6533b870fe1ef96bd12cfd60
RESEARCH PRODUCT
An Examination of Tourist Arrivals Dynamics Using Short-Term Time Series Data: A Space—Time Cluster Approach
Raffaele ScuderiDogan GursoyAnna Maria Parrocosubject
spline interpolationjoin count testSeries (mathematics)Computer scienceSpace timeGeography Planning and Developmentk-means clusteringcluster analysis; short time series; spline interpolation; K-means; join count test; Italian tourist areasFunctional data analysisjel:C21jel:C22jel:C38jel:C14jel:L83K-meanshort time serieContiguity (probability theory)Tourism Leisure and Hospitality Managementcluster analysiItalian tourist areasEconometricsCluster (physics)Settore SECS-S/05 - Statistica SocialeSpline interpolationCluster analysisdescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2013-08-01 | Tourism Economics |