Geodemographic classification schemes GEOG3025

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

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 GEOG3025

Introductory questions… What is a geodemographic classifier? How does it capture the characteristics of a neighbourhood? GEOG3025

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 GEOG3025

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 GEOG3025

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 GEOG3025

What makes neighbourhoods different? Photo: Samantha Cockings Photo: Dave Martin Photo: Dave Martin Multiple dimensions… GEOG3025

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’ GEOG3025

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 GEOG3025

Data on 2 standardised variables e.g. income e.g. house size GEOG3025

Initial cluster centres e.g. income e.g. house size GEOG3025

Allocation of data to cluster centres e.g. income e.g. house size GEOG3025

Iteration to achieve final cluster memberships e.g. income e.g. house size GEOG3025

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? GEOG3025

Clustering by grouping of most similar observations 4 1 2 e.g. income 3 e.g. house size GEOG3025

Classification dendrogram Number of classes Individual observations… GEOG3025

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 GEOG3025

Example ACORN hierarchy… New (2001) Acorn classification: 5 Categories 17 Groups 56 Types Source: www.caci.co.uk GEOG3025

GEOG3025 Pre-2001 classification – outer Southampton Source: www.upmystreet.com Photos: Dave Martin Pre-2001 classification – outer Southampton GEOG3025

New ACORN for same postcode Source: www.caci.co.uk Photos: Dave Martin Reflecting late 1990s new-build housing GEOG3025

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 GEOG3025

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 GEOG3025

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 GEOG3025