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Positioning and clustering of the world's top tourist destinations by means of dimensionality reduction techniques for categorical data

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This study aims to cluster the world's top tourist destinations based on the growth of the main tourism indicators over the period between 2000 and 2010. It ranks the destinations with respect to the average growth rate over the sample period. The results find that both China and Turkey are at the top of the rankings of all variables. By assigning a numerical value to each country corresponding to its position, a Spearman's coefficient is calculated and a negative correlation found between a destination's dependency on tourism and the profitability of the tourism activity. Finally, several multivariate techniques for dimensionality reduction are used to cluster all destinations according to their positioning. Three groups are obtained: China, Turkey, and the rest of the destinations. These results show that the persistent growth of the tourism industry poses different challenges in different markets regarding destination marketing and management.

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Journal of Destination Marketing & Management, 2016, vol. 6, num. 1, p. 22-32

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Articles publicats en revistes  (Econometria, Estadística i Economia Aplicada)

# metadata.dc.title[].value
Positioning and clustering of the world's top tourist destinations by means of dimensionality reduction techniques for categorical data