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RESEARCH PRODUCT

Short term Dynamics of tourist Arrivals: What do destination have in common?

subject

description

This work aims to detect the common short term dynamics to yearly time series of 413 Italian tourist areas. We adopt the clustering technique of Abraham et al. (Scand J Stat. 30:581–595, 2003) who propose a two-stage method which fits the data by B-splines and partitions the estimated model coefficients using a k-means algorithm. The description of each cluster, which identifies a specific kind of dynamics, is made through simple descriptive cross tabulations in order to study how the location of the areas across the regions or their prevailing typology of tourism characterize each group.