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浙江大学学报(理学版)  2023, Vol. 50 Issue (1): 1-15    DOI: 10.3785/j.issn.1008-9497.2023.01.001
旅游学     
出境旅游网络关注度时空演变及影响因素研究
袁利,孙根年()
陕西师范大学 地理科学与旅游学院,陕西 西安 710119
Research on the spatial-temporal dynamic evolution and influencing factors of outbound tourism network attention: A case study on Thailand
Li YUAN,Gennian SUN()
School of Geography and Tourism,Shaanxi Normal University,Xi'an 710119,China
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摘要:

网络关注度是测量潜在旅游者对目的地旅游关注情况及需求变化的重要手段之一。基于百度指数,以我国31个省(区、市)(不含港、澳、台)的泰国旅游网络关注度为研究对象,运用季节性强度指数、地理集中度指数、赫芬达尔-赫希曼指数、地理探测器等方法,探讨我国居民对泰国旅游网络关注度的时空演化规律及其影响因素。结果表明:从时序演化上看,2011—2019年泰国旅游网络关注度呈波动上升态势,可划分为快速上升期和平稳发展期2个阶段,地区季节性差异显著,3月、7月、12月为泰国旅游网络关注度的高峰时段;从空间分异上看,泰国旅游网络关注度空间分异变化不大,空间集聚趋于分散状态,整体呈“东高-西低”的阶梯状递减特征,高关注度地区主要集中在东部地区及四川省,低关注度地区则主要分布于除四川省外的西部省份;从影响因素上看,经济发展水平(人均可支配收入、GDP)、交通便利程度、贸易开放度以及国际旅游开放度共同影响泰国旅游网络关注度的空间分布格局。

关键词: 出境旅游网络关注度时空演变地理探测器泰国    
Abstract:

Based on Baidu index, this study takes 'Thailand tourism network attention' of 31 provinces (autonomous regions and municipalities) as the research object, and explores the spatial-temporal evolution of domestic residents' attention to Thailand tourism network and its influencing factors based on seasonal intensity index, geographic concentration index, Herfindahl-Hirschman index and with geographic detector. The results can be concluded as below: from the perspective of time series evolution, Thailand tourism network attention shows a fluctuating upward trend from 2011 to 2019, which can be divided into two stages: rapid upward stage and stable development stage. The seasonal differences are extremely obvious. Over the years, March, July and December are the peak time of Thailand tourism network attention. From the perspective of spatial differentiation, the spatial structure of Thailand tourism network attention exhibits a similar spatial distribution every year. It appears as a ladder-like distribution of "high in the east and low in the west" as a whole. The area with high attention are mainly concentrated in the eastern region and Sichuan Province, while area with low attention are mainly distributed in the western provinces. From the perspective of influencing factors, the overall economic level (GDP), per capital disposable income, transportation convenience, trade openness and international tourism openness jointly affect the spatial distribution of Thailand's tourism attention, and the fundamental cause for the spatial differentiation on network attention lies in the level of economic development.

Key words: outbound tourism    network attention    spatial-temporal evolution    geographic detectors    Thailand
收稿日期: 2021-12-02 出版日期: 2023-01-13
CLC:  F590  
基金资助: 国家社会科学基金项目(20BJY204)
通讯作者: 孙根年     E-mail: gnsun@snnu.edu.cn
作者简介: 袁利(1985—),ORCID:https://orcid.org/0000-0003-1614-9080,女,博士研究生,主要从事旅游经济运行研究.
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引用本文:

袁利,孙根年. 出境旅游网络关注度时空演变及影响因素研究[J]. 浙江大学学报(理学版), 2023, 50(1): 1-15.

Li YUAN,Gennian SUN. Research on the spatial-temporal dynamic evolution and influencing factors of outbound tourism network attention: A case study on Thailand. Journal of Zhejiang University (Science Edition), 2023, 50(1): 1-15.

