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An examination of tourist arrivals dynamics using short-term time series data: a space-time cluster approach

Author

Listed:
  • Dogan Gursoy

    (School of Hospitality Business Management, Washington State University)

  • Anna Maria Parroco

    (Department of Economics, Business and Finance, University of Palermo)

  • Raffaele Scuderi

    (Free University of Bolzano‐Bozen, School of Economics and Management.)

Abstract

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 ‘space–time clusters’; that is, areas in the same ‘time cluster’ are also spatially contiguous. These findings appear to be more robust in particular for those series characterized by an increasing trend.

Suggested Citation

  • Dogan Gursoy & Anna Maria Parroco & Raffaele Scuderi, 2013. "An examination of tourist arrivals dynamics using short-term time series data: a space-time cluster approach," BEMPS - Bozen Economics & Management Paper Series BEMPS06, Faculty of Economics and Management at the Free University of Bozen.
    • Handle: RePEc:bzn:wpaper:bemps06
    as

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    References listed on IDEAS

    as
    1. 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;The Classification Society, vol. 27(1), pages 3-40, March.
    2. 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, September.
    3. 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.
    4. 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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    Cited by:

    1. Alessandro Magrini, 2022. "Assessment of agricultural sustainability in European Union countries: a group-based multivariate trajectory approach," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(4), pages 673-703, December.
    2. Enrico Conti & Laura Grassini & Catia Monicolini, 2020. "Tourism competitiveness of Italian municipalities," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(5), pages 1745-1767, December.

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    More about this item

    Keywords

    cluster analysis; short time series; spline interpolation; K-means; join count test; Italian tourist areas;
    All these keywords.

    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; Diffusion Processes
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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