends the clearer becomes our idea of things. The more numerous the objects which employ our attention the more difficult it becomes to form such a method and the more necessary.” (Linnæus 1737)
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5. The myths of geodemographics
The more data the better?
The smaller the areas the more meaningful?
These are distinct groups?
A true depiction of reality?

6. The Objective in Creating OAC
To provide a publicly accessible classification, free at the point of use for researchers, policy makers and the public, avoiding the expense of commercial classifications.
To provide a transparent and replicable methodology with the input data as well as the classification output.
To stimulate value added research either using the classification or the raw data.
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7. The seven steps of cluster analysis
Clustering elements (objects to cluster, also known as “operational taxonomic units”)
Clustering variables (attributes of objects to be used)
Variable standardisation
Measure of association (proximity measure)
Clustering method
Number of clusters
Interpretation, testing and replication
adapted from Milligan, G. W. (1996) Clustering validation: Results and implications for applied analyses. in Arabie, P., Hubert, L. J. and De Soete, G. Eds., Clustering and Classification. Singapore: World Scientific.

8. What went in
Output Areas are the smallest area for general census output.
223,060 in the UK
England & Wales
Number of OAs: 174,434, Min size: 40 households, 100 people, Mean size: 124 households, 297 people
Scotland
Number of OAs: 42,604, Min size: 20 households, 50 people, Mean size: 52 households, 119 people
Northern Ireland
Number of OAs: 5,022, Min size: 40 households, 100 people, Mean size: 125 households, 336 people

9. What went 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.

10. Standardising the variables
Log Transformation
Reduce the effect of extreme values
Range Standardisation (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 necessary therefore to make each variable equally represented in the distance measure by standardising the data.

11. Clustering the data K-means clustering
K-means is an iterative relocation algorithm based on an error sum of squares measure. The basic operation of the algorithm is to move a case from one cluster to another to see if the move would improve the sum of squared deviations within each cluster (Aldenderfer and Blashfield, 1984).
The case will then be assigned/re-allocated to the cluster to which it brings the greatest improvement. The next iteration occurs when all the cases have been processed. A stable classification is therefore reached when no moves occur during a complete iteration of the data. After clustering is complete, it is then possible to examine the means of each cluster for each dimension (variable) in order to assess the distinctiveness of the clusters (Everitt et al., 2001).

12. Issues of cluster number selection
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.
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.

13. Issues of cluster number 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

14. Creating a hierarchy

15. 1: BLUE COLLAR COMMUNITIES
1a: Terraced Blue Collar
1b: Younger Blue Collar
1c: Older Blue Collar
2: CITY LIVING
2a: Transient Communities
2b: Settled in the City
3: COUNTRYSIDE
3a: Village Life
3b: Agricultural
3c: Accessible Countryside
4: PROSPERING SUBURBS
4a: Prospering Younger Families
4b: Prospering Older Families
4c: Prospering Semis
4d: Thriving Suburbs
5: CONSTRAINED BY CIRCUMSTANCES
5a: Senior Communities
5b: Older Workers
5c: Public Housing
6: TYPICAL TRAITS
6a: Settled Households
6b: Least Divergent
6c: Young Families in Terraced Homes
6d: Aspiring Households
7: MULTICULTURAL
7a: Asian Communities
7b: Afro-Caribbean Communities

16. 1: Blue Collar Communities

17. 2: City Living

18. 3: Countryside

19. 4: Prospering Suburbs

20. 5: Constrained by Circumstance

21. 6: Typical Traits

22. 7: Multicultural

23. Alternative Profiles

24. Mapping the Classification

25. Geodemographics over time

26. Variables V01: Age 0-4 V02: Age 5-14 V03: Age 25-44 V04: Age 45-64
V06: Indian, Pakistani & Bangladeshi
V07: Black African, Black Caribbean & Black Other
V08: Born Outside the UK
V09: Unemployed
V10: Working part-time
V13: Economically inactive looking after family
V14: No central heating
V16: Rent (private)
V17: Rent (public)
V18: 2+ Car Households
V20: Flats
V21: Detached
V22: Terraced
V23: Lone parent household
V24: Single pensioner household
V25: Single person (not pensioner) household
V26: Population Density
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27. Methodology England only
1991 data assigned to 2001 Output areas Simpson (2002), Norman et al. (2003)
Clustered based on the 2001 data
1991 data assigned to centroids created from 2001 data
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© The University of Sheffield

