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Arid Land Geography >

2023 , Vol. 46 >Issue 10: 1714 - 1722

DOI: https://doi.org/10.12118/j.issn.1000-6060.2022.614

Regional Development

Spatiotemporal distribution patterns and driving factors of tourism information flow in Chinese provinces along the Belt and Road

  • Li YANG ,
  • Yuhui FU ,
  • Jiaojiao WANG
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  • 1. School of Tourism, Xinjiang University, Urumqi 830046, Xinjiang, China
    2. Key Laboratory of the Sustainable Development of Xinjiang’s Historical and Cultural Tourism, Xinjiang University, Urumqi 830046, Xinjiang, China

Received date: 2022-11-20

  Revised date: 2023-01-01

  Online published: 2023-11-10

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Abstract

This study analyzed the spatial distribution pattern and driving factors of tourism information flow in Chinese provinces along the Belt and Road using spatial field strength, quadratic assignment procedure correlation analysis, and Geodetector. The following results were observed. (1) From 2012 to 2022, the development of the total field strength of tourism information flow in Chinese provinces along the Belt and Road is divided into three stages, such as high growth, deceleration growth, and rapid decline stage. (2) The agglomeration field strength in Chinese provinces along the Belt and Road shows a spatial distribution pattern, exhibiting high agglomeration in the northeast and southeast regions and low agglomeration in the northwest and southwest regions from 2012 to 2022. Moreover, the diffusion field strength demonstrates a spatial distribution pattern with Yunnan and Tibet as the center and the two places radiating outward, forming a gradual decrease from southwest to northeast of China. Additionally, the total field strength presents a multicore and multilevel distribution pattern with Xinjiang, Yunnan, Heilongjiang, and Guangdong as the total field strength areas of tourism information flow in the northwest, southwest, northeast, and southeast, respectively. (3) The push-pull resistance in three aspects affects the tourism information flow in Chinese provinces along the Belt and Road from 2012 to 2019. The total telecommunication services, number of internet broadband users, number of people working in the tourism industry, number of accommodation enterprises, number of business corporations in the tourism industry, number of corporate entities, total government investment in tourism, tourism resource endowment, and spatial distance are the crucial factors that affect tourism information flow.

Key words： tourism information flow; provinces along the Belt and Road; driving factors; China

Cite this article

Li YANG , Yuhui FU , Jiaojiao WANG . Spatiotemporal distribution patterns and driving factors of tourism information flow in Chinese provinces along the Belt and Road[J]. Arid Land Geography, 2023 , 46(10) : 1714 -1722 . DOI: 10.12118/j.issn.1000-6060.2022.614

