Abstract
Despite the importance of tourism as a leading industry in the development of a country’s economy, there is a lack of criteria and methodologies for the detection, promotion, and governance of local tourism systems. We propose a quantitative approach for the detection of local tourism systems the size of which is optimal with respect to geographical, economic, and demographical criteria: we formulate the problem as an optimisation problem and we solve it by a metaheuristic approach; then we compare the obtained results with standard clustering approaches and with an exact optimisation solver. Results show that our approach requires low computational times to provide results that are better than other clustering techniques and than the current approach used by local authorities.
Similar content being viewed by others
References
Andria J, di Tollo G (2015) Clustering local tourism systems by threshold acceptance. In: Mora AM, Squillero G (eds) Applications of evolutionary computation. Lecture notes in computer science, vol 9028. Springer International Publishing, New York, pp 629–640
Bezdek JC, Pal NR (1998) Some new indexes of cluster validity. IEEE Trans Syst Man Cybern 3:301–315
Brandenburger A, Nalebuff B (1996) Co-opetition. Doubleday, New York
Brenner T, Gildner A (2006) The long-term implications of local industrial clusters. Eur Plann Stud 14(9):1315–1328
Butler R (1980) The concept of a tourist area cycle of evolution: implications for management of resources. Can Geogr/Le Géogr Can 24(1):5–12
Butler R (ed) (2006) The tourism area life cycle volume 1: applications and modifications. Aspects of tourism. Channel View, Bristol
Coccossis H, Mexa A, Collovini A, Parpairis A, Konstandoglou M, van der Straaten J, van der Borg J, Trumbic I (2001) Defining, measuring and evaluating carrying capacity in European tourism destinations. Technical report, Laboratory of Environmental Planning, The University of Aegean
Cracolici MF, Nijkamp P (2009) The attractiveness and competitiveness of tourist destinations: a study of southern Italian regions. Tour Manag 30(3):336–344
Davies DL, Bouldin DW (1979) A cluster separation measure. IEEE Trans Pattern Anal Mach Intell 2:224–227
Duarte A, Garcia C, Giannarakis G, Limo S, Polydoropoulou A, Litinas N (2010) New approaches in transportation planning: happiness and transport economics. NETNOMICS Econ Res Electron Netw 11(1):5–32
Dueck G, Scheuer T (1990) Threshold accepting: a general purpose optimization algorithm appearing superior to simulated annealing. J Comput Phys 90(1):161–175
Dunn J (1974) Well separated clusters and optimal fuzzy partitions. J Cybern 4:95–104
Enright MJ (2003) Regional clusters: what we know and what we should know. In: Broecker Johannes, Dohse Dirk, Soltwedel Ruediger (eds) Innovation clusters and interregional competition., Advances in spatial scienceSpringer, Berlin, pp 99–129
Gilli M, Maringer D, Schumann E (2011) Numerical methods and optimization in finance. Academic Press, London
Gilli M, Schumann E (2011) Optimal enough? J Heuristics 17(4):373–387
Gilli M, Winker P (2009) Heuristic optimization methods in econometrics. In: Belsley DA, Kontoghiorghes E (eds) Handbook of computational econometrics. Wiley, New York
Gormsen E (1997) The impact of tourism on coastal areas. GeoJournal 42(1):39–54
Gusfield D (2002) Partition-distance: a problem and class of perfect graphs arising in clustering. Inf Process Lett 82(3):159–164
Hawkins DE, Mann S (2007) The world bank’s role in tourism development. Ann Tour Res 34(2):348–363
Hwang YH, Fesenmaier DR (2003) Multidestination pleasure travel patterns: empirical evidence from the American travel survey. J Travel Res 42(2):166–171
ISTAT (1998–2002) Capacità degli esercizi ricettivi e movimento dei clienti negli esercizi ricettivi
ISTAT (2008) Primo Repertorio Statistico dei Comuni della Sicilia. Sistema Statistico Nazionale