28. The clusters were named as follows:
Variable
Cluster 1 2 3 4 5 6 7
Age 0-4
.17
.24
.18
.31
.19
.26
Age 5-14
.13
.23
.29
.22
.20
Age 25-44
.47
.41
.30
.38
.34
.36
.33
Age 45-64
.50
.28
.43
Age 65+
.15
.10
.21
Indian, Pakistani & Bangladeshi
.05
.03
.01
.44
.02
.06
Black African/ Caribbean / Other
.07
.00
.14
.16
Born Outside the UK
.08
.39
Unemployed
.12
Working part-time
.32
Looking after family
.25
No central heating
.04
Rent (private)
Rent (public)
.11
.67
.52
2+ Car Households
.57
Flats
.68
.77
Detached
Terraced
.66
Lone parent household
Single pensioner household
Single person (not pensioner) hhold
.09
Population Density
The clusters were named as follows:
1. Urban Melting Pot
2. Mixed Communities
3. Out in the Sticks
4. Asian Influence
5. Middle Class Achievers
6. Down and Out
7. Working Class Endeavour

29. Changing prevalence of types
1991
Frequency
Percent
2001
Change Freq
Change %
1. Urban Melting Pot
11,350
6.9
15,392
9.3
4,042
2.4
2. Mixed Communities
21,949
13.2
25,910
15.6
3,961
3. Out in the Sticks
32,915
19.9
35,704
21.6
2,789
1.7
4. Asian Influence
5,039
3.0
4,930
-109
5. Middle Class Achievers
50,578
30.5
46,791
28.2
-3,787
-2.3
6. Down and Out
8,257
5.0
10,878
6.6
2,621
1.6
7. Working Class Endeavour
35,577
21.5
26,060
15.7
-9,517
-5.8
Total
165,665
100.0
0.0

30. Cluster results 1991 1 2 3 4 5 6 7 Total 2001 1: Urban Melting Pot
9,200
1,318
195
467
2,245
1,116
851
15,392
2: Mixed Communities
779
15,991
237
432
3,445 76
4,950
25,910
3: Out in the Sticks 23
206
26,561
8,444 8
456
35,704
4: Asian Influence
153
519
3,699
117
129
310
4,930
5: Middle Class Achievers
482
2,628
5,658
103
33,044 27
4,849
46,791
6: Down and Out
618
126 29
204
505
6,401
2,995
10,878
7: Working Class Endeavour 95
1,161
232
128
2,778
500
21,166
26,060
11,350
21,949
32,915
5,039
50,578
8,257
35,577
165,665

31. London
Sheffield

32. Tyneside
Surry

33. Measuring Segregation with geodemographics

34. Segregation Index
Calculates the number of output areas that would need to be moved between Local Authority Districts to have an equal distribution of OAC types across the country

35. Segregation index
The index of segregation, as used here, is the index of dissimilarity between a group and the whole of the population. This is the index we use to measure the degree to which a group is spatially polarised. The index of dissimilarity between two groups is half the sum over all areas of the absolute differences between each group divided by its respective total population. It ranges between 0 and near 100%.
where Da is the index between groups a and b (b in our case is the population as a whole), Pi is the population of area i of group a, * represents all areas, and N is the number of areas being considered
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© The University of Sheffield

36. Most Segregated types Blue Collar Communities 26.5% City Living 48.8%
Countryside 53.3%
Prospering Suburbs 21.4%
Constrained by Circumstances 28.8%
Typical Traits 22.7%
Multicultural 67.7%

37. Where is most geodemographicly segregated
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© The University of Sheffield

38. Segregated Places Most Least 1 Newham 89.3 2 City of London 87.0 3
Hackney
82.7 4
Westminster 5
Tower Hamlets
82.6 6
Lambeth
82.5 7
Kensington and Chelsea
82.2 8
Camden
82.0 9
Hammersmith and Fulham 10
Islington
81.7
425
Dacorum
16.3
426
Wellingborough
15.0
427
Welwyn Hatfield
14.8
428
Gravesham
14.5
429
Cardiff
14.3
430
Sheffield
13.8
431
Leeds
11.6
432
Peterborough
11.2
433
North Hertfordshire
10.5
434
Epping Forest
9.5

39. Thank you D.Vickers@sheffield.ac.uk Areaclassification.org.uk
Sheffield.ac.uk/SASI
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