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Current Issues in Method and Practice

Cluster-mapping procedure for tourism regions based on geostatistics and fuzzy clustering: example of Polish districts

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Pages 2365-2385 | Received 11 Apr 2017, Accepted 16 Apr 2018, Published online: 30 Apr 2018

References

  • Anselin, L. (1995). Local indicators of spatial association – LISA. Geographical Analysis, 27, 93–115.
  • Arefiev, N., Terleev, V., & Badenko, V. (2015). GIS-based Fuzzy Method for urban planning. Procedia Engineering, 117, 39–44.
  • Beritelli, P., Reinhold, S., Laesser, C., & Bieger, T. (2015). The St. Gallen Model for destination management. St. Gallen: Institute for Systemic Management and Public Governance, University of St. Gallen.
  • Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers & GeoSciences 10, 191–203.
  • Buyst, E., & Helgers, R. (2013). The ripple effect and the linguistic border in Belgium: A country divided? Vives Discussion Paper, 39, 1–33. Retrieved from https://lirias.kuleuven.be/bitstream/123456789/403608/1/VIVES+dp39.pdf
  • Calinski, R. B., & Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics, 3, 1–37.
  • Campello, R. J. G. B., & Hruschka, E. R. (2006). A fuzzy extension of the silhouette width criterion for cluster analysis. Fuzzy Sets and Systems, 157(21), 2858–2875.
  • Capone, F. (2004, August). Regional competitiveness in tourism local systems. Paper presented at 44th European Congress of the European Regional Science Association, Regions and Fiscal Federalism, Porto.
  • Capone, F., & Boix, R. (2008). Sources of growth and competitiveness of local tourist production systems: An application to Italy (1991-2001). The Annals of Regional Science, 42(1), 209–224.
  • Carreras, C. (1995). Mega-events: Local strategies and global tourists attractions. In A. Montanari & W. A.W. (Eds.), European tourism: Regions, spaces and restructuring (pp. 193–205). Chichester: Wiley.
  • Carroll, C. M., Reid, N., & Smith, B. W. (2008). Location quotients versus spatial autocorrelation in identifying potential cluster regions. The Annals of Regional Science, 42, 449–463. doi: 10.1007/s00168-007-0163-1
  • Chen, Y. G. (2012). On the four types of weight functions for Spatial Contiguity Matrix. Letters in Spatial and Resource Sciences, 5(2), 65–72. doi: 10.1007/s12076-011-0076-6
  • Chhetri, P. (2015). A GIS methodology for modelling hiking experiences in the Grampians National Park, Australia. Tourism Geographies, 17(5), 795–814.
  • Chow, T. E., Dede-Bamfo, N., & Dahal, K. R. (2016). Geographic disparity of positional errors and matching rate of residential addresses among geocoding solutions. Annals of GIS, 22(1), 29–42. doi: 10.1080/19475683.2015.1085437
  • Chu, H.-J., Liau, C. J., Lin, C.-H., & Su, B.-S. (2012). Integration of fuzzy cluster analysis and kernel density estimation for tracking typhoon trajectories in the Taiwan region. Expert Systems with Applications, 39, 9451–9457.
  • Chunchun, H. U., Lingkui, M., & Wenzhong, S. (2008). Fuzzy clustering validity for spatial data. Geo-spatial Information Science, 11, 191–196.
  • Coppi, R., D’Urso, P., & Giordani, P. (2010). A Fuzzy Clustering Model for multivariate spatial time series. Journal of Classification, 27, 54–88. doi: 10.1007/s00357-010-9043-y
  • CSO. (2015). Polish Central Statistical Office. Centrum Informatyki Statystycznej, REGON registry, WOiN-01-7041-277/2015.
  • CSO. (2016). Polish Central Statistical Office. LOCAL DATA BANK. Retrieved from https://bdl.stat.gov.pl/BDL
  • de la Mata, T, & Llano-Verduras, C. (2012). Spatial pattern and domestic tourism: An econometric analysis using inter-regional monetary flows by type of journey. Papers in Regional Science, 91(2), 437–470.
  • Di Martino, F., Loia, V., & Sessa, S. (2008). Extended fuzzy C-means clustering algorithm for hotspot events in spatial analysis. International Journal of Hybrid Intelligent Systems, 5(1), 31–44.
  • Dubois, D., & Prade, H. (1997). The three semantics of fuzzy sets. Fuzzy Sets and Systems, 90, 141–150.
  • D'Urso, P., & Maharaj, E. A. (2009). Autocorrelation-based fuzzy clustering of time series. Fuzzy Sets and Systems, 160, 3565–3589.
