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How The British Population Survey can enhance geodemographics
 

How The British Population Survey can enhance geodemographics

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Martin Callingham

Martin Callingham
Visiting Professor
Birkbeck College
University of London

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    How The British Population Survey can enhance geodemographics How The British Population Survey can enhance geodemographics Presentation Transcript

    • How The British Population Survey can enhance geodemographics Royal Statistical Society OAC Conference September 6 th Martin Callingham Visiting Professor Birkbeck College University of London
    • Regionality Bespoke combinations Comparison of systems Changing character over time Resolution of ambiguity Importance of each census variable Highly resolved OAC Introduction
    • Centre of gravity of each OAC 52 sub-group
    • Regionality OAC types are distributed unevenly across the country So the mix in an area is unique It is a principle of geodemographics that a type in Brighton is the same as a type in Bolton Is this true?
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    • Bespoke combinations Because the mix in an area is unique some areas have none or very few of particular OAC types It is often sensible to combine the low represent types together to form bigger hybrid types But what are the characteristics of these bespokely formed types?
    • Relationship between the levels in the system and mean Area % R Squared =0.99 10 15 20 25 30 35 0 10 20 30 40 50 60 70 Number of Levels Area % Effectiveness of a system depends only upon the number of levels
    • Comparison of systems There are a variety of different geodemographic systems Which is the ‘best’? Which is the best for me?
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    • Changing character over time An area will change over time due to population movements It will also change over time due to social and economic factors What are the types of changes that could happen? Are some OAC types more likely to change than others?
    • Distribution of standard deviation of the distance from centroid of OAC subgroups
    • Resolution of ambiguity Geodemographics are created by using cluster analysis This seeks to group records that are similar together Sometimes record are really quite different from the mass But these still have to be put into a cluster on the basis of ‘least worst’ fit What effect does this variation cause?
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    • Importance of each census component 41 variables are used in the creation of OAC Each has been carefully selected But fewer variables make better segmentation (to a limit) What actually is the impact of each variable on the use of OAC? Could the number of variables be reduced?
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    • Highly resolved OAC The highest number of clusters in OAC is 52 A greater number would give greater discrimination Creating OAC with many more variables is a trivial process But what do they mean?
    • Summary Regionality Bespoke combinations Comparison of systems Changing character over time Resolution of ambiguity Importance of each census variable Highly resolved OAC
    • THE END
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