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.
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References
Bagnasco A (1977) Tre Italie. La problematica territoriale dello sviluppo italiano. IL Mulino, Bologna
Becattini G (1996) I sistemi locali nello sviluppo economico italiano e nella sua interpretazione. Sviluppo Locale 2–3: 5–25
Becattini G (2004) Industrial districts. A new approach to industrial change. Edward Elgar, Cheltenham
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
Bock HH, Diday E (2000) Analysis of symbolic data. Springer, Berlin
Chavent M, de Carvalho F, Lechevallier Y, Verde R (2006) New clustering methods of interval data. Comput Stat 21: 211–229 (Physica-Verlag)
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
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
Dunford M, Greco L (2005) After the three Italies: wealth, inequality and industrial change. Blackwell Publishing, Oxford
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
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]
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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
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DOI: https://doi.org/10.1007/s11634-008-0024-5