  • Everitt, B. S., Landau, S., & Leese, M. (2001). Cluster analysis (4th ed.). London: Arnold Press.
  • Feng, Z., & Flowerdew, R. (1998). Fuzzy geodemographics: A contribution from fuzzy clustering methods. In Innovations in GIS (Vol. 5, pp. 119–127). London: Taylor & Francis.
  • Fingleton, B., & López-Bazo, E. (2006). Empirical growth models with spatial effects. Papers in Regional Science, 85(2), 177–198.
  • Gordon, A. D. (1996). A survey of constrained classification. Computational Statistics and Data Analysis, 21, 17–29.
  • Gordon, I., & Goodall, B. (2000). Localities and tourism. Tourism Geographies, 2(3), 290–311.
  • Griffith, D. A. (1996). Some guidelines for specifying the geographic weights matrix contained in spatial statistical models. In S. L. Arlinghaus & D. A. Griffith (Eds.), Practical handbook of spatial statistics (pp. 65–82). Boca Raton: CRC Press.
  • GUS. (2015). Analiza walorów turystycznych powiatów i ich bezpośredniego otoczenia na podstawie danych statystycznych m.in. z zakresu bazy noclegowej, kultury i dziedzictwa narodowego oraz przyrodniczych obszarów chronionych [Analysis of tourist attractiveness of districts and their proximate neighborhood on the basis of statistical data on, among others, accommodation, culture and national heritage, and natural protected areas]. Warsaw: Centrum Badań i Edukacji Statystycznej GUS.
  • Haining, R. P. (2010). The nature of georeferenced data. In M. M. Fischer & A. Getis (Eds.), Handbook of applied spatial analysis. Software tools, methods and applications (pp. 197–217). Berlin: Springer.
  • Heiser, W. J., & Groenen, P. J. F. (1997). Cluster differences scaling with a within-clusters loss component and a fuzzy successive approximation strategy to avoid local minima. Psychometrika, 62, 63–83.
  • Herrera, M., Ruiz, M., & Mur, J. (2013). Detecting dependence between spatial processes. Spatial Economic Analysis, 8(4), 469–497.
  • Hu, T., & Sung, S. Y. (2006). A Hybrid EM Approach to spatial clustering. Computational Statistics and Data Analysis, 50, 1188–1205.
  • Hwang, H., De Sarbo, W. S., & Takane, Y. (2007). Fuzzy clusterwise generalized structured component analysis. Psychometrika, 72, 181–198.
  • Izakian, H., & Abraham, A. (2011). Fuzzy C-means and fuzzy swarm for fuzzy clustering problem. Expert Systems with Applications, 38(3), 1835–1838.
  • Jackson, J, & Murphy, P. (2002). Tourism destinations as clusters: Analytical experiences from the New World. Tourism and Hospitality Research, 4(1), 36–52.
  • Jajuga, K. (1984). Zbiory rozmyte w zagadnieniu klasyfikacji [Fuzzy sets in casiffication]. Przegląd Statystyczny, 3-4, 237–250.
  • Kontos, D., & Megalooikonomou, V. (2005). Fast and effective characterization for classification and similarity searches of 2D and 3D spatial region data. Pattern Recognition, 38, 1831–1846.
  • Kriegel, H.-P., Kröger, P., Sander, J., & Zimek, A. (2011). Density-based clustering. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 1(May/June), 231–240.
  • Kurtener, D., & Badenko, V. (2001). GIS fuzzy algorithm for evaluation of attribute data quality. Geomatics Info Magazine, 15, 76–79.
  • Lawson, A. B., Simeon, S., Kulldorff, M., Biggeri, A., & Magnani, C. (2007). Line and point cluster models for spatial health data. Computational Statistics and Data Analysis, 51, 6027–6043.
  • LeSage, J. P., & Pace, R. K. (2010). The Biggest Myth in Spatial Econometrics, SSRN, 1-42. Retrieved from http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1725503
  • Liew, A. W. C., & Yan, H. (2003). An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation. IEEE Transactions on Medical Imaging, 22, 1063–1075.
  • Lloyd, C. D. (2010). Spatial data analysis: An introduction for GIS users. Oxford: University Press.
  • Longley, P. A., Goodchild, M. F., Maguire, D. J., & Rhind, D. W. (2011). Geographic information systems and science (3rd ed.). Hoboken, NJ: Wiley.
  • Maimon, O., & Rokach, L. (2010). Data mining and knowledge discovery handbook (2nd ed.). New York: Springer.
  • Majewska, J. (2015). Inter-regional agglomeration effects in tourism in Poland. Tourism Geographies, 17(3), 408–436.
