An examination of tourist arrivals dynamics using short-term time series data: a space-time cluster approach
The purpose of this study is to examine the development of Italian tourist areas (circoscrizioni turistiche) through a cluster analysis of short time series. The technique is an adaptation of the functional data analysis approach developed by Abraham et al (2003), which combines spline interpolation with k-means clustering. The findings indicate the presence of two patterns (increasing and stable) averagely characterizing groups of territories. Moreover, tests of spatial contiguity suggest the presence of â€˜spaceâ€“time clustersâ€™; that is, areas in the same â€˜time clusterâ€™ are also spatially contiguous. These findings appear to be more robust in particular for those series characterized by an increasing trend.
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- JG. Brida & M. Pulina, 2010. "A literature review on the tourism-led-growth hypothesis," Working Paper CRENoS 201017, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
- C. Abraham & P. A. Cornillon & E. Matzner-Løber & N. Molinari, 2003. "Unsupervised Curve Clustering using B-Splines," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 30(3), pages 581-595.
- Mark Chiang & Boris Mirkin, 2010. "Intelligent Choice of the Number of Clusters in K-Means Clustering: An Experimental Study with Different Cluster Spreads," Journal of Classification, Springer, vol. 27(1), pages 3-40, March.
- Daria Mendola & Raffaele Scuderi & Valerio Lacagnina, 2013. "Defining and measuring the development of a country over time: a proposal of a new index," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(5), pages 2473-2494, August.
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