The spatial distribution and relationship of tourist flow in Turkey
Keywords:Tourism distribution, Exploratory spatial data analysis, Spatial Autocorrelation, Hotspots, Turkey.
Tourism is considered as back bone of Turkish economy with its greater share in GDP than many other economic sectors of the country. Present research is sought to analyse the spatial association and distribution of foreign and domestic tourist inflows in 81 provinces of Turkey through exploratory spatial data analysis (ESDA) technique. ESDA is one of very useful Geographic Information System (GIS) based spatial statistics techniques to analyse spatial patterns, identification of hotspots and visualization of spatial association among variables. Analysis of global Moran’s I statistics results into a positive value that indicates presence of spatial autocorrelation among neighbouring provinces with high(low) number of both foreign and domestic tourist arrivals during years 2000-2016. This depicts positive and negative autocorrelation of places with geographical similarities and dissimilarities over the space. Moreover, Moran’s significance maps help indicating hotspot areas of high and low tourist arrivals where high clusters are found in western coastal areas and lower clusters are associated to eastern inland areas. Furthermore, Moran’s scatterplot analysis highlights the regional disparities within the country in terms of tourism development. This polarized spatial pattern of tourism is associated with differences in economic development and resource allocation between western and eastern provinces. This study is significant for policy makers by providing insights into spatial distribution of tourist flows for better resource allocation and management of hotspot areas. Besides, it also helps private sector and tour operators for economic utilization of tourist clusters and hotspots.
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Copyright (c) 2018 Creative Commons Attribution 4.0 International (CC BY 4.0)
This work is licensed under a Creative Commons Attribution 4.0 International License.