  • Majewska, J. (2017). GPS-based measurement of geographic spillovers in tourism – example of Polish districts. Tourism Geographies, 19(4), 612–643.
  • Majewska, J., Napierała, T., & Adamiak, M. (2016). Wykorzystanie nowych technologii i informacji do opisu przestrzeni turystycznej [Using new information and communication technologies for the description of tourism space]. Folia Turistica, 41, 309–339.
  • Majewska, J., & Truskolaski, S. (2016). The use of geostatistical information to measure the spatial agglomeration in tourism in the border counties. In T. Rynarzewski & M. Szymczak (Eds.), Changes and challenges in the modern world economy. Recent advances in research on international economics & business (pp. 294–310). Poznań: Poznań University of Economics and Business.
  • Majewska, J., & Truskolaski, S. (2017). Spatial concentration of economic activity and competitiveness of Central European regions. Przedsiębiorczość Międzynarodowa, 3(1), 47–64.
  • Marrocu, E., & Pacci, R. (2013). Different tourists to different destinations. Evidence from spatial interaction models. Tourism Management, 39, 71–83.
  • Mason, G. A., & Jacobson, R. D. (2007). Fuzzy geographically weighted clustering. In Proceedings of the 9th international conference on geocomputation (pp. 1–7). Maynooth: National Centre for Geocomputation, National University of Ireland.
  • McArdle, G., Demšar, U., van der Spek, S., & McLoone, S. (2014). Classifying pedestrian movement behaviour from GPS trajectories using visualization and clustering. Annals of GIS, 20(2), 85–98.
  • McBratney, A. B., & Moore, A. W. (1985). Application of fuzzy sets to climatic classification. Agricultural and Forest Meteorology, 35, 165–185.
  • McKercher, B., Shoval, N., Ng, E., & Birenboim, A. (2012). First and repeat visitor behaviour: GPS tracking and GIS analysis in Hong Kong. Tourism Geographies, 14(1), 147–161.
  • Milligan, G. W., & Cooper, M. C. (1985). An examination of procedures for determining the number of clusters in a data set. Psychometrika, 50(2), 159–179.
  • Minth, N. V., & Son, L. H. (2015). Fuzzy approaches to context variables in fuzzy geographically weighted clustering. Computer Science & Information Technology, 5, 21–30.
  • Páez, A., & Scott, D. M. (2004). Spatial statistics for urban analysis: A review of techniques with examples. GeoJournal, 61, 53–67.
  • Patacchini, E., & Rice, P. (2007). Geography and economic performance: Exploratory spatial data analysis for Great Britain. Regional Studies, 41(4), 489–508.
  • Permuter, H., Francos, J., & Jermyn, I. (2006). A study of Gaussian mixture models of color and texture features for image classification and segmentation. Pattern Recognition, 39, 695–706.
  • Pham, D. L. (2001). Spatial models for Fuzzy clustering. Computer Vision and Image Understanding, 84, 285–297.
  • Pilevar, A. H., & Sukumar, M. (2005). GCHL: A grid-clustering algorithm for high-dimensional very large spatial data bases. Pattern Recognition Letters, 26, 999–1010.
  • Podani, J. (1996). Explanatory variables in classifications and the detection of the optimum number of clusters. In C. Hayashi, N. Ohsumi, K. Yajima, Y. Tanaka, H. Bock, & Y. Baba (Eds.), Data science, classification and related methods (pp. 125–132). Tokyo: Springer-Verlag.
  • Porter, M. (2003). The economic performance of regions. Regional Studies, 37, 549–578.
  • Prager, J.-C., & Thisse, J.-F. (2012). Economic geography and the unequal development of regions. London: Routledge.
  • Prayag, G., Landré, M., & Ryan, C. (2012). Restaurant location in Hamilton, New Zealand: Clustering patterns from 1996 to 2008. International Journal of Contemporary Hospitality Management, 24(3), 430–450.
  • Rushton, G., Armstrong, M. P., Gittler, J., Greene, B. R., Pavik, C. E., West, M. M., & Zimmerman, D. L. (Eds.). 2010. Geocoding health data: The Use of geographic codes in cancer prevention and control, research and practice. Boca Raton, FL: CRC Press.
  • Schabenberger, O., & Gotway, C. A. (2005). Statistical methods for spatial data analysis: Texts in statistical science. Boca Raton, FL: Chapman & Hall/CRC.
  • Shrivastava, P., & Gupta, H. (2012). A review of density-based clustering in spatial data. International Journal of Advanced Computer Research, 2(3/5), 210–213.
