Geodemographics, GIS and Neighbourhood TargetingGeodemographic classification is ‘big business’ in the marketing and service sector industries, and in public policy there has also been a resurgence of interest in neighbourhood initiatives and targeting. As an increasing number of professionals realise the potential of geographic analysis for their business or organisation, there exists a timely gap in the market for a focussed book on geodemographics and GIS. Geodemographics: neighbourhood targeting and GIS provides both an introduction to and overview of the methods, theory and classification techniques that provide the foundation of neighbourhood analysis and commercial geodemographic products. Particular focus is given to the presentation and use of neighbourhood classification in GIS.
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Contents
List of Case Study Contributors | xi |
London to Chicago and Back Again The Origins | 29 |
Charles Booth yesterday once more? | 45 |
Further Reading | 51 |
The Evolution of Geodemographics and the Market Today | 53 |
Geodemographics and GIS | 77 |
Geodemographic Information Systems and Analysis | 109 |
How Geodemographic Classifications are Built | 147 |
Geodemographics Around the World | 185 |
But Does It Work? Geodemographics in the Dock | 207 |
There are three Is in geodemographics | 273 |
279 | |
289 | |
Common terms and phrases
ACORN actually analysis apparent applications approach areas assigned associated attribute average build calculated catchment census centre Chapter cities classification clusters collection Community companies comparing consider consumer contain correlation count countries customers datasets defined demographic deprivation described detail distance effects example Families Figure further geodemographic geographical give given households identify important income increasing indices individual interest less lifestyle linked live London look mean measure methods Mosaic neighbourhood neighbourhood classification neighbourhood type objects particular patterns places population postcode problem proportion ranked region relative represented respondents result sample sector selected shows similar single social solution sources spatial specific statistics survey Table techniques unit University urban users usually variables weight zones