链接本文:

https://www.zjujournals.com/sci/CN/10.3785/j.issn.1008-9497.2023.01.001        https://www.zjujournals.com/sci/CN/Y2023/V50/I1/1

判据交互作用类型
qX1X2)<min(qX1), qX2))非线性减弱
min(qX1), qX2))< qX1X2)<max(qX1), qX2))单因子非线性减弱
qX1X2)>max(qX1), qX2))双因子增强
qX1X2)= qX1)+qX2独立
qX1X2)>qX1)+qX2非线性增强
表1  交互作用类型
图1  2011—2019年泰国旅游网络关注度的年际变化
图2  2011—2019年各月泰国旅游网络关注度占全年的比重
月份2011年2012年2013年2014年2015年2016年2017年2018年2019年
1月0.087 99.616 80.000 01.221 00.327 43.083 90.024 59.100 94.989 4
2月0.966 62.022 40.019 90.226 31.128 26.329 21.716 73.917 42.895 3
3月0.068 31.719 82.137 82.110 28.453 58.823 30.087 414.959 02.686 7
4月0.584 31.123 50.009 42.860 50.672 30.042 92.887 82.929 44.769 9
5月0.006 30.626 70.000 81.934 70.275 10.059 60.051 51.011 61.786 1
6月0.012 50.547 02.017 96.512 90.660 00.038 90.480 10.018 80.718 1
7月1.684 50.896 75.752 00.024 85.781 81.132 84.210 64.643 40.382 3
8月3.465 80.673 11.907 90.346 90.388 80.252 40.876 68.400 60.470 5
9月1.553 90.038 70.210 62.398 73.176 45.316 60.964 220.943 789.416 7
10月0.116 30.494 51.352 95.375 04.439 16.506 11.430 49.459 99.494 4
11月3.548 40.874 85.698 50.467 64.446 77.028 91.542 64.812 35.507 8
12月0.302 827.633 65.741 913.877 60.176 50.819 60.065 51.185 61.201 2
全年1.016 41.963 61.439 01.764 41.579 21.812 81.093 12.604 23.218 7
表2  2011—2019年各月泰国旅游网络关注度季节性强度指数
年份201120122013201420152016201720182019
赫芬达尔-赫希曼指数0.0400.0410.0400.0400.0420.0420.0400.0400.042
地理集中度指数20.04920.26620.07019.87820.39120.42720.10820.07820.386
首位度1.1231.1711.0641.2031.2771.2751.2821.3081.829
表3  2011—2019年泰国旅游网络关注度的空间分异
省(区、市)季节性强度指数
2011年2012年2013年2014年2015年2016年2017年2018年2019年
安徽1.6241.1342.7060.9661.3291.3052.5531.9721.591
北京1.8242.2031.8481.9391.0711.2901.3081.9941.207
福建1.5900.8713.1420.7961.5061.3461.1641.7721.825
甘肃1.6461.3312.9621.7310.7171.1020.9681.5561.447
广东1.8321.1813.1951.5071.4961.2540.6141.8824.081
广西1.4140.5982.7990.8801.1541.2510.9981.5533.523
贵州1.6050.8172.8910.8740.7600.9260.9041.4041.866
海南1.8321.2012.8830.7981.0031.1690.5971.1572.174
河北1.5710.8882.7301.6060.9161.5261.0202.0681.495
河南1.5710.8692.9301.0430.9811.5682.0302.1121.766
黑龙江1.6491.0022.5621.3490.8491.1260.8571.6402.901
湖北1.7221.0983.1970.6741.7061.5071.1402.0611.374
湖南1.6910.9583.0480.9281.8981.6691.1021.7551.497
吉林1.6290.5432.7861.1500.8611.0810.9141.7362.371
江苏1.7851.2522.8561.2581.7881.9741.0802.4561.283
江西1.5940.8393.1831.0271.4341.6830.9851.8232.453
辽宁1.5671.2402.3941.4890.8601.3020.9281.8861.721
内蒙古1.7520.9872.7051.5080.6870.9470.8951.5722.183
宁夏2.3441.5852.7201.8660.8721.2771.0432.0031.364
青海3.3072.4374.1472.0130.6860.8111.2971.7631.009
山东1.4801.2482.7281.4620.9071.4430.9382.1321.295
山西1.6590.9752.9501.5520.7861.4480.8061.6472.530
陕西1.3511.1533.0880.8621.0171.2400.9481.7391.477
上海1.7291.2141.8240.9991.0861.3850.7631.8721.022
四川1.5241.7673.1480.9921.3531.2181.6951.9982.396
天津1.4051.3482.1520.9340.8201.3150.6401.4790.731
西藏3.0311.8933.9862.4982.8530.8131.3993.2632.982
新疆1.8681.0632.7651.1940.5630.9821.5601.3221.748
云南1.4090.6052.8610.5140.8910.7631.1901.4043.274
浙江1.8741.4632.6611.2701.8031.8481.2052.3921.756
重庆1.6230.7953.1710.5061.4421.1932.4321.6241.306
表4  2011—2019年各省(区、市)泰国旅游网络关注度季节性强度指数
图3  2011—2019年泰国旅游网络关注度的省(区、市)分布注 基于国家自然资源部标准地图服务系统标准地图(审图号:GS(2019)1822号)制作,底图无修改。
影响因素(探测因子)代表性指标阐释参量属性
经济发展水平人均可支配收入/元X1正向
整体经济水平(GDP)/亿元X2正向
城市化水平城镇化率/%X3正向
网络发达程度互联网用户占总人口的比重/%X4正向
地理空间距离省会城市(直辖市)与曼谷的空间距离/kmX5负向
交通便利程度各省(区、市)直飞泰国航班数/架X6正向
贸易开放度进出口总额占地区GDP的比重/%X7正向
国际旅游开放度旅游外汇收入占地区GDP的比重/%X8正向
表5  影响因素指标体系构成
探测因子

