<abstract xmlns="http://www.w3.org/1999/xhtml"><p> Due to confidentiality considerations, the microdata available from the 2011 Spanish Census have been codified at a provincial (NUTS 3) level except when the municipal (LAU 2) population exceeds 20,000 inhabitants (a requirement that is met by less than 5% of all municipalities). For the remainder of the municipalities within a given province, information is only provided for their classification in wide population intervals. These limitations, hampering territorially-focused socio-economic analyses, and more specifically, those related to the labour market, are observed in many other countries. This article proposes and demonstrates an automatic procedure aimed at delineating a set of areas that meet such population requirements and that may be used to re-codify the geographic reference in these cases, thereby increasing the territorial detail at which individual information is available. The method aggregates municipalities into clusters based on the optimisation of a relevant objective function subject to a number of statistical constraints, and is implemented using evolutionary computation techniques. Clusters are defined to fit outer boundaries at the level of labour market areas.</p></abstract>

Due to confidentiality considerations, the microdata available from the 2011 Spanish Census have been codified at a provincial (NUTS 3) level except when the municipal (LAU 2) population exceeds 20,000 inhabitants (a requirement that is met by less than 5% of all municipalities). For the remainder of the municipalities within a given province, information is only provided for their classification in wide population intervals. These limitations, hampering territorially-focused socio-economic analyses, and more specifically, those related to the labour market, are observed in many other countries. This article proposes and demonstrates an automatic procedure aimed at delineating a set of areas that meet such population requirements and that may be used to re-codify the geographic reference in these cases, thereby increasing the territorial detail at which individual information is available. The method aggregates municipalities into clusters based on the optimisation of a relevant objective function subject to a number of statistical constraints, and is implemented using evolutionary computation techniques. Clusters are defined to fit outer boundaries at the level of labour market areas.

Delineating zones to increase geographical detail in individual response data files: An application to the Spanish 2011 Census of population

International Economics Institute, University of Alicante, Spain

International Economics Institute and Department of Applied Economic Analysis, University of Alicante, Spain

Moravian Geographical Reports

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Due to confidentiality considerations, the microdata available from the 2011 Spanish Census have been codified at a provincial (NUTS 3) level except when the...

Delineating zones to increase geographical detail in individual response data files: An application to the Spanish 2011 Census of population

Publié en ligne: 08 juil. 2016

Pages: 26 - 36

Reçu: 30 sept. 2015

Accepté: 10 déc. 2015

DOI: https://doi.org/10.1515/mgr-2016-0008

Mots clés
labour market areas, census, microdata, regionalisation, clustering, evolutionary computation, Spain

© 2016

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Delineating zones to increase geographical detail in individual response data files: An application to the Spanish 2011 Census of population

Publié en ligne: 08 juil. 2016

Pages: 26 - 36

Reçu: 30 sept. 2015

Accepté: 10 déc. 2015

DOI: https://doi.org/10.1515/mgr-2016-0008

Mots cléslabour market areas, census, microdata, regionalisation, clustering, evolutionary computation, Spain

© 2016

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Mots clés
labour market areas, census, microdata, regionalisation, clustering, evolutionary computation, Spain