1. Geodemographic classification schemes
GEOG3025

2. Geodemographic classification schemes
Lecture overview:
History
Data sources
Classification methods
Selection and labelling of classes
Examples
Applications
GEOG3025

3. 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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4. Introductory questions…
What is a geodemographic classifier?
How does it capture the characteristics of a neighbourhood?
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5. 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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6. 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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7. 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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8. What makes neighbourhoods different?
Photo: Samantha Cockings
Photo: Dave Martin
Photo: Dave Martin
Multiple dimensions…
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9. 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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10. e.g. 2 standardised variables
HIGH
e.g. run down larger houses in unfashionable inner suburbs
e.g. Prestigious large houses in semi-rural commuter locations
LOW
e.g. income
HIGH
e.g. Apartments in prestigious modern urban redevelopments
e.g. Inner city high rise rented flats
LOW
e.g. house size
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11. Data on 2 standardised variables
e.g. income
e.g. house size
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12. Initial cluster centres
e.g. income
e.g. house size
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13. Allocation of data to cluster centres
e.g. income
e.g. house size
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14. Iteration to achieve final cluster memberships
e.g. income
e.g. house size
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15. 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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16. Clustering by grouping of most similar observations4 1 2
e.g. income 3
e.g. house size
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17. Classification dendrogram
Number of classes
Individual observations…
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18. 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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19. Example ACORN hierarchy…
New (2001) Acorn classification:
5 Categories
17 Groups
56 Types
Source:
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20. GEOG3025 Pre-2001 classification – outer Southampton
Source:
Photos: Dave Martin
Pre-2001 classification – outer Southampton
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21. New ACORN for same postcode
Source:
Photos: Dave Martin
Reflecting late 1990s new-build housing
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22. 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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23. Assignment 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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24. 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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