人均可支

配收入(X1

GDP

X2

城市化

水平(X3

网络发达

程度(X4

地理空间

距离(X5

交通便利

程度(X6

贸易开放度(X7

国际旅游

开放度(X8

2013年q0.6950.7380.4110.6060.0850.6980.6550.661
p0.0050.0000.1010.0000.7000.0250.0190.019
q值排序31768254
2019年q0.5170.7230.2040.1840.1090.7340.6360.571
p0.0160.0030.2710.4340.6700.0320.0290.019
q值排序52678134
表6  因子探测分析结果

交互

因子

q

交互

因子

q

交互

因子

q
2013年2019年2013年2019年2013年2019年
X1X20.9270.797X2X60.8710.869X4X70.7960.872
X1X30.8390.772X2X70.7880.749X4X80.7330.785
X1X40.8340.907X2X80.8790.911X5X60.8780.830
X1X50.9040.922X3X40.7160.495X5X70.7530.911
X1X60.9220.907X3X50.6390.512X5X80.8180.805
X1X70.8420.798X3X60.8640.827X6X70.8260.831
X1X80.8210.938X3X70.7910.798X6X80.8550.836
X2X30.9340.833X3X80.8290.929X7X80.8130.919
X2X40.8720.885X4X50.8230.749
X2X50.8790.907X4X60.8380.817
表7  交互作用探测结果
1 李中建, 孙根年. 中美英德法出境旅游国际影响力比较: 基于经济视角的时空分析[J]. 资源科学, 2019, 41(5): 919-930. DOI:10.18402/resci.2019.05.09
LI Z J, SUN G N. International influences of outbound tourism from China, the United States, the United Kingdom, Germany, and France: Spatiotemporal analysis based on economic perspective[J]. Resources Science, 2019, 41(5): 919-930. DOI:10.18402/resci. 2019.05.09
doi: 10.18402/resci. 2019.05.09
2 XIANG Y X. The characteristics of independent Chinese outbound tourists[J]. Tourism Planning & Development, 2013, 10(2): 134-148. DOI:10.1080/21568316.2013.783740
doi: 10.1080/21568316.2013.783740
3 LIN V S S, LIU A Y, SONG H Y. Modeling and forecasting Chinese outbound tourism: An econometric approach[J]. Journal of Travel & Tourism Marketing, 2015, 32(1/2): 34-49. DOI:10.1080/10548408.2014. 986011
doi: 10.1080/10548408.2014. 986011
4 蒋依依, 刘祥艳, 李兰兰, 等. 中国出境旅游流研究[M]. 北京: 科学出版社, 2017: 1-4.
JIANG Y Y, LIU X Y, LI L L, et al. Study on Tourism Flow from China[M]. Beijing: Science Press, 2017: 1-4.
5 中国旅游研究院. 中国出境旅游发展年度报告2020[M]. 北京: 旅游教育出版社, 2021: 3-6. doi:10.4337/9781788117531.00008
China Tourism Academy. Annual Report of China Outbound Tourism Development 2020[M]. Beijing: Tourism Education Press, 2021: 3-6. doi:10.4337/9781788117531.00008
doi: 10.4337/9781788117531.00008
6 温晓金, 蒋依依, 刘焱序. “一带一路”国家入境游客规模演化规律与中国出境游客的对应特征[J]. 资源科学, 2019, 41(5): 931-942. DOI:10.18402/resci. 2019.05.10
WEN X J, JIANG Y Y, LIU Y X. Inbound tourism from destination countries in the “Belt and Road” region and corresponding outbound tourism from China during 2001 to 2015[J]. Resources Science, 2019, 41(5): 931-942. DOI:10.18402/resci.2019. 05.10
doi: 10.18402/resci.2019. 05.10
7 杨军. 中国出境旅游“双高”格局与政策取向辨析:兼与戴学锋、巫宁同志商榷[J]. 旅游学刊, 2006, 21(6): 65-68. DOI:10.3969/j.issn.1002-5006. 2006. 06.010
