Skip to main content
SpringerLink
Log in

Classifying Tourism Destinations: An Application of Network Analysis

  • Conference paper
  • First Online:
Statistical Models for Data Analysis

Abstract

Tourism is basically a spatial phenomenon, which implies moving consumption within space. Starting from the assumption that the destinations are nodes of a network, we are able to reconstruct a spatial grid where each locality shows different grades and types of centrality. The analysis, focusing on the spatial dimension, shows clusters of locations. By shifting interest from single locations to destination networks, the study points out the structural features of each network. Employing traditional network analysis measures, we classify destinations considering the routes of a self-organized tourists sample that visited more than one destination in Sicily.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    The survey is carried out as PRIN 2007–2009 “Socio-economic effects of behavior and motivations of real tourism in Sicily. Internal mobility and its economic effects” by University of Palermo, Catania, Sassari, Bologna. Selected data consist in face-to-face interviews submitted to tourists during their departures from most important Sicilian airports and ports.

  2. 2.

    A complete graph is one in which all the points are adjacent to one another (Wasserman and Faust 1994, p. 102) and each point is connected directly to every other point.

References

  • Borgatti, S. P., & Everett, M. G. (1992). Notions of positions in social network analysis. Sociological Methodology, 22, 1–35.

    Article  Google Scholar 

  • Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2002). Ucinet for windows: Software for social network analysis. Harvard: Analytic Technologies.

    Google Scholar 

  • Borgatti, S. P., Everett, M. G., & Shirey, P. (1990). LS sets, lambda sets and other cohesive subsets. Social Networks, 12, 337–357.

    Article  MathSciNet  Google Scholar 

  • Breiger, R., Boorman, S., & Arabie, P. (1975). An algorithm for clustering relational data with applications to social network analysis and comparison with multidimensional scaling. Journal of Mathematical Psychology, 12, 328–383.

    Article  Google Scholar 

  • Hanneman, R. A., & Riddle M. (2005). Introduction to social network methods. Riverside: University of California. http://faculty.ucr.edu/~hanneman/nettext/Introduction_to_Social_Network_Methods.pdf.

  • Nunkesser, M., & Sawitzki, D. (2005). Blockmodels. In U. Brandes & T. Erlebach (Eds.), Network analysis: Methodological foundations (pp. 253–292). Hidelberg: Springer.

    Chapter  Google Scholar 

  • Opsahl, T., Agneessens, F., & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32, 245–251.

    Article  Google Scholar 

  • Rowley, T. J. (1997). Moving beyond dyadic ties: A network theory of stakeholder influences. The~Academy of Management Review, 22(4), 887–910.

    Google Scholar 

  • Schwartz, J. E. (1977). An examination of CONCOR and related methods for blocking sociometric data. Sociological Methodology, 7, 255–282.

    Article  Google Scholar 

  • Scott, J. (2000). Social network analysis: A handbook (2nd ed.). London: Sage.

    Google Scholar 

  • Scott, N., Baggio, R., & Cooper, C. (2008). Network analysis and tourism. From theory to practice. Clevedon: Channel View.

    Google Scholar 

  • Shih, H. Y. (2006). Network characteristics of drive tourism destinations: An application of network analysis in tourism. Tourism Management, 27(5), 1029–1039.

    Article  Google Scholar 

  • UNWTO. (2002). News release: WTO Think Tank enthusiastically reaches consensus on frameworks for tourism destination success. http://destination.unwto.org/en/content/conceptual-framework-0. Accessed June 2011.

  • Wasserman, S., & Faust, K. (1994). Social network analysis. Methods and applications. Cambridge: Cambridge University Press.

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Catania, Vitt. Emanuele II, 8, Catania, Italy

    Rosario D’Agata & Venera Tomaselli

Authors
  1. Rosario D’Agata
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Venera Tomaselli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rosario D’Agata .

Editor information

Editors and Affiliations

  1. Department of Economics, and Management, University of Pavia, Via San Felice 7, Pavia, 27100, Italy

    Paolo Giudici

  2. Department of Economics, and Business, University of Catania, Corso Italia 55, Catania, 95129, Italy

    Salvatore Ingrassia

  3. , Department of Statistics, University of Rome "La Sapienza", Piazzale Aldo Moro 5, Rome, 00185, Italy

    Maurizio Vichi

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer International Publishing Switzerland

About this paper

Cite this paper

D’Agata, R., Tomaselli, V. (2013). Classifying Tourism Destinations: An Application of Network Analysis. In: Giudici, P., Ingrassia, S., Vichi, M. (eds) Statistical Models for Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00032-9_12

Download citation

Publish with us

Policies and ethics

Search

Navigation