Unmixing Aggregate Data

Estimating the Social Composition of Enumeration Districts

Dados Bibliográficos

ID4860012
AutoresR Mitchell (0000-0003-3827-7155, Social Statistics Research Unit, City University, Northampton Square, London EC1V OHB, England, autor correspondente), David Martins (0000-0003-0397-0769, University of Southampton, autor correspondente), D Martin (0000-0001-5639-4748, University of Southampton), G M Foody (0000-0001-6464-3054, University of Southampton, autor correspondente)
Ano1998
Volume30
Fascículo11
Páginas1929-1941
Data de publicação1998-11-01
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoEnvironment and Planning A Economy and Space (JOURNAL)
Identificadores do periódicoISSN: 0308-518X • E-ISSN: 1472-3409
EditoraSAGE Publications Inc (PUBLISHER)
DOI10.1068/a301929
PMID12294199
OpenAlexW1988113054
IdiomaEN
Citações recebidas5
Referências citadas10

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

Aggregate data · Art · Combinatorics · Compositional data · Econometrics · Enumeration · Geography · Statistics · Income, Poverty, and Inequality · Local Government Finance and Decentralization · Mathematics · Urban, Neighborhood, and Segregation Studies