An examination of tourist arrivals dynamics using short-term time series data: a space-time cluster approach
AbstractThe 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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Bibliographic InfoPaper provided by School of Economics and Management at the Free University of Bozen in its series BEMPS - Bozen Economics & Management Paper Series with number BEMPS06.
Length: 26 pages
Date of creation: Jun 2013
Date of revision:
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cluster analysis; short time series; spline interpolation; K-means; join count test; Italian tourist areas;
Find related papers by JEL classification:
- L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-06-16 (All new papers)
- NEP-ECM-2013-06-16 (Econometrics)
- NEP-TUR-2013-06-16 (Tourism Economics)
- NEP-URE-2013-06-16 (Urban & Real Estate Economics)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- 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.
- 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.
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