YANG J. An analysis of “overgrowth and over-consumption” in China's outbound travel and policy orientation[J]. Tourism Tribune, 2006, 21(6): 65-68. DOI:10.3969/j.issn.1002-5006.2006.06.010
doi: 10.3969/j.issn.1002-5006.2006.06.010
8 KEATING B W, HUANG S S, KRIZ A, et al. A systematic review of the Chinese outbound tourism literature: 1983-2012[J]. Journal of Travel & Tourism Marketing, 2015, 32(1/2): 2-17. DOI:10. 1080/10548408.2014.986009
doi: 10. 1080/10548408.2014.986009
9 戴斌, 张耀军. 90年代中国出境旅游市场的特征与趋势[J]. 财贸研究, 1997(6): 16-18. DOI: 10. 19337/j.cnki.34-1093/f.1997.06.014
DAI B, ZHANG Y J. Characteristics and trends of China's outbound tourism market in the 1990s[J]. Finance and Trade Research, 1997(6): 16-18. DOI:10.19337/j.cnki.34-1093/f.1997.06.014
doi: 10.19337/j.cnki.34-1093/f.1997.06.014
10 WANG Y, SHELDON P J. The sleeping dragon awakes: The outbound Chinese travel market[J]. Journal of Travel and Tourism Marketing, 1996, 4(4): 41-51. DOI:10.1300/J073v04n04_03
doi: 10.1300/J073v04n04_03
11 蔡家成. 试论我国出境旅游管理体制发展问题[J]. 旅游学刊, 2000, 15(3): 13-18. DOI:10.3969/j.issn. 1002-5006.2000.03.003
CAI J C. On the reform of China's outbound travel management[J]. Tourism Tribune, 2000, 15(3): 13-18. DOI:10.3969/j.issn.1002-5006.2000.03.003
doi: 10.3969/j.issn.1002-5006.2000.03.003
12 ZHOU L, KING B, TURNER L. The China outbound market: An evaluation of key constraints and opportunities[J]. Journal of Vacation Marketing, 1998, 4(2): 109-119. DOI:10.1177/135676679800400203
doi: 10.1177/135676679800400203
13 依绍华. 中国加入WTO后对出境旅游政策的影响[J]. 社会科学家, 2001, 16(3): 11-14. DOI:10. 3969/j.issn.1002-3240.2001.03.020
YI S H. Influence of China's entry into WTO on outbound tourism policy[J]. Social Scientist, 2001, 16(3): 11-14. DOI:10.3969/j.issn.1002-3240.2001. 03.020
doi: 10.3969/j.issn.1002-3240.2001. 03.020
14 张广瑞. 中国出境旅游热的冷静思考:关于中国出境旅游发展政策的辨析[J]. 财贸经济, 2005(7): 87-91,97. DOI:10.19795/j.cnki.cn11-1166/f.2005. 07.019
ZHANG G R. Sober judgement on China's outbound tourism boom: Argument for future China's outbound tourism policy[J]. Finance & Trade Economics, 2005(7): 87-91,97. DOI:10.19795/j.cnki.cn11-1166/f.2005.07.019
doi: 10.19795/j.cnki.cn11-1166/f.2005.07.019
15 WEI X, MENG F, ZHANG P. Chinese citizens' outbound destination choice: Objective and subjective factors[J]. International Journal of Tourism Research, 2017, 19(1): 38-49. DOI:10.1002/jtr. 2082
doi: 10.1002/jtr. 2082
16 KIM S S, WAN Y K P, PAN S. Differences in tourist attitude and behavior between Mainland Chinese and Taiwan tourists[J]. Journal of Travel & Tourism Marketing, 2015, 32(1/2): 100-119. DOI:10.1080/10548408.2014.986015