  • Son, L. H. (2014). Enhancing clustering quality of geo-demographic analysis using context fuzzy clustering type-2 and particle swarm optimization. Applied Soft Computing, 22, 566–584.
  • Son, L. H. (2015a). DPFCM: A novel distributed picture fuzzy clustering method on picture fuzzy sets. Expert Systems with Applications, 42, 51–66.
  • Son, L. H. (2015b). A novel Kernel fuzzy clustering algorithm for geo-demographic analysis. Information Sciences, 317, 202–223.
  • Son, L. H., Cuong, B. C., Lanzi, P. L., & Thong, N. T. (2012). A novel intuitionistic fuzzy clustering method for geo-demographic analysis. Expert Systems with Applications, 39(10), 9848–9859.
  • Son, L. H., Cuong, B. C., & Long, H. V. (2013). Spatial interaction – modification model and applications to geo-demographic analysis. Knowledge-Based Systems, 49, 152–170.
  • Son, L. H., Lanzi, P. L., Cuong, B. C., & Hung, H. A. (2011). Data mining in GIS: A novel context-based fuzzy geographically weighted clustering algorithm. In Proceedings of the 2011 3rd IEEE international conference on machine learning and computing (ICMLC 2011) (pp. 508–511), Singapore.
  • Son, L. H., Lanzi, P. L., Cuong, B. C., & Hung, H. A. (2012, June). Data mining in GIS: A novel context-based fuzzy geographically weighted clustering algorithm. International Journal of Machine Learning and Computing, 2(3), 235–238.
  • Sørensen, F. (2007). The geographies of social networks and innovation in tourism. Tourism Geographies, 9(1), 22–48.
  • Sugimoto, K. (2011). Analysis of scenic perception and its spatial tendency: Using digital cameras, GPS loggers, and GIS. Procedia - Social and Behavioral Sciences, 21, 43–52.
  • Tiefelsdorf, M. (2003). Misspecifications in interaction model distance decay relations: A spatial structure effect. Journal of Geographical Systems, 5(1), 25–50.
  • Timmins, T. L., Hunter, A. J. S., Cattet, M. R. L., & Stenhouse, G. B. (2013). Developing spatial weight matrices for incorporation into multiple linear regression models: An example using grizzly bear body size and environmental predictor variables. Geographical Analysis, 45(4), 359–379.
  • Wang, W., & Zhang, Y. (2007). On fuzzy cluster validity indices. Fuzzy Sets and Systems, 158(19), 2095–2117.
  • Weidenfeld, A., Butler, R., & Williams, A. M. (2016). Visitor attractions and events locations and linkages. New York, NY: Routledge. Advances in Event Research Series.
  • Widaningrum, D. (2015). A GIS-based approach for catchment area analysis of convenience store. Procedia Computer Science, 72, 511–518.
  • Wijayanto, A. W., & Purwarianti, A. (2014, November 24–27). Improvement of Fuzzy Geographically Weighted Clustering using Particle Swarm Optimization. International Conference on Information Technology Systems and Innovation (ICITSI) 2014, Bandung & Bali.
  • Wijayanto, A. W., Purwarianti, A., & Son, L. H. (2016). Fuzzy geographically weighted clustering using artificial bee colony: An efficient geo-demographic analysis algorithm and applications to the analysis of crime behavior in population. Applied Intelligence, 44(2), 377–398.
  • Worboys, M. F., & Duckham, M. (2004). GIS: A computing perspective (2nd ed.). Boca Raton, FL: CRC Press.
  • Wysocki, F. (2010). Metody taksonomiczne w rozpoznawaniu typów ekonomicznych rolnictwa i obszarów wiejskich [Taxonomic methods in recognizing economic types of agriculture and rural areas]. Poznań: Wyd. Uniwersytetu Przyrodniczego w Poznaniu.
  • Xia, Y., Feng, D., Wang, T., Zhao, R., & Zhang, Y. (2007). Image segmentation by clustering of spatial patterns. Pattern Recognition Letters, 28, 1548–1555.
  • Xie, Z., & Yan, J. (2008). Kernel density estimation of traffic accidents in a network space. Computers, Environment, and Urban Systems, 32(5), 396–406.
  • Yang, Y., & Fik, T. J. (2014). Spatial effects in regional tourism growth. Annals of Tourism Research, 46, 144–162.
  • Yang, Y., & Wong, K. K. F. (2012). A spatial econometric approach to model spillover effects in tourism flows. Journal of Travel Research, 51(6), 768–778.
  • Yang, Y., & Wong, K. K. F. (2013). Spatial distribution of tourist flows to China’s cities. Tourism Geographies, 15(2), 338–363.

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