地理研究 ›› 2019, Vol. 38 ›› Issue (4): 937-949.doi: 10.11821/dlyj020180236

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基于大数据分析的城市旅游地网络结构特征及其演化模式——以新浪微博签到数据为例

徐敏(), 黄震方(), 曹芳东, 张郴   

  1. 南京师范大学地理科学学院,南京210023
  • 收稿日期:2018-03-12 修回日期:2018-05-31 出版日期:2019-04-20 发布日期:2019-04-30
  • 作者简介:

    作者简介:徐敏(1985-),女,山西大同人,博士研究生,讲师,主要从事旅游地理与旅游规划研究。 E-mail: xuminnanshi1985@163.com

  • 基金资助:
    国家自然科学基金项目(41771154,41671137,41401144);教育部人文社会科学研究青年基金项目(14YJC790003);国家旅游局青年专家培养计划(TYETP201526)

The network structure of urban tourist destination and its evolution mode based on big data analysis:Taking the data of Sina weibo sign-in as an example

Min XU(), Zhenfang HUANG(), Fangdong CAO, Chen ZHANG   

  1. School of Geographical Science, Nanjing Normal University, Nanjing 210023, China
  • Received:2018-03-12 Revised:2018-05-31 Online:2019-04-20 Published:2019-04-30

摘要:

流动性表征了旅游地域系统要素间的内在关系,深刻影响着旅游地域网络结构的演变过程和方向。借助新浪微博爬虫技术,探讨了城市旅游地的网络结构特征,测度了旅游地节点的对外联系强度,提炼了复杂网络结构的演化模式。结果表明:① 从旅游地的等级结构来看,形成了以北京和上海为核心的全国性中心城市,核心城市凭借自身的旅游集聚和吸引功能,扩大了对周边城市的旅游影响,网络化的复杂程度逐步加深。② 从旅游地节点的层级结构来看,复杂化网络形态愈加明显,旅游地节点呈现明显的点状式分布态势,以北京、上海、广州为核心的旅游地域网络结构基本形成。③ 通过对比网络演化的过程表明,单中心发育模式由在一定时期内占据主导,逐渐转向以北京、上海、广州为核心的多地域、多中心的发展模式,核心城市的主导地位更加突显。与此同时,以成都和重庆为中心的区域性双核心空间结构发育模式表现突出,形成了以多中心发育模式和区域性双核心结构模式共存的局面。以上研究无疑为新流动性范式下重新认知和揭示复杂旅游网络体系特征及其对节点、网络、结构、空间的互动关系的深刻解读,提供了一次有益的理论尝试。

关键词: 微博签到, 数据挖掘, 城市旅游地, 网络结构, 演化模式

Abstract:

The movement demonstrates the internal association among the elements of the tourism regional system, and profoundly influences the course of evolution and the direction of the network structure in tourism zones. Using the data from the Sina weibo sign-in together with the perception of the city tourism destination node, this study examines how diverse levels of network nodes disseminate in the space-dimension and its hierarchical structure characteristics in the network. Then, through measurement of the external connection intensity of the nodes and its degree of association within various cities, the study discloses and extracts the evolution process of the network configuration in tourism areas. Results are as follows: (1) From the point of the city's hierarchical configuration, it creates several national center cities that consider Beijing and Shanghai as the core. Depending on the strength of tourism collection and tourist attraction in core cities, the adjacent towns develop their tourist market. Slowly, the nodes are enclosed in an increasing number of cities. Thus, the complexity level of the system deepened. (2) From the assessment of node hierarchy of the entire system, most nodes exhibit dotted distribution situation. The major cities play a major part in the core of the network system, which creates the tourism network space organization with cities such as Beijing, Shanghai and Guangzhou as the core. Hence, these cities become an essential growth ladder of tourism improvement in China. (3) The comparison of network structure course of evolution in two distinct years demonstrates that the sole center development pattern prevails in a period, which slowly turns into the development mode of multiregional and multi-center with the cities of Beijing, Shanghai and Guangzhou as the core, wherein the principal position of the core cities is further emphasized. Meanwhile, the development mode of dual-core space structure, with Chengdu and Chongqing as the core cities, dominates. Thereby, it presents the synchronized condition of multi-center development mode and dual-core structure mode. This research offers a theoretical struggle for rediscovering and revealing the extent and profoundness of tourism activities and deep analysis of interactive relations among the node, network, structure, and space under the new flow model.

Key words: microblog sign-in, data mining, urban tourist destination, network structure, evolution mode