doi: 10.1080/10548408.2014.986015
17 LI X, HARRILL R, MUZAFFER U, et al. Estimating the size of the Chinese outbound travel market: A demand-side approach[J]. Tourism Management, 2010, 31 (2): 250-259. DOI:10. 1016/j.tourman.2009.03.001
doi: 10. 1016/j.tourman.2009.03.001
18 WANG L, FANG B, LAW R. Effect of air quality in the place of origin on outbound tourism demand: Disposable income as a moderator[J]. Tourism Management, 2018, 68: 152-161. DOI:10.1016/j.tourman.2018.03.007
doi: 10.1016/j.tourman.2018.03.007
19 刘亚萍, 于杰, 王富强. 中国赴东盟旅游流重心移动轨迹及旅游市场态分析[J]. 旅游科学, 2019, 33(4): 85-95.
LIU Y P, YU J, WANG F Q. A study on the gravity center trajectory of the tourist flow from China to ASEAN and its market form[J]. Tourism Science, 2019, 33(4): 85-95.
20 王亚力, 王楚君. 中国大陆游客访日目的地的热度分区及变化趋势[J]. 世界地理研究, 2018, 27(1): 151-158. DOI:10.16265/j.cnki.issn1003-3033.2015. 07.028
WANG Y L, WANG C J. Popularity-based destination segmentation of tourists from Mainland China to Japan and its tendency[J]. World Regional Studies, 2018, 27(1): 151-158. DOI:10.16265/j.cnki.issn1003-3033.2015.07.028
doi: 10.16265/j.cnki.issn1003-3033.2015.07.028
21 沈阳, 谢朝武. 内地游客赴港澳旅游安全事件时空分布研究[J]. 中国安全科学学报, 2015, 25(7): 171-176. DOI:10.16265/j.cnki.issn1003-3033.2015.07.028
SHEN Y, XIE C W. Research on temporal and spatial distribution of tourism safety & security emergencies for mainland tourists travelling to Hong Kong and Macao[J]. China Safety Science Journal, 2015, 25(7): 171-176. DOI:10.16265/j.cnki.issn1003-3033.2015.07.028
doi: 10.16265/j.cnki.issn1003-3033.2015.07.028
22 黄锐, 谢朝武. 中国赴东盟地区旅游安全事故风险因子的组态影响探测: 基于HEVP框架的模糊集定性比较分析[J]. 经济地理, 2021, 41(7): 202-212. DOI:10.15957/j.cnki.jjdl.2021.07.022
HUANG R, XIE C W. The detection of the configuration impact of the risk factor on the Chinese tourists' safety accidents in ASEAN: A qualitative comparative analysis of fuzzy sets based on HEVP framework[J]. Economic Geography, 2021, 41(7): 202-212. DOI:10.15957/j.cnki.jjdl.2021.07.022
doi: 10.15957/j.cnki.jjdl.2021.07.022
23 邢宁宁, 杨双双, 黄宇舟, 等. 90后出境旅游动机及价值追寻[J]. 旅游学刊, 2018, 33(9): 58-69. DOI:10.3969/j.issn.1002-5006.2018.09.011
XING N N, YANG S S, HUANG Y Z, et al. The motivation and value pursuit of outbound tourism of post-90s[J]. Tourism Tribune, 2018, 33(9): 58-69. DOI:10.3969/j.issn.1002-5006.2018.09.011
doi: 10.3969/j.issn.1002-5006.2018.09.011
