Spatial analysis of the relationship between tourist attractions and tourist flows in Turkey

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KARAGÖZ D., Aktaş S., Kantar Y. M.

European Journal of Tourism Research, vol.31, 2022 (ESCI) identifier

  • Publication Type: Article / Article
  • Volume: 31
  • Publication Date: 2022
  • Doi Number: 10.54055/ejtr.v31i.2745
  • Journal Name: European Journal of Tourism Research
  • Journal Indexes: Emerging Sources Citation Index (ESCI), Scopus, CAB Abstracts, Hospitality & Tourism Complete, Hospitality & Tourism Index, Veterinary Science Database, Directory of Open Access Journals
  • Keywords: Attractions, Exploratory spatial data analysis, Spatial regression analysis, Spectral clustering, Tourist flows
  • Anadolu University Affiliated: Yes


© 2022 The Author(s).This study is intended to examine the relationship between tourist attractions (natural, cultural and historical) and tourist flows. In the study, secondary data for six provinces and 110 sub-provinces in the Southwestern Anatolia region of Turkey, visited by local and foreign tourists, are used. Four of these provinces have a coastline to the Aegean Sea and the Mediterranean. In this context, overnight data of tourists for 110 sub-provinces and the printed and online materials and overnight data of tourists are used to identify attractions. In this study, mapping analysis, local and global Moran’s I, the classical regression and spatial regression models are benefited. Primarily, the spillover of attractions through maps and the distribution of tourist flows are presented in the study. When the relationship between tourist attractions and tourist flows are examined, the results of our analyses show that the Global Moran’s I value is 0.25 and that those 110 sub-provinces could be similar in terms of tourist flow. It was determined whether there is a global clustering based on Global Moran’s I value, and then the similar clusters, that is, similar sub-provinces in terms of tourist flow, were determined using the spectral clustering method. In addition, the neighborhood relationship and neighborhood interactions in terms of tourist flow are determined using local indicators of spatial analysis (LISA) alongside the Spectral Clustering Method. Finally, in the study field, the relationship between cultural, historical, and natural tourist attractions and tourist flow is explained using the classical regression model and the spatial regression model. The spatial-based models, especially the SEM, improve the model performance compared to the corresponding OLS model. In conclusion, it is found that there is a positive correlation between tourist flows and natural and historical attractions of the region, but a negative relationship between tourist flows and cultural attractions. Destination management implications are discussed.