Jackson J, Murphy P (2006) Clusters in regional tourism an Australian case. Ann Tour Res 33(4):1018–1035
Jin X, Weber K, Bauer T (2012) Impact of clusters on exhibition destination attractiveness: evidence from mainland China. Tour Manag 33(6):1429–1439
Kim H, Park H (2007) Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis. Bioinformatics 23(12):1495–1502
Leask A (2010) Progress in visitor attraction research: towards more effective management. Tour Manag 31(2):155–166
Lee SH, Choi JY, Yoo SH, Oh YG (2013) Evaluating spatial centrality for integrated tourism management in rural areas using GIS and network analysis. Tour Manag 34:14–24
Macbeth J (2005) Towards an ethics platform for tourism. Ann Tour Res 32(4):962–984
Martello S, Toth P (1990) Knapsack problems: algorithms and computer interpretations. Wiley-Interscience, New York
Middleton V, Hawkins R (1998) Sustainable tourism: a marketing perspective. Butterworth-Heinemann, Oxford
Moscato P, Fontanari JF (1990) Stochastic versus deterministic update in simulated annealing. Phys Lett A 146(4):204–208
Pearce DG, Tan R, Schott C (2007) Distribution channels in international markets: a comparative analysis of the distribution of New Zealand tourism in Australia, Great Britain and the USA. Curr Issues Tour 10(1):33–60
Pelleg D, Moore A (2000) X-means: extending k-means with efficient estimation of the number of clusters. In: Proceedings of the 17th international conf. on machine learning. Morgan Kaufmann, Kaufmann, pp 727–734
Porter ME (1998) On competition. Harvard Business School Publishing, Boston
Ray S, Turi RH (1999) Determination of number of clusters in k-means clustering and application in colour image segmentation. In: Proceedings of the 4th international conference on advances in pattern recognition and digital techniques, pp 137–143
Regione Siciliana (2010) Criteri e modalità per il riconoscimento dei distretti turistici. Gazzetta Ufficiale della Regione Siciliana
Ritchie JRB, Crouch GI (2003) The competitive destination: a sustainable tourism perspective. CABI Publishing Series. CABI Pub., Cambridge
Rousseeuw PJ (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20:53–65
Ryan C (1991) Recreational tourism: a social science perspective. Routledge, London
Ryan (2002). The tourist experience. Continuum, London
Saporta G, Youness G (2002) Comparing two partitions: some proposals and experiments. In: Hrdle W, Rnz B (eds) Compstat. Physica-Verlag, HD, Heidelberg, pp 243–248
Scott N, Baggio R, Cooper C (2008) Network analysis and tourism: from theory to practice. In: Aspects of tourism. Channel View Publications, Bristol
Shih Hsin-Yu (2006) Network characteristics of drive tourism destinations: an application of network analysis in tourism. Tour Manag 27(5):1029–1039
Sim IB (2002) A study on the establishment of tourism zone and development strategic. Korean Acad Soc Cult Tour 4:199–223
Stansfield C (1978) Atlantic city and the resort cycle background to the legalization of gambling. Ann Tour Res 5(2):238–251
Yabuta M, Scott N (2010) A theoretical framework of the dynamic property of the tourism destination network. Technical report, the University of Queensland
Yabuta M, Scott N (2011) Dynamic properties of a tourism destination network. Tour Anal 16(4):493–498
Yang Y (2012) Agglomeration density and tourism development in china: an empirical research based on dynamic panel data model. Tour Manag 33(6):1347–1359
Acknowledgments
Authors want to thank Manfred Gilli and Gerda Cabej for insightful comments and remarks on a former draft of the paper.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Andria, J., di Tollo, G. & Pesenti, R. Detection of local tourism systems by threshold accepting. Comput Manag Sci 12, 559–575 (2015). https://doi.org/10.1007/s10287-015-0238-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10287-015-0238-x