Tourist flow network structure and its influencing factors in peripheral tourism areas ————An empirical study of Yunnan Province based on online booking data

doi:10.3969/j.issn.1004-9479.2022.04.2020454

Abstract

Abstract:

Tourism scenic spots are important to the development of tourism. Analyzing the structure of the frontier province's tourism network and identifying the roles of scenic spots in the network can highlight the spatial function of the scenic spots and promote the efficient development of tourism in frontier areas. Based on the data of online travel route products, this paper used the role recognition model of scenic spots, modular analysis and optimal scale to analyze the network structure and influencing factors of 126 tourist scenic spots in Yunnan, a typical frontier province. The results show that: ①The tourism flow network in Yunnan province presents 6 cluster modules, the position of the module in the tourism flow network is determined by its main role, and the modularization trend of the scenic spots in the destination is obvious, forming a "three-core, multi-axis" mode; ②Tourist scenic spots are identified as four roles: hub scenic spots, important scenic spots, ordinary scenic spots, and fringe scenic spots. The core hubs of high-level scenic spots are prominent and have a significant agglomeration and diffusion effect. Important scenic spots are closely connected with hub scenic spots, ordinary and fringe scenic spots Concentrated in western Yunnan and connected with evacuation;③The frontier provinces Scenic spots with strong nationality and high altitude as well as large differences and high resource endowments have large passenger flows. The research results can provide some suggestions for Yunnan Province to strengthen the construction of global tourism, promote the layout of tourism industry and the role of scenic spots and quality upgrade.

Key words: tourism flow, role of scenic spot, tourist route, optimal scale analysis, peripheral tourism areas

摘要：

旅游景区是旅游业发展的关键，分析边疆省域旅游网络结构，识别网络中的景区角色，可凸显景区空间功能，促进边疆地区旅游业高效发展。研究基于在线预定数据，利用模块化分析、景区角色识别模型和最优尺度回归方法，对典型边疆省域云南省内126家旅游景区进行网络结构及影响因素分析。结果表明：①云南省旅游流网络呈现6个群集模块，模块在旅游流网络中地位由其主要角色决定，目的地内景区模块化趋势明显，形成“三核心、多轴线”的模式；②旅游景区识别为核心景区、重要景区、普通景区及边缘景区4种角色，高等级景区核心枢纽地位突出，具有显著的集聚扩散效应，重要景区与核心景区联系紧密，普通和边缘景区集中在滇东南部，联系疏散；③边疆省域内民族性强、海拔高、差异大、资源禀赋高的景区客流量大。研究结果可为云南省加强全域旅游建设、推动旅游产业布局及景区角色定位、品质升级方面提供一定参考。

关键词: 旅游流, 景区角色, 旅游线路, 最优尺度, 边缘型旅游地

Xiaoqing ZHANG, Jianxin ZHANG, Peixue LIU, Ruibing WEI, Lu TANG. Tourist flow network structure and its influencing factors in peripheral tourism areas ————An empirical study of Yunnan Province based on online booking data[J]. World Regional Studies, 2022, 31(4): 837-848.

张小青, 张建新, 刘培学, 魏瑞兵, 唐璐. 边疆省域旅游流的网络结构及影响因素[J]. 世界地理研究, 2022, 31(4): 837-848.

Figures/Tables 7

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分类	变量名称	符号	变量解释	变量类型
景区属性	景区等级	Level	分为5A、4A、3A及其他	有序变量
	景区类型	Type	分为自然、人文、民族主题及综合类	名义变量
	景区门票	LN(Price)	存在较多免门票的景区，加1后取对数处理	数值变量
	景区满意度	Satisfaction	根据携程网获取景区满意度评分	数值变量
	景区海拔高度	LN(Height)	景区海拔高度，取对数	数值变量
所属区域特征	距市中心距离	LN(Centro)	景区到所属市中心城区的距离，取对数	数值变量
	距最近机场距离	LN(Airport)	景区到最近机场的距离，取对数	数值变量
	所属城市	City	景区所在城市	名义变量
	城市经济发展水平	LN(GDP)	景区所在城市近五年GDP的均值，取对数	数值变量

分类	变量名称	符号	变量解释	变量类型
景区属性	景区等级	Level	分为5A、4A、3A及其他	有序变量
	景区类型	Type	分为自然、人文、民族主题及综合类	名义变量
	景区门票	LN(Price)	存在较多免门票的景区，加1后取对数处理	数值变量
	景区满意度	Satisfaction	根据携程网获取景区满意度评分	数值变量
	景区海拔高度	LN(Height)	景区海拔高度，取对数	数值变量
所属区域特征	距市中心距离	LN(Centro)	景区到所属市中心城区的距离，取对数	数值变量
	距最近机场距离	LN(Airport)	景区到最近机场的距离，取对数	数值变量
	所属城市	City	景区所在城市	名义变量
	城市经济发展水平	LN(GDP)	景区所在城市近五年GDP的均值，取对数	数值变量

变量	标准化系数	df值	F	重要性	容差
变量	标准化系数	df值	F	重要性	转换后	转换前
Level	0.344^**	1.000	5.251	0.089	0.746	0.744
Type	0.160^**	3.000	2.804	0.048	0.906	0.859
LN(Price)	-0.239^**	1.000	4.044	-0.001	0.680	0.677
Satisfaction	0.054	1.000	0.309	0.003	0.935	0.927
LN(Height)	0.590^***	7.000	8.087	0.225	0.720	0.851
LN(Centro)	-0.377^*	1.000	2.755	0.176	0.247	0.256
LN(Airport)	0.249	1.000	1.052	-0.110	0.222	0.257
City	1.168^***	5.000	10.008	0.370	0.250	0.632
LN(GDP)	-1.244^***	3.000	6.080	0.198	0.229	0.694

变量	标准化系数	df值	F	重要性	容差
变量	标准化系数	df值	F	重要性	转换后	转换前
Level	0.344^**	1.000	5.251	0.089	0.746	0.744
Type	0.160^**	3.000	2.804	0.048	0.906	0.859
LN(Price)	-0.239^**	1.000	4.044	-0.001	0.680	0.677
Satisfaction	0.054	1.000	0.309	0.003	0.935	0.927
LN(Height)	0.590^***	7.000	8.087	0.225	0.720	0.851
LN(Centro)	-0.377^*	1.000	2.755	0.176	0.247	0.256
LN(Airport)	0.249	1.000	1.052	-0.110	0.222	0.257
City	1.168^***	5.000	10.008	0.370	0.250	0.632
LN(GDP)	-1.244^***	3.000	6.080	0.198	0.229	0.694

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[2]	Qingyuan ZHANG, Lei LI, Lin LU. Optimization and reorganization of tourism flow network structure ——A comparative study between during-tour and post-tour in Huangshan City [J]. World Regional Studies, 2020, 29(6): 1213-1225.