|Table of Contents|

The Spatial Pattern of Virtual Tourism Flow and Its InfluencingFactors in Yangtze River Delta(PDF)

《南京师大学报(自然科学版)》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2021年02期
Page:
48-54
Research Field:
·地理学·
Publishing date:

Info

Title:
The Spatial Pattern of Virtual Tourism Flow and Its InfluencingFactors in Yangtze River Delta
Author(s):
Du Jiazhen1Jin Cheng12Xu Jing3Zhou Yucui4
(1.School of Geography,Nanjing Normal University,Nanjing 210023,China)(2.Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application,Nanjing 210023,China)(3.Tourism and Social Administration College,Nanjing Xiaozhuang University,Nanjing 211171,China)(4.College of Business,Quzhou University,Quzhou 324000,China)
Keywords:
virtual tourism flowsemi-parametric GWR modelinfluencing factorsBaidu index
PACS:
K902,F592
DOI:
10.3969/j.issn.1001-4616.2021.02.008
Abstract:
Tourism flow,which is the cross-field within tourism and geography,plays an important role in study of Tourism Geography. However,previous studies have lacked exploration regarding the pattern of virtual tourism flow and its influencing factors and missed consideration about information flow in cyberspace. In fact,most studies on tourism flow only be discussed from practical point recently. In this paper,the search index in the Baidu index is used as the analysis data of virtual tourism flow. The space pattern of virtual tourism flow in Yangtze River Delta has been explored by ArcGIS and Geoda. A semi-parametric GWR model is employed for analyzing virtual tourism’s influencing factors from the perspective of inflow and outflow by comparison. The results show that:firstly,it is easy to find the feature of geographical bias in virtual tourism flow which shows the core-periphery structure in space from 2013 to 2018. Secondly,in 2013,the density of tourism flow network in the semi-parametric GWR model of the inflow of virtual tourism flow is a local parameter with liquidity,which also is the main traction force for the inflow of tourism flow,while population and per capita disposable income are non-fixed parameters of the outflow of virtual tourism flow. Population growth has a greater effect on the expansion of tourism outflow than the increase in per capita disposable income. Thirdly,the amount of A-level tourism spots in 2018 is non-fixed variable which belongs to the inflow of virtual tourism flow,promoting the inflow of virtual tourism flow in Yangtze River Delta city area,while the total retail sales of consumer goods and the amount of Internet broadband user access are significant flow variables for the outbound virtual tourism flow of cities.

References:

[1] 汪德根,陈田,陆林,等. 区域旅游流空间结构的高铁效应及机理:以中国京沪高铁为例[J]. 地理学报,2015,70(2):214-233.
[2]刘法建,张捷,陈冬冬. 中国入境旅游流网络结构特征及动因研究[J]. 地理学报,2010,65(8):1013-1024.
[3]陈超,刘家明,马海涛,等. 中国农民跨省旅游网络空间结构研究[J]. 地理学报,2013,68(4):547-558.
[4]李创新,马耀峰,张颖,等. 1993—2008年区域入境旅游流优势度时空动态演进模式:基于改进熵值法的实证研究[J]. 地理研究,2012,31(2):257-268.
[5]蒋依依,温晓金,刘焱序. 2001—2015年中国出境旅游流位序规模演化特征[J]. 地理学报,2018,73(12):2468-2480.
[6]GAO J,RYAN C,CAVE J,et al. Tourism border-making:a political economy of China’s border tourism[J]. Annals of tourism research,2019,76:1-13.
[7]FRASH RE JR,BLOSE J E. Serious leisure as a predictor of travel intentions and flow in motorcycle tourism[J]. Tourism recreation research,2019,44(4):516-531.
[8]徐维祥,李露,黄明均,等. 浙江县域“四化同步”与居民幸福协调发展的时空分异特征及其形成机制[J]. 地理科学,2019,39(10):1631-1641.
[9]罗秋菊,梁思贤. 基于数字足迹的自驾车旅游客流时空特征研究:以云南省为例[J]. 旅游学刊,2016,31(12):41-50.
[10]VITOVA P,HARMACEK J,OPRSAL Z. Determinants of tourism flows in Small Island Developing States(SIDS)[J]. Island studies journal,2009,14(2):3-22.
[11]YANG Y,FIK T,ZHANG J,et al. Modeling sequential tourist flows:where is the next destination?[J] Annals of tourism research,2013,43:297-320.
[12]涂玮,黄震方,方叶林. 基于网络团购的虚拟旅游流空间差异及动力机制研究[J]. 地域研究与开发,2013,32(4):84-89.
[13]杨小彦,张秋娈,路紫,等. 旅游网站信息流距离衰减形态描述与集中度计算[J]. 地理与地理信息科学,2010,26(6):88-91.
[14]马丽君,肖洋. 典型城市居民国内旅游流网络结构特征[J]. 经济地理,2018,38(2):197-205,219.
[15]FIGUEROA V,HERRERO L C,BAEZ A,et al. Analysing how cultural factors influence the efficiency of tourist destinations in Chile[J]. International journal of tourism research,2018,20(1):11-24.
[16]PANTANO E,CORVELLO V. Tourists’ acceptance of advanced technology-based innovations for promoting arts and culture[J]. International journal of technology management,2014,64(1):3-16.
[17]关伟,许淑婷. 中国能源生态效率的空间格局与空间效应[J]. 地理学报,2015,70(6):980-992.
[18]杨晴青,刘倩,尹莎,等. 秦巴山区乡村交通环境脆弱性及影响因素:以陕西省洛南县为例[J]. 地理学报,2019,74(6):1236-1251.
[19]彭华. 旅游发展驱动机制及动力模型探析[J]. 旅游学刊,1999,14(6):39-44.
[20]杨兴柱,顾朝林,王群. 旅游流驱动力系统分析[J]. 地理研究,2011,30(1):23-36.

Memo

Memo:
-
Last Update: 2021-06-30