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Unmixing Aggregate Data

Estimating the Social Composition of Enumeration Districts

Datos Bibliográficos

ID4860012
AutoresR Mitchell (0000-0003-3827-7155, Social Statistics Research Unit, City University, Northampton Square, London EC1V OHB, England, autor de correspondencia), David Martins (0000-0003-0397-0769, University of Southampton, autor de correspondencia), D Martin (0000-0001-5639-4748, University of Southampton), G M Foody (0000-0001-6464-3054, University of Southampton, autor de correspondencia)
Año1998
Volumen30
Número11
Páginas1929-1941
Fecha de publicación1998-11-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaEnvironment and Planning A Economy and Space (JOURNAL)
Identificadores de la revistaISSN: 0308-518X • E-ISSN: 1472-3409
EditorialSAGE Publications Inc (PUBLISHER)
DOI10.1068/a301929
PMID12294199
OpenAlexW1988113054
IdiomaEN
Citas recibidas5
Referencias 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

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Obras citantes distintas5
Citas por año0,2
Intervalo de citas2001 - 2011 (11)
Velocidad de citaciónhistorical
Altamente citadoNo
Tipos de citaNeutras: 5
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