Abstract
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.
Highlights
Turkey is a popular tourist destination across the globe with many tourist attractions including transcontinental location, variety of historical and heritage sites, hot spas and seaside resorts along Aegean and Mediterranean coasts
exploratory spatial data analysis (ESDA) measures the spatial autocorrelation in two main statistical categories: first, universal map statistics that focus on processing all the cases for an attribute; second, local or focused map statistics that process spatially defined subsets of data by looking for evidence of local attributes of mapped data
The analysis of data is started with the choropleth distribution maps of inbound and domestic tourist arrival for the year 2016 in the provinces of Turkey (Figure 1-2)
Summary
Turkey is a popular tourist destination across the globe with many tourist attractions including transcontinental location, variety of historical and heritage sites, hot spas and seaside resorts along Aegean and Mediterranean coasts. The steady growth of tourism industry has strong direct and indirect impacts on country’s economy. The total contribution of tourism and travel (including direct, indirect and induced contribution) to country’s GDP in 2016 was $88 billion, that was 12.5% of GDP Travel and tourism sector generated 2.1 million jobs, that makes 8.1% of total employments in the country (WTTC, 2017). The development of tourism industry, an important source of exchange product, in Turkey has gained momentum since 1980s and a rapid growth in number of tourists, tourism receipts and accommodation facilities has been observed during last three decades. Turkish government adopted incentive policy in tourism sector by making Tourism Encouragement Law of 1982 to achieve exportled industrialization by accelerating the mass tourism in the country. Tourism is acknowledged as an economic multiplier and highly supported by local administrations and communities in the country to derive economic benefits due to which tourism is given high priority in regional planning (Göymen, 2000; Tosun and Jenkins, 1996)
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