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.
ButlerR.W. (1980), ‘The concept of a tourism area cycle of evolution: Implications for the management of resources’, Canadian Geographer, Vol 24, No 1, pp 5–12.
9.
ChiangM.M.T.MirkinB. (2010), ‘Intelligent choice of the number of clusters in k-means clustering: An experimental study with different cluster spreads’, Journal of Classification, Vol 27, No 1, pp 3–40.
10.
CliffA.D.OrdJ.K. (1981), Spatial Processes – Models and Applications, Pion, London.
11.
CrouchG.I. (1994), ‘The study of international tourism demand: A survey of practice’, Journal of Travel Research, Vol 33, No 4, pp 41–55.
12.
CurryH.B.SchoenbergI.J. (1966), ‘On Pólya frequency functions IV: The fundamental spline functions and their limits’, Journal d'Analyse Mathematique, Vol 17, pp 71–107.
13.
De BoorC. (1978), A Practical Guide to Splines, Springer, New York.
14.
De CantisS.FerranteM.VaccinaF. (2011), ‘Seasonal pattern and amplitude – a logical framework to analyse seasonality in tourism: An application to bed occupancy in Sicilian hotels’, Tourism Economics, Vol 17, No 3, pp 655–675.
15.
DolnicarS. (2002), ‘A review of data-driven market segmentation in tourism’, Journal of Travel & Tourism Marketing, Vol 12, No 1, pp 1–22.
16.
ChouakriaA. DouzalDialloA.GiroudF. (2007), ‘Adaptive clustering of time series’, Proceedings of the IASC ConferenceStatistics for Data Mining, Learning and Knowledge Extraction, Aveiro, Portugal.
17.
EspaG.ArbiaG.BenedettiR. (1996). ‘Effects of the MAUP on image classification’, Geographical Systems, Vol 3, No 2–3, pp 123–141.
18.
FarsariI. (2012), ‘The development of a conceptual model to support sustainable tourism policy in North Mediterranean destinations’, Journal of Hospitality Marketing & Management, Vol 21, No 7, pp 710–738.
19.
FormicaS.UysalM. (1996), ‘The revitalization of Italy as a tourist destination’, Tourism Management, Vol 17, No 5, pp 323–331.
20.
GooroochurnN.SugiyartoG. (2005), ‘Competitiveness indicators in the travel and tourism industry’, Tourism Economics, Vol 11, No 1, pp 25–43.
21.
HanJ.KamberM. (2001), Data Mining: Concepts and Techniques, Morgan Kaufmann, San Francisco.
22.
HanG.FangW.-T.HuangY.W. (2011), ‘Classification and influential factors in the perceived tourism impacts of community residents on nature-based destinations: China's Tiantangzhai scenic area’, Procedia Environmental Sciences, Vol 10, Part C, pp 2010–2015.
23.
HartiganJ.A.WongM.A. (1979), ‘A k-means clustering algorithm’, Applied Statistics, Vol 28, No 1, pp 100–108.
LiaoT.W. (2005), ‘Clustering of time series data – a survey’, Pattern Recognition, Vol 38, No 11, pp 1857–1874.
27.
MendolaD.ScuderiR.LacagninaV. (2012), ‘Defining and measuring the development of a country over time: A proposal of a new index’, Quality & Quantity, doi:10.1007/s11135–012–9665–8.
Möller-LevetC.S.KlawonnF.ChoK.-H. (2003), ‘Fuzzy clustering of short time series and unevenly distributed sampling points’, Lecture Notes in Computer Science, Vol 2810, pp 330–340.
30.
MooreW.WhitehallP. (2005), ‘The tourism area lifecycle and regime switching models’, Annals of Tourism Research, Vol 32, No 1, pp 112–126.
31.
ParrocoA.M.ScuderiR. (2012), ‘Short term dynamics of tourist arrivals: What do Italian destinations have in common?’, in GaulW.Geyer-SchulzA.Schmidt-ThiemeL.KunzeJ., eds, Challenges at the Interface of Data Analysis, Computer Science, and Optimization, Studies in Classification, Data Analysis, and Knowledge Organization, Springer, Heidelberg-Berlin, pp 577–584.
32.
PiccoloD. (1990), ‘A distance measure for classifying ARIMA models’, Journal of Time Series Analysis, Vol 11, No 2, pp 153–164.
33.
R Development Core Team (2011), R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna.
RipleyB.D. (1981), Spatial Statistics, Wiley, New York.
36.
RipleyB.D. (1996), Pattern Recognition and Neural Networks, Cambridge University Press, Cambridge MA.
37.
SchumakerL.L. (1981), Spline Functions: Basic Theory, Wiley, New York.
38.
SmeralE. (2007), ‘Research note: World tourism forecasting – keep it quick, simple and dirty’, Tourism Economics, Vol 13, No 2, pp 309–317.
39.
ToblerW.R. (1970), ‘A computer movie simulating urban growth in the Detroit region’, Economic Geography, pp 234–240.
40.
UptonG.FingletonB. (1985), Spatial Data Analysis by Example: Point Pattern and Quantitative Data, Wiley, Chichester.
41.
UzamaA. (2012), ‘Yokoso! Japan: Classifying foreign tourists to Japan for market segmentation’, Journal of Hospitality Marketing & Management, Vol 21, No 2, pp 132–154.
42.
VuJ.C. (2006), ‘Effect of demand volume on forecasting accuracy’, Tourism Economics, Vol 12, No 2, pp 263–276.
43.
WangC.-H.HsuL-.C. (2008), ‘Constructing and applying an improved fuzzy time series model: Taking the tourism industry for example’, Expert Systems with Applications, Vol 34, No 4, pp 2732–2738.
44.
ZhongL.DengJ.XiangB. (2008), ‘Tourism development and the tourism area life-cycle model: A case study of Zhangjiajie national forest park, China’, Tourism Management, Vol 29, No 5, pp 841–856.
45.
ZouganeliS.TrihasN.AntonakiM.KladouS. (2012), ‘Aspects of sustainability in the destination branding process: A bottom-up approach’, Journal of Hospitality Marketing & Management, Vol 21, No 7, pp 739–757.
