Skip to main content
Log in

Cluster analysis of census data using the symbolic data approach

  • Regular Article
  • Published:
Advances in Data Analysis and Classification Aims and scope Submit manuscript

Abstract

The aim of this paper is to investigate the economic specialization of the Italian local labor systems (sets of contiguous municipalities with a high degree of self-containment of daily commuter travel) by using the Symbolic Data approach, on the basis of data derived from the Census of Industrial and Service Activities. Specifically, the economic structure of a local labor system (LLS) is described by an interval-type variable, a special symbolic data type that allows for the fact that all municipalities within the same LLS do not have the same economic structure.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Bagnasco A (1977) Tre Italie. La problematica territoriale dello sviluppo italiano. IL Mulino, Bologna

    Google Scholar 

  • Becattini G (1996) I sistemi locali nello sviluppo economico italiano e nella sua interpretazione. Sviluppo Locale 2–3: 5–25

    Google Scholar 

  • Becattini G (2004) Industrial districts. A new approach to industrial change. Edward Elgar, Cheltenham

    Google Scholar 

  • Billard L, Diday E (2003) From the statistics of data to the statistics of knowledge: symbolic data analysis. J Am Stat Assoc 462: 470–487

    Article  MathSciNet  Google Scholar 

  • Bock HH, Diday E (2000) Analysis of symbolic data. Springer, Berlin

    Google Scholar 

  • Chavent M, de Carvalho F, Lechevallier Y, Verde R (2006) New clustering methods of interval data. Comput Stat 21: 211–229 (Physica-Verlag)

    Article  MATH  Google Scholar 

  • de Carvalho F, Lechevallier Y, Verde R (2004) Clustering methods in symbolic data analysis. In: Banks D, House L, McMorris FR, Arabie P, Gaul E (eds) Classification, clustering, and data mining applications. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, pp. 299–317

  • Dei Ottati G, Grassini L (2006) Le trasformazioni industriali negli anni Novanta Un confronto fra sistemi locali di grande impresa e distretti industriali. In: Filippucci C (eds) Mutamenti nella geografia dell’economia italiana. Franco Angeli, Milano, pp. 135–157

  • Dei Ottati G, Grassini L (2008) Italy’s employment changes in the nineties: a comparison between large enterprise areas and industrial districts. Envir Plan C 26: 5

    Google Scholar 

  • Diday E (2002) An introduction to symbolic data analysis and the SODAS Software. Electron J Symbol Data Anal n.0

  • Diday E, Noirhomme-Fraiture M (2008) Symbolic data analysis and the SODAS Software. Wiley, New York

    MATH  Google Scholar 

  • Dunford M, Greco L (2005) After the three Italies: wealth, inequality and industrial change. Blackwell Publishing, Oxford

    Google Scholar 

  • Facchinetti G, Mastroleo G, Paba S (2000) A fuzzy approach to the geography of industrial districts. Applied Computing Symposium, Como, Italy. http://www.acm.org/conferences

  • ISTAT (2006) Rapporto annuale 2005. ISTAT, Roma

  • ISTAT (2007) Rapporto annuale 2006. ISTAT, Roma

  • ISTAT-Sforzi F (1997) I sistemi locali del lavoro 1991. ISTAT, Roma

  • Merino F, Rubalcaba-Bermejo L (1999) Business services in European economy. European Commission, Brussels

    Google Scholar 

  • SODAS (2004) User manual for the SODAS 2 Software. ASSO/WP3/D3.4 b, ASSO (Analysis System of Symbolic Official Data) Project [IST-200-25162]

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Antonio Giusti.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Giusti, A., Grassini, L. Cluster analysis of census data using the symbolic data approach. Adv Data Anal Classif 2, 163–176 (2008). https://doi.org/10.1007/s11634-008-0024-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11634-008-0024-5

Keywords

Mathematics Subject Classification (2000)

Navigation