References

[1] 杨延杰, 尹丹, 刘紫玟, 等. 基于大数据的流空间研究进展[J]. 地理科学进展, 2020, 39(8): 1397-1411.
[1] [Yang Yanjie, Yin Dan, Liu Ziwen, et al. Research progress on the space of flow using big data[J]. Progress in Geography, 2020, 39(8): 1397-1411. ]
[2] Usman K, Emeka O L, Katarzyna B. Do regional trade agreements enhance international tourism flows? Evidence from a cross-country analysis[J]. Journal of Travel Research, 2022, 61(6): 1391-1408.
[3] Andraz J M, Rodrigues P M. Monitoring tourism flows and destination management: Empirical evidence for Portugal[J]. Tourism Management, 2016, 56: 1-7.
[4] Lozano S, Gutiérrez E. A complex network analysis of global tourism flows[J]. International Journal of Tourism Research, 2018, 20(5): 588-604.
[5] 阮文奇, 张舒宁, 郑向敏. 中国游客赴泰旅游流网络结构及其形成机理研究[J]. 世界地理研究, 2018, 27(4): 34-44.
[5] [Ruan Wenqi, Zhang Shuning, Zheng Xiangmin. A study on the network structure of Chinese tourists’ traveling to Thailand and its formation mechanism[J]. World Regional Studies, 2018, 27(4): 34-44. ]
[6] 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.
[6] [Wang Degen, Chen Tian, Lu Lin, et al. Mechanism and HSR effect of spatial structure of regional tourist flow: Case study of Beijing-Shanghai HSR in China[J]. Acta Geographica Sinica, 2015, 70(2): 214-233. ]
[7] 韩剑磊, 明庆忠, 史鹏飞, 等. 多维“流”视角下区域旅游网络结构特征及其作用机制分析——以云南省为例[J]. 世界地理研究, 2021, 30(3): 645-656.
[7] [Han Jianlei, Ming Qingzhong, Shi Pengfei, et al. Analysis on structural characteristics of regional tourism network and its influence mechanism from the perspective of multi-dimensional flow: A case of Yunnan Province[J]. World Regional Studies, 2021, 30(3): 645-656. ]
[8] 唐顺铁, 郭来喜. 旅游流体系研究[J]. 旅游学刊, 1998(3): 38-41.
[8] [Tang Shuntie, Guo Laixi. Research on tourism flow system[J]. Tourism Tribune, 1998(3): 38-41. ]
[9] 周慧玲, 许春晓. 中国城市旅游信息流空间网络结构特征分析[J]. 统计与决策, 2019, 35(20): 91-94.
[9] [Zhou Huiling, Xu Chunxiao. Analysis on spatial network structure’s characteristics of China’s urban tourism information flow[J]. Statistics & Decision, 2019, 35(20): 91-94. ]
[10] 任浩科, 魏伟, 汪克会. 新冠疫情影响下宁夏旅游流网络结构演化研究[J]. 干旱区地理, 2023, 46(2): 316-324.
[10] [Ren Haoke, Wei Wei, Wang Kehui. Structural evolution of tourism flow network in Ningxia under the influence of COVID-19[J]. Arid Land Geography, 2023, 46(2): 316-324. ]
[11] 张秋娈, 朱苏加, 路紫, 等. 旅游网站信息流距离衰减形态分异特征及其与网站功能的关系[J]. 地理与地理信息科学, 2012, 28(4): 94-97.
[11] [Zhang Qiuluan, Zhu Sujia, Lu Zi, et al. Differentiation characteristics of distance attenuation form of tourism website information flow and the relationship between the form and website function[J]. Geography and Geo-information Science, 2012, 28(4): 94-97. ]
[12] 韩剑磊, 明庆忠, 史鹏飞, 等. 基于百度指数的中国省域旅游信息流网络结构特征及其影响因素[J]. 陕西师范大学学报(自然科学版), 2021, 49(6): 43-53.
[12] [Han Jianlei, Ming Qingzhong, Shi Pengfei, et al. The structural characteristics and influencing factors of tourism information flow network in China based on Baidu index[J]. Journal of Shaanxi Normal University (Natural Science Edition), 2021, 49(6): 43-53. ]
[13] Ruan W Q, Zhang S N. Can tourism information flow enhance regional tourism economic linkages?[J]. Journal of Hospitality and Tourism Management, 2021, 49: 614-623.
[14] 虞洋, 宋周莺, 史坤博. 基于百度指数的中国省域间信息联系网络格局及其动力机制[J]. 经济地理, 2019, 39(9): 147-155.
[14] [Yu Yang, Song Zhouying, Shi Kunbo. Network pattern of inter-provincial information connection and its dynamic mechanism in China: Based on Baidu index[J]. Economic Geography, 2019, 39(9): 147-155. ]
[15] 章锦河, 张捷, 刘泽华. 基于旅游场理论的区域旅游空间竞争研究[J]. 地理科学, 2005, 25(2): 248-256.
[15] [Zhang Jinhe, Zhang Jie, Liu Zehua. A study on spatial competition among tourism regions based on the theory of tourism field[J]. Scientia Geographica Sinica, 2005, 25(2): 248-256. ]
[16] 闫超栋, 马静. 中国省际信息化的空间关联及其影响因素分析[J]. 情报科学, 2017, 35(6): 145-153.
