基于网络游记的城市旅游流网络结构演化研究——以北京市为例

doi:10.13249/j.cnki.sgs.2020.02.015

地理科学 ›› 2020, Vol. 40 ›› Issue (2): 298-307.doi: 10.13249/j.cnki.sgs.2020.02.015

基于网络游记的城市旅游流网络结构演化研究——以北京市为例

周李¹, 吴殿廷¹(), 虞虎², 王永明³, 马腾⁴, 胡灿¹

^1. 北京师范大学地理科学学部环境遥感与数字城市北京市重点实验室/北京师范大学地理科学学部地理学院, 北京 100875
^2. 中国科学院地理科学与资源研究所, 北京100101
^3. 湖南师范大学旅游学院,湖南长沙 410081
^4. 杭州师范大学经济与管理学院, 浙江杭州 311121

收稿日期:2019-03-23 修回日期:2019-06-05 出版日期:2020-02-10 发布日期:2020-04-09
通讯作者: 吴殿廷 E-mail:wudianting@bnu.edu.cn
作者简介:周李（1988-）,男,安徽宣城人,博士研究生,主要研究方向为旅游地理与旅游规划。E-mail: zhouliahxc@163.com
基金资助:
国家自然科学基金项目(41771128);国家自然科学基金项目(41701164);国家自然科学基金项目(41761023);国家旅游局“万名旅游英才计划”研究型英才培养项目(WMYC20171092)

Evolution of Urban Tourism Flow Network Structure Based on Network Travel Notes: A Case Study of Beijing City

Zhou Li¹, Wu Dianting¹(), Yu Hu², Wang Yongming³, Ma Teng⁴, Hu Can¹

^1. Beijing Key Laboratory of Environmental Remote Sensing and Digital City, School of Geography, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
^2. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
^3. Tourism College,Hunan Normal University,Changsha 410081, Hunan,China
^4. Economics and Management School, Hangzhou Normal University, Hangzhou 311121, Zhejiang, China

Received:2019-03-23 Revised:2019-06-05 Online:2020-02-10 Published:2020-04-09
Contact: Wu Dianting E-mail:wudianting@bnu.edu.cn
Supported by:
National Natural Science Foundation of China(41771128);National Natural Science Foundation of China(41701164);National Natural Science Foundation of China(41761023);The Research Oriented Talent Training Program of National Administration of China(WMYC20171092)

摘要/Abstract

摘要：

采用网络游记文本数据,结合爬虫技术和社会网络分析方法,分析北京市“十二五”和“十三五”时期城市旅游客流网络结构的演变特征。结果表明：① 城市旅游流网络结构存在不均衡性,城市历史文化遗产节点的的影响控制能力强,新兴旅游吸引物的集聚能力相对较弱,受不同类型节点的路径依赖效应的影响,这种差异存在动态扩大特征。② 城市旅游流网络结构呈现显著的等级分层结构和节点分散分布特征,传统游憩场所是城市旅游流的核心节点,外围发育的旅游节点较分散且规模能级提升空间较大。③ 城市旅游流拥有显著的赋能效应、倒逼效应和联通效应。

关键词: 旅游流, 演化特征, 效应, 北京市, 社会网络

Abstract:

Studying the law of urban tourism flow is an important scientific problem to be solved first in the utilization and optimization of urban recreational space. Based on the text of network travel notes, this article combines the crawler technology and social network analysis method, obtains the data of tourist flow in Beijing during the 12th Five-Year Plan and 13th Five-Year Plan, and analyses the evolution characteristics of urban tourist flow network structure. 1) The overall density of Beijing's tourism flow network is low, the network structure is increasing unevenly, and the tourism flow network is obviously controlled by the core tourism nodes. 2) The structure of Beijing's tourism flow network is characterized by central agglomeration and hierarchy. The traditional recreational sites such as Tiananmen, the Forbidden City, the Summer Palace and the Badaling Great Wall are still the core tourist nodes of Beijing's tourism flow, and the scale and energy level of the peripheral tourist nodes are greatly improved. 3) The unbalance of tourism flow nodes in different directions of urban development is aggravating, which is greatly influenced by the distribution of urban historical and cultural heritage, and the agglomeration ability of emerging tourism attractions is relatively weak. 4) The rapid development of urban rail transit is reshaping the spatial pattern of urban tourism flow. The demand for new cultural space such as Olympic Sports Center, 798 Art District and Sanlitun is increasing, which makes it develop towards multi-node and multi-region.

Key words: tourism flow, evolutionary characteristics, effects, Beijing City, social network

中图分类号:

F592

周李, 吴殿廷, 虞虎, 王永明, 马腾, 胡灿. 基于网络游记的城市旅游流网络结构演化研究——以北京市为例[J]. 地理科学, 2020, 40(2): 298-307.

Zhou Li, Wu Dianting, Yu Hu, Wang Yongming, Ma Teng, Hu Can. Evolution of Urban Tourism Flow Network Structure Based on Network Travel Notes: A Case Study of Beijing City[J]. SCIENTIA GEOGRAPHICA SINICA, 2020, 40(2): 298-307.

图/表 5

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2012年				2017年
旅游节点	效能大小	效率性	约束性	旅游节点	效能大小	效率性	约束性
南锣鼓巷	14.80	0.78	0.16	南锣鼓巷	16.74	0.73	0.15
奥体中心	13.63	0.72	0.17	天坛	14.17	0.68	0.16
天安门广场	13.56	0.80	0.15	天安门广场	13.83	0.73	0.15
王府井步行街	9.58	0.64	0.21	什刹海	13.18	0.69	0.17
什刹海	9.18	0.66	0.22	奥体中心	12.78	0.67	0.16
前门	8.84	0.63	0.20	颐和园	10.32	0.65	0.19
天坛	8.65	0.62	0.22	王府井步行街	9.05	0.57	0.19
颐和园	8.53	0.71	0.22	故宫	8.88	0.56	0.22
故宫	7.53	0.58	0.24	恭王府	7.68	0.59	0.20
北海公园	6.65	0.55	0.25	前门	7.53	0.54	0.21
八达岭长城	6.05	0.61	0.22	八达岭长城	6.14	0.51	0.20
恭王府	6.00	0.67	0.22	三里屯	5.75	0.58	0.20
圆明园	4.40	0.63	0.34	北海公园	4.59	0.42	0.25
西单	4.22	0.60	0.26	雍和宫	4.54	0.57	0.31
798艺术区	3.58	0.60	0.28	景山公园	4.47	0.41	0.24

基于网络游记的城市旅游流网络结构演化研究——以北京市为例

Evolution of Urban Tourism Flow Network Structure Based on Network Travel Notes: A Case Study of Beijing City

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