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京沪入境旅游流网络结构特征分析 被引量:84

Characteristic of Beijing and Shanghai Inbound Tourists Flow Network
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摘要 应用社会网络理论和方法,对京沪入境旅游流网络的整体型态特征和个体结构位置特征进行定量研究。结果显示,由11个城市节点和24条流向关系组成的大型京沪入境旅游流网络密度为0.218,程度中心势为0.400,其中包含了3组不少于3个节点的1-派系和2组不少于5个节点的2-派系;由14个城市节点和46条流向关系组成的小型京沪入境旅游流网络密度为0.253,程度中心势为0.566,其中包含了1组不少于4个节点的1-派系和2组不少于6个节点的2-派系。以上结果说明,京沪入境旅游流网络密度低、中心势高、派系交叉性显著;京沪入境旅游流网络节点层次分化严重,核心节点突出,边缘节点明显;京沪入境旅游流网络中存在明显派系。得到两点启示:国家应采取切实措施引导入境旅游流在国内旅游城市之间合理流动,避免入境旅游流过于集中在某些城市;旅游城市应了解其在全国入境旅游流网络中的位置,一方面应积极地与核心城市建立良好的入境旅游关系,另一方面应争取进入旅游城市组合,联合开发旅游产品,共同开展市场营销。 The holistic and individual structural characteristics of the Beijing and Shanghai inbound tourists flow network was studied using social network analysis. It was found that the large inbound tourists flow network is build up with 11 city - nodes and 24 flow - relations, containing three 1 - clique which is no less than 3 nodes and two 2 - cliques which is no less than 5 nodes. Its density is 0.218 and its centralization is 0. 400. The small inbound tourists network is build up with 14 city - nodes and 46 flow - relations, containing one 1 - clique which is no less than 4 nodes and two 2 - cliques which is no less than 6 nodes. Its density is 0. 253 and its centralization is 0.566. Three conclusions had been obtained : (1) The compact intersection level of the Jing and Hu inbound tourists flow network is lower and unevenly distributed. (2) The nodes composed the network are polarized into central nodes and fringe nodes evidently. (3)There are evident cliques in the Beijing and Shanghai inbound tourists flow network. Two suggestions are put forward: (1)The government should take actions to avoid the inbound tourists flow concentrating in few core cities and give correct guidance. (2) Cities should find out their position in the inbound tourists network and constitute good relationships with those core cities and make an alliance with them.
出处 《地理科学》 CSCD 北大核心 2010年第3期370-376,共7页 Scientia Geographica Sinica
基金 国家自然科学基金项目(40771058)资助
关键词 入境旅游流 旅游流网络 结构特征 社会网络理论 inbound tourists flow tourists flow network structural characteristics social network theory
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