ABSTRACT
There are a variety of commercial geodemographic classifications which can be used to inform spatial decision making in Higher Education, however, none have been specifically designed for this purpose. Although geodemographic classifications originated in the public sector they were subsequently augmented with private sector data for commercial applications such as customer segmentation and direct mailing. In socio-economic clustering applications such as geodemographics, it is proposed that dissection is a more accurate representation of the function of the clustering algorithm. A model was created which estimated the 1819 year old base population in England and Wales at unit postcode level and was compared to the same age range accepting Higher Education places in 2004. Variable performance was found, with high correlation with the lowest POLAR and NS-SEC 6. Some issues requiring further investigation were the lower correlation with NS-SEC 7 and 4. The highest correlation was recorded using the Experian "wealth" ranking.
