The research of “Tourism spatial text destinations network “in Golestan province (Post-Structural Reading)

Post-structural theory and text based approach of tourism are produced, and provided as a spatial text strategic communication between different elements and attention to cooperation of beneficiary in planning and development process are accounted as essential part of tourism development. This communication is formed around of tourism spatial text destinations network. Based on this theory perspective we can put tourism spatial text associations in regional Intertextual deconstruction to this method, we can proceed to identify and provide tourist’s needs and requirements, providefacilities according to the needs and inform tourists about choosing the appropriate path. When using network theories which have complex processes, reading destinations network in tourism spatial text would be enabled and through this we can try to read tourism space text in regional scale to make tourism stable.
Regard to this, in order to reading tourism spatial text in Golestan province with emphasis on drawing tourism spatial text destinations networkby descriptive- analysis research method have been done, tourism spatial text destinations network in Golestan is drawn in different dimension by utilizing network theory, Dolphin method and snowball technique and by asking experts in national and regional scale. The results show that the density value of the tourism destinations network in this research is 0.437. With relatively close attention to mean, we can suppose that this network has lower density. The resulted clustering coefficient is 0.678 which is rather high and shows high trend of destinations toorganizecluster in the network. This means the establishment of relationship with common trust (confidence)between tourism destinations. In addition, four space text destinations of Golestan including Gonbad-e Qabus Tower with 78.002%, IncheboronAqqalamarket with 44.446%, Gorgan Nahar Khoran forest with 23.549% and Bander Gaz Port waterfront with 18.882% having the most intermediate centrality can be the destinations through them the tourists can pass to choose the next tourism destination and based on 100% centrality they have the highest proximity centrality. This means that these four destinations have a shorter distance compared to each of the nearbydestinations and have more centrality.