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| RESEARCH ON THE SPATIAL NETWORK CHARACTERISTICS OF TRAVEL ITINERARY IN YUNNAN PROVINCE |
| SUN Yong, SHI Chun-yun, TANG Wen-wen, LIU Jing |
| College of Urban and Environmental Sciences, Jiangsu Normal University, Xuzhou 221116, China |
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Abstract After 30 years of development, the tourism industry in Yunnan province, is in a critical period of the construction of tourism economy, and is experiencing the rapid development process. In order to promote the sustainable development of regional tourism industry in Yunnan province, this paper discusses the characteristics of spatial network structure for the travel itinerary in Yunnan province, based on the perspective of social network analysis and two aspects of whole network and ego-network. Two different types of travel itineraries data are used in our research, which included travel quotations from national top 100 travel agencies in china and self-help tourists' diaries on the Mafengwo and Tuniu website. The results show that the network of the itinerary in Yunnan province is unbalanced and its density is low. In addition, the characteristics of core-periphery structure exist obviously in the network. As the capital of Yunnan province, Kunming city occupies the absolute core position in the network. Dali, Lijiang, Xishuangbanna, Diqing and Baoshan cities have competitive advantages in the network. The rest of the destination cities just have low status in the network of Yunnan province. Meanwhile, the spatial distribution characteristics of the travel itinerary network in Yunnan province reveal that destination cities are closely connected in the western and the northern region while destination cities contact less in the east and the south. Therefore this spatial itinerary distribution leads to the concentration to the popular destination nodes. Finally, each nodes of travel itinerary can form an independent closed loop based on the relationships among the ego-network in Yunnan province, which is convenient for tourists to select the appropriate gateway destination, transit destination and departure destination according to the ego-networks' characteristics and the nodes' geographic location.
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Received: 10 December 2014
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2016, Vol. 31 
