Transcript Geog3025_Geodemographics

Geodemographic classification
schemes
GEOG3025
Geodemographic classification
schemes
• Lecture overview:
– History
– Data sources
– Classification methods
– Selection and labelling of classes
– Examples
– Applications
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Objectives
• To be familiar with the core techniques
of geodemographic classification
• To recognise the inherent subjectivity
and limitations of the approach
• To understand the application and use
of geodemographic classification
schemes
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Introductory questions…
What is a
geodemographic
classifier?
How does it
capture the
characteristics of a
neighbourhood?
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History
• Originally derived from census small
area statistics (1971)
• Richard Webber: a classification of
residential neighbourhoods (ACORN)
• Data-led area classification scheme
• Labelled neighbourhood ‘types’
• General purpose neighbourhood
classification initially for commercial use
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Motivation for classification
• To find customers by identifying
neighbourhoods with similar population
characteristics
• By inference, finding local populations
with similar consumer behaviour
• More recently, used for differentiating
strategies for existing customers and
branches
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Data sources
• More modern classifiers using
combination of census and non-census
data
• Information from customer databases
and lifestyle/consumption surveys
• Data generally recorded by postcode
• Postcode/census matching issues
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What makes neighbourhoods
different?
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Multiple
dimensions…
Classification methods
• Large pool of input variables
• Geographical linkage to clustering scale?
• Data reduction/classification methodology
intended to capture key dimensions
– Cluster analysis
– Principal components/factor analysis
– Numerous variants and extensions
• Standardization of variables
• Clustering in multidimensional ‘space’
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e.g. 2 standardised variables
HIGH
e.g. Prestigious
large houses in
semi-rural
commuter locations
e.g. run down
larger houses in
unfashionable
inner suburbs
LOW
e.g. income
HIGH
e.g. Inner city high
rise rented flats
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e.g. Apartments in
prestigious
modern urban
redevelopments
LOW
e.g. house size
Data on 2 standardised variables
e.g. income
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e.g. house size
Initial cluster centres
e.g. income
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e.g. house size
Allocation of data to cluster
centres
e.g. income
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e.g. house size
Iteration to achieve final cluster
memberships
e.g. income
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e.g. house size
Design considerations
• How to determine initial cluster centres?
– Subjective
– Data-driven
• How to measure distances in
multidimensional space?
• Treat all variables with equally or assign
weights?
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Clustering by grouping of most
similar observations
4
1
2
e.g. income
3
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e.g. house size
Classification dendrogram
Number of
classes
Individual observations…
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Selection and labelling of clusters
• Subjective decision regarding number of
clusters
• Most classification schemes offering
several different levels of grouping
• Subjective naming of clusters and
groups
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Example ACORN hierarchy…
New (2001)
Acorn
classification:
5 Categories
17 Groups
56 Types
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Pre-2001 classification – outer Southampton
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New ACORN for same postcode
Reflecting late 1990s new-build housing
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Contemporary applications
• Increasing opportunity to integrate data
from different scales:
– Person/address
– Postcode
– Output area
• Neighbourhood data most powerful
where relatively little transactional data
available about customer base
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Assignment
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Select variable set and study region
CASWEB retrieval (including denominators)
Run Geodemo spreadsheet
Import dataset
Define denominators
Select number of classes
Run/re-run
Label classes
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Lecture summary
• Geodemographic classifications seeking
to capture key differentiating
characteristics by data reduction
• Subjective decisions in cluster
methodology and labelling
• Use of census and non-census data,
multiple geographies
• Widespread commercial applications
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