24 HUANG S S, HSU C H C. Effects of travel motivation, past experience, perceived constraint, and attitude on revisit intention[J]. Journal of Travel Research, 2009, 48(1): 29-44. DOI:10.1177/0047287508328793
doi: 10.1177/0047287508328793
25 孙艳, 李咪咪, 李少华, 等. 千禧一代出境游目的地决策过程叙事研究:良机驱动的发现及其理论意义[J]. 旅游学刊, 2021, 36(7): 92-103. DOI:10. 19765/j.cnki.1002-5006.2021.07.012
SUN Y, LI M M, LI S H, et al. A narrative analysis of destination decision making process of Millennium outbound tourists: The role of opportunity-driven decision making[J]. Tourism Tribune, 2021, 36(7): 92-103. DOI:10.19765/j.cnki.1002-5006. 2021.07.012
doi: 10.19765/j.cnki.1002-5006. 2021.07.012
26 樊纲治, 王珏. 促内需消费视角下中国公民出境旅游消费的比较研究: 基于境外个人跨境旅游消费调查数据的研究[J].数理统计与管理, 2021, 40(1): 117-134. DOI:10.13860/j.cnki.sltj.20200610-001
FANG G Z, WANG J. A comparative study of Chinese citizens' outbound tourism consumption under the perspective of promoting domestic demand and consumption: Based on individual survey data from overseas countries[J]. Journal of Applied Statistics and Management, 2021, 40(1): 117-134. DOI:10.13860/j.cnki.sltj.20200610-001
doi: 10.13860/j.cnki.sltj.20200610-001
27 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. DOI:10.11821/dlxb201812014
JIANG Y Y, WEN X J, LIU Y X. Evolutionary characteristics of China's outbound tourism flow in rank-size distribution from 2001 to 2015[J]. Acta Geographica Sinica, 2018, 73(12): 2468-2480. DOI:10.11821/dlxb201812014
doi: 10.11821/dlxb201812014
28 张子昂, 保继刚. 多重距离对中国入境与出境旅游流的影响: 基于组态的视角[J]. 地理科学, 2021, 41(1): 13-21. DOI:10.13249/j.cnki.sgs.2021.01.002
ZHANG Z A, BAO J G. Effects of multiple distances on inbound and outbound tourism flows in China: A configuration-based perspective[J]. Scientia Geographica Sinica, 2021, 41(1): 13-21. DOI:10. 13249/j.cnki.sgs.2021.01.002
doi: 10. 13249/j.cnki.sgs.2021.01.002
29 刘祥艳, 杨丽琼, 吕兴洋. 文化距离对我国出境旅游的影响:基于引力模型的动态面板数据分析[J]. 旅游科学, 2018, 32(4): 60-70. DOI:10.16323/j.cnki.lykx.2018.04.005
LIU X Y, YANG L Q, LYU X Y. The Impact of cultural distance on China's outbound tourism: A dynamic panel data analysis based on the gravity model[J]. Tourism Science, 2018, 32(4): 60-70. DOI:10.16323/j.cnki.lykx.2018.04.005
doi: 10.16323/j.cnki.lykx.2018.04.005
30 吴中堂, 刘建徽, 袁俊. 大陆居民赴台湾自由行旅游流网络分析及演化研究[J]. 旅游学刊, 2016, 31(10): 113-121. DOI:DOI:10.3969/j.issn.1002-5006.2016.10.021
WU Z T, LIU J H, YUAN J. Network analysis and evolutionary studies based on tourist flows of mainland residents' self-service traveling in Taiwan[J]. Tourism Tribune, 2016, 31(10): 113-121. DOI:110.3969/j.issn.1002-5006.2016.10.021
doi: 110.3969/j.issn.1002-5006.2016.10.021
