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An integration of aggregate and disaggregate census data

An integration of aggregate and disaggregate census data
An integration of aggregate and disaggregate census data

Identifying the different social characteristics and location of residential populations is an important task (Shevky and Bell 1955), both for social theorists and for the machinery of state and administration. Currently, the data with which the relationship between social character and residential location is explored, are inadequate for the task. This inadequacy stems from the lack of detailed information about social structure at a fine spatial scale (Birkin and Clarke, 1995). The source most commonly applied to issues of socio-spatial structure is the Small Area Statistics (SAS), drawn from the 1991 UK Census Of Population. The SAS data are aggregate and do not provide detailed information on the characteristics of households or individuals for the small spatial areas which they describe. They are therefore often used to provide single classifications of areas, based upon their aggregate census characteristics.

In 1993 a new, disaggregate, data set was released from the 1991 Census of Population. The Samples of Anonymised Records (SAR) provide considerable detail about the characteristics of two samples of households and individuals. However, a restriction on the spatial resolution of these data is employed to assist in preserving the anonymity of sample members. Alone, the SAR is not suited to the detailed analysis of socio-spatial structure required.

Through the use of social theory (Harvey, 1989), an analogy may be drawn between the problems in using aggregate census data and those faced by Earth Observation (EO) researchers in the analysis of satellite imagery. EO researchers have developed solutions to these problems, through the fuzzy classification of small areas. Such an approach is adapted for use with census data, through deriving a classification of households from the SAR and inferring the proportional presence of these household classes in a small area, from the SAS.

University of Southampton
Mitchell, Richard James Lamacraft
9e346826-b2f0-43f2-a9fd-75748247dfb0
Mitchell, Richard James Lamacraft
9e346826-b2f0-43f2-a9fd-75748247dfb0

Mitchell, Richard James Lamacraft (1997) An integration of aggregate and disaggregate census data. University of Southampton, Doctoral Thesis.

Record type: Thesis (Doctoral)

Abstract

Identifying the different social characteristics and location of residential populations is an important task (Shevky and Bell 1955), both for social theorists and for the machinery of state and administration. Currently, the data with which the relationship between social character and residential location is explored, are inadequate for the task. This inadequacy stems from the lack of detailed information about social structure at a fine spatial scale (Birkin and Clarke, 1995). The source most commonly applied to issues of socio-spatial structure is the Small Area Statistics (SAS), drawn from the 1991 UK Census Of Population. The SAS data are aggregate and do not provide detailed information on the characteristics of households or individuals for the small spatial areas which they describe. They are therefore often used to provide single classifications of areas, based upon their aggregate census characteristics.

In 1993 a new, disaggregate, data set was released from the 1991 Census of Population. The Samples of Anonymised Records (SAR) provide considerable detail about the characteristics of two samples of households and individuals. However, a restriction on the spatial resolution of these data is employed to assist in preserving the anonymity of sample members. Alone, the SAR is not suited to the detailed analysis of socio-spatial structure required.

Through the use of social theory (Harvey, 1989), an analogy may be drawn between the problems in using aggregate census data and those faced by Earth Observation (EO) researchers in the analysis of satellite imagery. EO researchers have developed solutions to these problems, through the fuzzy classification of small areas. Such an approach is adapted for use with census data, through deriving a classification of households from the SAR and inferring the proportional presence of these household classes in a small area, from the SAS.

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Published date: 1997

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Local EPrints ID: 463077
URI: http://eprints.soton.ac.uk/id/eprint/463077
PURE UUID: 4e169f29-d3a3-43ad-ab20-d6188d2cd2be

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Date deposited: 04 Jul 2022 20:43
Last modified: 16 Mar 2024 19:01

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Author: Richard James Lamacraft Mitchell

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