Title: The United Kingdom National Area Classification of Output Areas
1 The United Kingdom National Area Classification
of Output Areas
- Daniel Vickers
- School of Geography, University of Leeds
2What Is An Area Classification?
- A segmentation system which groups similar
neighbourhoods into categories, based on the
characteristics of their residents.
3What 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
4What Goes In?
- 41 Census Variables covering
- Age
- Ethnicity
- Health
- Housing Tenure
- Household Composition
- Employment and Education
5Standardising The Data
Why?
- Reduces the effect of extreme values (outliers)
6Standardising The Data
- Range standardisation between 0 -1
- Why?
- 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 to therefore
make each variable equally represented in the
distance measure by standardising the data.
7What Technique Was Used?
- 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
8Issues 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. - Analysis of average distance from cluster
distance 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. - 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.
9Issues of Cluster Number Selection
- 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. - (Martin Callingham Birkbeck College, 2003,
Personal Correspondence)
10Cluster Selection
- A three tier hierarchy 7, 21 52 clusters
11Cluster 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
12What Does The Classification Look Like?
2
2
2
3
3
2
3
3
3
3
2
2
2
2
2
3
3
3
2
3
2
52
7
21
13What To Call The Clusters?
The naming of the clusters is a near impossible
task and on that always provokes much debate
however it is a very important one, as if it is
done wrong it can a false impression of the
people within a cluster. The naming must follow
two general principals 1. Mustn't offend
residents 2. Mustn't contradict other
classifications or use already established names.
14How Does It Discriminate?
15How Does It Discriminate?
16How Does It Discriminate?
17How Does It Discriminate?
18How Does It Discriminate?
19Focus On Leeds
20A Look Around The Country
- London
- Edinburgh
- Cardiff
- Birmingham
- Manchester
- Liverpool
- Newcastle
- Bristol
- Bradford
- Norwich
- Nottingham
- Southampton
- Glasgow
- Dundee
21 Questions and Comments?
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