31 刘逸, 保继刚, 陈凯琪. 中国赴澳大利亚游客的情感特征研究:基于大数据的文本分析[J]. 旅游学刊, 2017, 32(5): 46-58. DOI:10.3969/j.issn.1002-5006. 2017.05.010
LIU Y, BAO J G, CHEN K Q. Sentimental features of Chinese outbound tourists in Australia: Big-data based content analysis[J]. Tourism Tribune, 2017, 32(5): 46-58. DOI:10.3969/j.issn.1002-5006. 2017.05.010
doi: 10.3969/j.issn.1002-5006. 2017.05.010
32 何月美, 邹永广, 莫耀柒. 中国游客赴马来西亚的安全感知研究:基于网络文本分析[J]. 世界地理研究, 2019, 28(6): 200-210. DOI:10.3969/j.issn.1004-9479.2019.06.2018213
HE Y M, ZOU Y G, MO Y Q. Safety perception of Chinese outbound tourists in Malaysia: Based on web text analysis[J]. World Regional Studies, 2019, 28(6): 200-210. DOI:10.3969/j.issn.1004-9479. 2019.06.2018213
doi: 10.3969/j.issn.1004-9479. 2019.06.2018213
33 阮文奇, 张舒宁, 李勇泉, 等. 中国赴泰旅游需求时空分异及其影响因素[J]. 旅游学刊, 2019, 34(5): 76-89. DOI:CNKI:SUN:LYXK.0.2019-05-012
RUAN W Q, ZHANG S N, LI Y Q, et al. Spatiotemporal differentiation and influencing factors of Chinese's tourism demand to Thailand[J]. Tourism Tribune, 2019, 34(5): 76-89. DOI:CNKI:SUN:LYXK.0.2019-05-012
doi: CNKI:SUN:LYXK.0.2019-05-012
34 徐映梅, 高一铭. 基于互联网大数据的 CPI 舆情指数构建与应用:以百度指数为例[J]. 数量经济技术经济研究, 2017, 34(1): 94-112.DOI:10.13653/j.cnki.jqte.2017.01.006
XU Y M, GAO Y M. Construction of the public opinion index of CPI based on the internet big data[J]. The Journal of Quantitative & Technical Economics, 2017, 34(1): 94-112.DOI:10.13653/j.cnki.jqte.2017.01.006
doi: 10.13653/j.cnki.jqte.2017.01.006
35 任乐, 崔东佳. 基于网络搜索数据的国内旅游客流量预测研究: 以北京市国内旅游客流量为例[J]. 经济问题探索, 2014(4): 67-73. DOI:10.3969/j.issn. 1006-2912.2014.04.011
REN L, CUI D J. Prediction of domestic tourist flow based on web search data: A case study of Beijing[J]. Inquiry Into Economic Issues, 2014(4): 67-73. DOI:10.3969/j.issn.1006-2912.2014.04.011
doi: 10.3969/j.issn.1006-2912.2014.04.011
36 任欢, 刘婷, 康俊锋, 等. 一种基于百度指数的城市日游客规模预测方法[J]. 浙江大学学报(理学版), 2020, 47(6): 753-761. DOI:10.3785/j.issn.1008-9497.2020.06.014
REN H, LIU T, KANG J F, et al. A prediction method of urban daily tourist scale based on Baidu index[J]. Journal of Zhejiang University (Science Edition), 2020, 47(6): 753-761. DOI:10.3785/j.issn.1008-9497.2020.06.014
doi: 10.3785/j.issn.1008-9497.2020.06.014
37 唐鸿, 许春晓. 中国红色旅游经典景区网络关注度时空演变及影响因素[J]. 自然资源学报, 2021, 36(7): 1792-1810. DOI:10.31497/zrzyxb.20210712
TANG H, XU C X. Spatio-temporal evolution and influencing factors of Chinese red tourism classic scenic spots network attention[J]. Journal of Natural Resources, 2021, 36(7): 1792-1810. DOI:10. 31497/zrzyxb.20210712
doi: 10. 31497/zrzyxb.20210712
