The United Kingdom National Area Classification of Output Areas

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The United Kingdom National Area Classification
of Output Areas

• Daniel Vickers
• with Phil Rees Mark Birkin
• School of Geography, University of Leeds

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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

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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
• EW 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

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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

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Standardising the Data

• Log Transformation

Why?
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.
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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.

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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)

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Cluster Selection

• A three tier hierarchy 7, 21 52 clusters

• 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

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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

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Cluster Creation
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Naming the Clusters
1 City Centre Melting Pot 1a Younger Metropolitan Dwellers
1 City Centre Melting Pot 1b Older Metropolitan Dwellers
2 Typical Traits 2a Transitional Neighbourhoods
2 Typical Traits 2b Settled Families
2 Typical Traits 2c Established Metropolitan Hinterland
2 Typical Traits 2d Young Terraced Families
3 Inner City Multicultural Blend 3a Afro-Caribbean Communities
3 Inner City Multicultural Blend 3b Asian Influence
4 Blue Collar Communities 4a Terraced Routine Workers
4 Blue Collar Communities 4b Older Routine Workers
4 Blue Collar Communities 4c Young Families, Routine Workers
5 Idyllic Countryside 5a Agricultural UK
5 Idyllic Countryside 5b Retired to the Countryside
5 Idyllic Countryside 5c Families in the Countryside
6 Constraints of Circumstance 6a Families of Hardship
6 Constraints of Circumstance 6b Assisted Existence
6 Constraints of Circumstance 6c Older Hard Fortune
7 Comfortable Suburban Estates 7a Opulent Older Families
7 Comfortable Suburban Estates 7b Suburban Transition
7 Comfortable Suburban Estates 7c Suburban Melting Pot
7 Comfortable Suburban Estates 7d Young Suburban Families
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.

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How Well Does It Discriminate?
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How Well Does It Discriminate?
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How Well Does It Discriminate?
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How Well Does It Discriminate?
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Comparing the Core Cities (and Fife)
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Who do Each Type Vote for?
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2001 Election Data courtesy of Ed Fieldhouse,
CCSR, University of Manchester
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Deconstructing Rural England (Devon case study)
Devon Average 31 UK Average 12.5
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Focus On Leeds
Map appears in forthcoming book Twenty-First
Century Leeds Geographies of a Regional City
edited by Rachael Unsworth John Stillwell
City Centre Melting Pot
Typical Traits
Inner City Multicultural Blend
Blue Collar Communities
Idyllic Countryside
Constraints of Circumstance
Comfortable Suburban Estates
Boundaries Community Areas, as defined by Pete
Shepherd, School of Geography, University of
Leeds (built from Output Areas)
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Focus on Fife
City Centre Melting Pot
Typical Traits
Inner City Multicultural Blend
Blue Collar Communities
Idyllic Countryside
Constraints of Circumstance
Comfortable Suburban Estates
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Focus on Fife
31.6
Total number of OAs in Fife 2882
25.8
19.7
8.9
8.6
5.4
0
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Focus on Fife
Total number of OAs in Fife 2882
16.8
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Focus on Fife
Total number of OAs in Fife 2882
11.7
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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
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Where would you like to go?
Belfast Birmingham Bradford Bristol Cardiff Dundee
Edinburgh Glasgow Liverpool London Manchester New
castle Norwich Nottingham Southampton St-Andrews
Thank you for listening Any Questions?
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