地理科学进展

地理科学进展 ›› 2012, Vol. 31 ›› Issue (4): 518-526.doi: 10.11820/dlkxjz.2012.04.016

• 旅游与文化地理 • 上一篇    下一篇

中国入境游客多城市旅游空间网络结构

王永明1, 马耀峰2, 王美霞1   

  1. 1. 吉首大学商学院,吉首 416000;
    2. 陕西师范大学旅游与环境学院,西安 710062
  • 收稿日期:2011-07-01 修回日期:2011-10-01 出版日期:2012-04-25 发布日期:2012-04-25
  • 基金资助:
    国家自然科学基金项目(40771058,40901077);湖南省高校科技创新团队支持计划项目(湘财教[200870]);湖南省哲学社会科学基金项目（11JL09）.

Network Structure of Multicity Inbound Tourists to China

WANG Yongming1, MAYaofeng2, WANG Meixia1   

  1. 1. Business School of Jishou University, Jishou 416000, China;
    2. College of Tourism and Environment, Shaanxi Normal University, Xi'an 710062, China
  • Received:2011-07-01 Revised:2011-10-01 Online:2012-04-25 Published:2012-04-25

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110 | 15

摘要： 借鉴社会网络分析技术和方法,综合运用GIS 空间分析和数理统计等方法,分析了中国入境游客多城市旅游空间网络结构特征.结果显示：①入境游客多城市旅游空间网络共有46 个节点,节点分布呈现“东南密、西北疏”的空间格局;②旅游网络中每个城市节点平均与2.96 个节点具有旅游流联系,节点体系共分4 个等级,等级越高,节点数量越小,传统热点城市和区域中心城市在中心性和结构洞指标方面表现好,在旅游网络中占有重要地位;③整体旅游网络密度很低,网络功能发育不完善,且均在较大的不均衡性,网络核心节点对边缘节点的入境旅游辐射效应很弱;④旅游网络共存在9 个派系,区位交通、旅游资源、经济联系是派系形成的重要驱动力,城市空间距离对派系形成影响作用极小.基于旅游流联系的角度,构建以城市为节点的大尺度入境旅游空间网络结构并进行深入分析,具有理论意义和实践指导意义.

关键词: 多城市旅游, 旅游网络, 入境游客, 社会网络分析, 中国

Abstract: This study examines the network structure of multicity inbound tourism in China in 2008 with the methods of social network analysis, GIS spatial analysis and mathematical analysis. More precisely, this investigation acquires analyses of the structural configuration of each city, network and cohesion within a country destination by measuring the indicators of network analysis which include node indicators of centrality and structural holes, network indicators of density, centralization and core-periphery, and cohesion indicator of clique. The results show that there are 46 nodes in the tourism network. Nodes distribution shows the spatial pattern of "dense southeast and sparse northwest". On average, each city node in the tourism network has tourists flow-related connections with 2.96 other nodes. And the node system is divided into four levels, the higher the rank, the smaller the number of nodes is. The major traditional tourism cities and regional central cities have good performance, and take important positions. The density of tourism network is low and unbalanced. There are nine cliques in the tourism network, which were driven by location transportation, tourism resources and economic links, but the effect of urban space distance is the minimal.

Key words: China, inbound tourists, multicity tourism, social network analysis, tourism network