38 陆利军, 戴湘毅. 基于百度指数的湖南旅游目的地城市旅游者网络关注度及其空间格局研究[J]. 长江流域资源与环境, 2020, 29(4): 836-849. DOI:10. 11870/cjlyzyyhj20200405
LU L J, DAI X Y. Research on the tourist network attention and spatial pattern of tourist destination cities in Hunan based on the Baidu index[J]. Resources and Environment in the Yangtze Basin, 2020, 29(4): 836-849. DOI:10.11870/cjlyzyyhj20200405
doi: 10.11870/cjlyzyyhj20200405
39 郑昭彦. 重大事件对举办地景区网络关注度的影响:以杭州G20峰会为例[J]. 地域研究与开发, 2019, 38(5): 111-114. DOI:
ZHENG Z Y. Influence of major events on network attention of the locations' scenic spots: A case from G20 summit in Hangzhou city[J]. Areal Research and Development, 2019, 38(5): 111-114. DOI:
40 陈金华, 胡亚美. 跨境网络舆情演化下目的地关注度时空特征:以普吉岛沉船事件为例[J]. 华侨大学学报(哲学社会科学版), 2020(3): 68-79. DOI:10. 16067/j.cnki.35-1049/c.2020.03.007
CHEN J H, HU Y M. Online public opinion of cross-border tourism: A case study of the shipwreck in Phuket Island[J]. Journal of Huaqiao University(Philosophy & Social Sciences), 2020(3): 68-79. DOI:10.16067/j.cnki.35-1049/c.2020.03.007
doi: 10.16067/j.cnki.35-1049/c.2020.03.007
41 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. DOI:
HAN J L, MING Q Z, SHI P F, 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. DOI:
42 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. DOI:10.11821/dlxb 201701010
WANG J F. XU C D. Geodetector: Principle and prospective[J]. Acta Geographica Sinica, 2017, 72(1): 116-134. DOI:10.11821/dlxb201701010
doi: 10.11821/dlxb201701010
43 李莉, 张捷. 互联网信息评价对游客信息行为和出游决策的影响研究[J]. 旅游学刊, 2013, 28(10): 23-29. DOI:10.3969/j.issn.1002-5006.2013.010.003
LI L, ZHANG J. Impact of network information evaluation on tourists' information-related behavior and travel decisions[J]. Tourism Tribune, 2013, 28(10): 23-29. DOI:10.3969/j.issn.1002-5006. 2013.010.003
doi: 10.3969/j.issn.1002-5006. 2013.010.003
44 张晓梅, 程绍文, 刘晓蕾, 等. 古城旅游地网络关注度时空特征及其影响因素:以平遥古城为例[J]. 经济地理, 2012, 36(7): 196-202,207. DOI:10. 15957/j.cnki.jjdl.2016.07.026
ZHANG X M, CHENG S W, LIU X L, et al. Spatial-temporal characteristics and influencing factors of network attention to ancient city destination: A case of Pingyao[J]. Economic Geography, 2012, 36(7): 196-202,207. DOI:10.15957/j.cnki.jjdl. 2016.07.026
doi: 10.15957/j.cnki.jjdl. 2016.07.026
45 中国旅游研究院(文化和旅游研究数据中心).《中国出境旅游发展年度报告2021》在京发布[EB/OL]. [2021-11-25]. . doi:10.14451/1.204
China Tourism Academy (Data Center of the Ministry of Culture and Tourism). Annual Report of China Outbound Tourism Development 2021[EB/OL]. [2021-11-25]. . doi:10.14451/1.204
doi: 10.14451/1.204
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