Unmixing Aggregate Data: Estimating the Social Composition of Enumeration Districts

Abstract

In this paper the authors address the problem of interpreting and classifying aggregate data sources and draw parallels between tasks commonly encountered in image processing and census analysis. Both of these fields already have a range of standard classification tools which are applied in such situations, but these are hindered by the aggregate nature of the input data. An approach to ‘unmixing’ aggregate data, and thus to revealing the nature of the subunit variation masked by aggregation, is introduced. This approach has already shown considerable success in Earth Observation applications, and in this paper the authors present the adaptation and application of the approach to Census small area statistics data for Southampton, Hants, revealing something of the social composition of Southampton's enumeration districts. The unmixing technique utilises an artificial neural network.