What is it about?

This study positions the world’s top ten tourist destinations based on the growth of the main tourism indicators over the period between 2000 and 2010. We rank the destinations with respect to the average growth rate over the sample period. Destinations are clustered according to their relative position in the rankings. We position the destinations in perceptual maps generated by means of dimensionality reduction techniques for categorical data (CATPCA and MDS).

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Why is it important?

We find that China and Turkey experienced the highest average growth. Both countries are clustered apart from the other eight destinations. The rest of the destinations can be grouped into three major cultural areas reflecting the prevailing language. These results show that the dynamics of growth in the tourism industry pose different challenges to each destination for marketing and management.


This research aims to contribute to destination research literature by analysing how the dynamic interactions between the main tourism indicators ultimately affect the positioning of destinations. We also try to highlight the utility of multivariate techniques for destination marketing and management. A comparison between a larger number of tourist destinations and indicators would provide a more complete picture of the tourism market. Furthermore, incorporating residents’ perception about incoming tourism would serve as a proxy for the carrying capacity of tourist destinations, and could help policymakers to manage the problems derived from congestion.

Oscar Claveria
AQR-IREA, Univeristy of Barcelona

Read the Original

This page is a summary of: Positioning and clustering of the world’s top tourist destinations by means of dimensionality reduction techniques for categorical data, Journal of Destination Marketing & Management, March 2017, Elsevier,
DOI: 10.1016/j.jdmm.2016.01.008.
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