地理研究 ›› 2020, Vol. 39 ›› Issue (6): 1370-1385.doi: 10.11821/dlyj020190654

• 研究论文 • 上一篇    下一篇

粤港澳大湾区旅游经济联系的空间结构及协同合作模式

吴志才1,2,3, 张凌媛1,2, 黄诗卉3,4()   

  1. 1. 华南理工大学经济与贸易学院,广州 510006
    2. 华南理工大学旅游与酒店管理学院,广州510006
    3. 华南理工大学广东旅游战略与政策研究中心,广州 510006
    4. 华南理工大学公共管理学院,广州 510006
  • 收稿日期:2019-07-31 修回日期:2019-11-20 出版日期:2020-06-20 发布日期:2020-08-20
  • 通讯作者: 黄诗卉
  • 作者简介:吴志才(1976-),男,江西上饶人,博士,教授,研究方向为区域发展战略与旅游规划、乡村旅游与扶贫。E-mail: zhcwu@scut.edu.cn
  • 基金资助:
    国家社会科学基金项目(18BJY199);中央高校基本科研业务费专项资金(D2181610);中央高校基本科研业务费专项资金(C2190270)

Spatial structure and characteristics of tourism economic connections in Guangdong-Hong Kong-Macao Greater Bay Area

WU Zhicai1,2,3, ZHANG Lingyuan1,2, HUANG Shihui3,4()   

  1. 1. School of Economics and Commerce, South China University of Technology, Guangzhou 510006, China
    2. School of Tourism and Hotel Management, South China University of Technology, Guangzhou 510006, China
    3. Guangdong Tourism Strategy and Policy Research Center, South China University of Technology, Guangzhou 510006, China
    4. School of Public Administration, South China University of Technology, Guangzhou 510006, China
  • Received:2019-07-31 Revised:2019-11-20 Online:2020-06-20 Published:2020-08-20
  • Contact: HUANG Shihui

摘要:

以2008年、2012年和2016年为时间截面,基于修正引力模型和社会网络理论,借助软件Ucinet 6.0分析粤港澳大湾区11个城市旅游经济联系的空间结构及网络特征,并探讨粤港澳大湾区旅游合作发展的协同模式及对策建议。结果表明:粤港澳大湾区旅游经济联系强度和总量快速增长,城市间联系逐渐密切,向均衡方向发展;在流向上,广、港、澳为资源要素流出的城市、其余城市担任接收角色,整体网络扩散效应大于联动效应;网络密度与节点中心性整体上升,存在明显的核心-边缘结构,可划分为广州-佛山、香港-深圳-东莞、珠海-中山-澳门3个不同的凝聚子群。基于“分层网络协同发展”思路,提出“旅游中心城市-旅游城市合作圈-全域旅游目的地网络”的路径以实现粤港澳大湾区旅游空间的合作升级。

关键词: 旅游经济联系, 空间结构, 社会网络分析, 协同合作模式, 粤港澳大湾区

Abstract:

In this study, we use the modified gravity model and social network theory to analyze the spatial structure and characteristics of the tourism economic connections among cities in the Guangdong-Hong Kong-Macao Greater Bay Area from 2008 to 2016 and propose a new cooperation model and some strategies to enhance the tourism economy in the region. The results show that first, the number of tourism economic links in the Guangdong-Hong Kong-Macao Greater Bay Area grew rapidly, and the links between particular cities strengthened, especially those between Guangzhou and Foshan, Macao and Zhuhai, and Hong Kong and Shenzhen. Second, the node centrality of the overall tourism economic network in the study area increased, indicating that the tourism economic network among the 11 cities in the Greater Bay Area gradually shifted from a multi-core structure toward a network structure. In terms of the direction of flow of tourism economy, Guangdong, Hong Kong, and Macao are outflow cities of tourism resources, while the other eight cities are inflow cities, indicating that the diffusion effect exceeds the linkage effect over the entire network. Third, the tourism economic links in the Greater Bay Area increased in strength, resulting in increased network density. There are obvious hierarchical differences between the core and edge areas in the network structure, which can be divided into three agglomerating subgroups: Guangzhou-Foshan, Hong Kong-Shenzhen-Dongguan, and Zhuhai-Zhongshan-Macao. Finally, based on the concept of "collaborative hierarchical network development", we propose a "tourism city center-tourism city cooperation circle-tourism global destination network" path to enhance tourism economic cooperation in the study area. In practice, we need to recognize the key roles that Guangzhou, Hong Kong, Macao, and other key cities played in the tourism-based economy, and focus on the transformation of the population, capital, and flow factors of other cities in relation to tourism development to strengthen the tourism foundations in this emerging region. Tourism can also result in ecological, economic, and environmental costs, and thus it is also necessary to focus on whether the policy of subsidizing tourism has a positive overall impact on the economic growth of the Greater Bay Area and its surrounding cities. How to maintain a positive relationship between economic growth and tourism should also be the focus of further research.

Key words: tourism economic connections, spatial structure, social network analysis, cooperative mode, Guangdong-Hong Kong-Macao Greater Bay Area