[16] [Yan Chaodong, Ma Jing. Analysis on spatial correlation of China’s provincial informationization and its determinants[J]. Information Science, 2017, 35(6): 145-153. ]
[17] 王劲峰, 徐成东. 地理探测器:原理与展望[J]. 地理学报, 2017, 72(1): 116-134.
[17] [Wang Jinfeng, Xu Chengdong. Geodetector: Principle and prospective[J]. Acta Geographica Sinica, 2017, 72(1): 116-134. ]
[18] 赵磊. 旅游发展与中国经济增长效率——基于Malmquist指数和系统GMM的实证分析[J]. 旅游学刊, 2012, 27(11): 44-55.
[18] [Zhao Lei. Tourism development and economic growth efficiency in China: An empirical analysis based on Malmquist index and GMM estimation[J]. Tourism Tribune, 2012, 27(11): 44-55. ]
[19] 马天, 谢彦君. 梦想的远方: 西藏具身旅游体验研究[J]. 西藏民族大学学报(哲学社会科学版), 2020, 41(3): 120-127.
[19] [Ma Tian, Xie Yanjun. The distance of dreams: A study on the embodied tourist experience of Tibet[J]. Journal of Xizang Minzu University (Philosophy and Social Sciences Edition), 2020, 41(3): 120-127. ]
[20] Dann G M S. Anomie, ego-enhancement and tourism[J]. Annals of Tourism Research, 1977, 4(4): 184-194.
[21] 杜家禛, 靳诚, 徐菁, 等. 长江三角洲虚拟旅游流空间格局及其影响因素分析[J]. 南京师大学报(自然科学版), 2021, 44(2): 48-54.
[21] [Du Jiazhen, Jin Cheng, Xu Jing, et al. The spatial pattern of virtual tourism flow and its influencing factors in Yangtze River Delta[J]. Journal of Nanjing Normal University (Natural Science Edition), 2021, 44(2): 48-54. ]
[22] 石建中, 范齐. 亚太经合组织旅游流网络结构演化及影响因素[J]. 自然资源学报, 2022, 37(8): 2169-2180.
[22] [Shi Jianzhong, Fan Qi. The evolution and influencing factors of APEC tourism flow network structure[J]. Journal of Natural Resources, 2022, 37(8): 2169-2180. ]
[23] 杨勇, 眭霞芸, 刘震. 中国省际虚拟旅游流网络结构的空间演变特征研究[J]. 地理科学进展, 2022, 41(8): 1349-1363.
[23] [Yang Yong, Sui Xiayun, Liu Zhen. Spatial pattern change of the network structure of China’s inter-provincial virtual tourism flow[J]. Progress in Geography, 2022, 41(8): 1349-1363. ]
[24] 阮文奇. 区域旅游信息流:空间网络、动力机制与溢出效应[D]. 泉州: 华侨大学, 2019.
[24] [Ruan Wenqi. Regional tourism information flow: Spatial network, dynamic mechanism and spillover effect[D]. Quanzhou: Huaqiao University, 2019. ]
[25] 刘利利, 杨秀平, 王里克, 等. 甘肃省旅游信息流影响因素研究[J]. 地域研究与开发, 2022, 41(2): 101-106.
[25] [Liu Lili, Yang Xiuping, Wang Like, et al. Influencing factors of tourism information flow in Gansu Province[J]. Areal Research and Development, 2022, 41(2): 101-106. ]
[26] 王娟, 孟凤娇, 封洁洁. 基于用户生成内容的成渝城市群旅游客流网络结构演化及机理研究[J]. 地域研究与开发, 2022, 41(1): 85-90.
[26] [Wang Juan, Meng Fengjiao, Feng Jiejie. User generated content based tourist flow network structure evolution and mechanism in Chengdu-Chongqing urban agglomeration[J]. Areal Research and Development, 2022, 41(1): 85-90. ]
[27] 张万强, 温晓丽. 中国区域市场活跃度的时空特征差异——兼对东北地区与长三角、京津冀的比较[J]. 经济纵横, 2022(10): 65-72.
[27] [Zhang Wanqiang, Wen Xiaoli. The difference of China’s regional market activity: On the comparison between northeast China, the Yangtze River Delta and the Beijing-Tianjin-Hebei region[J]. Economic Review Journal, 2022(10): 65-72. ]
[28] 常文娟, 熊元斌. 旅游公共服务水平评价及实证分析[J]. 统计与决策, 2015(17): 106-109.
[28] [Chang Wenjuan, Xiong Yuanbin. Evaluation and empirical analysis of tourism public service level[J]. Statistics & Decision, 2015(17): 106-109. ]
[29] 生延超, 李倩, 徐珊. 政府推动抑或市场驱动——中国旅游经济效率提升的动力研究[J]. 旅游学刊, 2022, 37(12): 68-82.
[29] [Sheng Yanchao, Li Qian, Xu Shan. Government impetus or market driven: On the driving force for the economic efficiency of China’s regional tourism[J]. Tourism Tribune, 2022, 37(12): 68-82. ]
[30] 梁璐, 符鸿燕, 李九全, 等. 网红城市网络关注度时空动态演变及影响因素研究——以西安市为例[J]. 地理科学, 2022, 42(9): 1566-1576.
[30] [Liang Lu, Fu Hongyan, Li Jiuquan, et al. Spatio-temporal dynamic evolution and influencing factors of net celebrity city network attention: A case of Xi’an[J]. Scientia Geographica Sinica, 2022, 42(9): 1566-1576. ]
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