自然资源学报 ›› 2022, Vol. 37 ›› Issue (1): 70-82.doi: 10.31497/zrzyxb.20220105

• 其他研究论文 • 上一篇    下一篇

中国市域旅游流网络结构空间分异及其效应研究——基于携程旅行网的大数据挖掘

方叶林1(), 黄震方2, 李经龙1, 程雪兰1, 苏雪晴1   

  1. 1.安徽大学商学院,合肥 230601
    2.南京师范大学地理科学学院,南京 210023
  • 收稿日期:2020-08-16 修回日期:2020-12-01 出版日期:2022-01-28 发布日期:2022-03-28
  • 作者简介:方叶林(1986- ),男,安徽巢湖人,博士,副教授,硕士生导师,研究方向为旅游地理与区域经济。E-mail: fangyelin2006@126.com
  • 基金资助:
    国家自然科学基金项目(42171238);国家自然科学基金项目(41601142);安徽省自然科学基金项目(2108085MD125);安徽省哲学社会科学规划项目(AHSKQ2020D64)

Research on the spatial differentiation and effects of network structure in tourism flow in Chinese cities: Big data mining based on Ctrip

FANG Ye-lin1(), HUANG Zhen-fang2, LI Jing-long1, CHENG Xue-lan1, SU Xue-qing1   

  1. 1. School of Business, Anhui University, Hefei 230601, China
    2. College of Geographic Science, Nanjing Normal University, Nanjing 210023, China
  • Received:2020-08-16 Revised:2020-12-01 Online:2022-01-28 Published:2022-03-28

摘要:

大数据驱动的旅游流网络结构研究是旅游流深化研究的主要方向之一。利用网络爬虫技术抓取携程旅行网上中国市域尺度的旅游线路及相关数据,在构建O-D矩阵的基础上,揭示网络结构指标的空间分异及其效应。中国市域旅游流网络具有以下特征:网络同质性较小;不同主题类旅游流流量总体上遵从东多西少、东南多西北少的总体格局;中国市域旅游流网络形成了五大高值集聚区,分别为长三角城市群、泛珠三角地区、云南内部、青海—甘肃交界地区、北疆地区;中国市域旅游流网络还具有显著的资源—经济指向性,“结构”效应总体不显著。未来需要进一步重视区域旅游网络化的发展特征,在尊重空间规律的基础上,发挥核心组团的辐射作用,发挥“资源—经济”的双核驱动,促进跨区域旅游合作。

关键词: 旅游流, 网络结构, 空间分异, 效应, 大数据

Abstract:

The research on the structure of tourism flow network driven by big data is one of the main directions of deepening research. By using web crawler technology to capture travel routes and related data on Ctrip, and then constructing a 299×299 tourism flow of O-D matrix in Chinese cities, this article reveals the spatial differentiation and effects of network structure. The results show that: (1) The tourism flow network homogeneity is small in Chinese cities. Tourism flow of different themes follows the overall pattern of "more in the east and less in the west, and more in the southeast and less in the northwest". The high-value areas of the network degree are mainly concentrated in the Yangtze River Delta Urban Agglomeration, the Chengdu-Chongqing Economic Circle, as well as Yunnan province and its surrounding areas. A low-value "depression" is formed in Northeast China. (2) The tourism flow network has obvious group characteristics in Chinese cities. It has formed five high-value agglomeration areas, namely: the Yangtze River Delta Urban Agglomeration (one strong and multi-super network), the Pan-Pearl River Delta Region (multi-core series network), and Yunnan (closed quadrilateral network), Qinghai-Gansu Junction Area (multi-node and fan-shaped network), and northern Xinjiang (node-series network). (3) The tourism flow network has significant resource-economic orientation in Chinese cities. The distribution of high value of tourism flow under different themes has the characteristics of "along the line and besieged city". Whether a node can have an advantageous "position" in the network is affected by the "push-pull" force of regional economy and tourism resources. (4) Generally, the "structure" effect is not obvious in the tourism flow network of Chinese cities. On the one hand, the network structure index has significant power-law characteristics, making the network structure advantage embodied in a few nodes; on the other hand, according to the average value of the network node degree and the number of tourists, the relationship between them can be identified into 4 types: high-high type, low-high type, low-low type, and high-low type. Most of the nodes are at a disadvantage position in the network. The high-quality development of tourism in the period of "14th Five-year Plan (2021-2025)" must attach importance to the development trend of networking characteristics of urban tourism, and reasonably arrange tourism elements according to the networking characteristics. For one thing, we should pay more attention to the development law of tourism flow network, promote cross-regional tourism cooperation; for the other, we should give full play to the radiation role of the core group and the dual-core drive of "resource-economy" of Chinese urban tourism.

Key words: tourism flow, network structure, spatial differentiation, effect, big data