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Spatial Characteristics of the Tourism Flows in China

A Study Based on the Baidu Index

Dados Bibliográficos

ID22034652
AutoresYongwei Liu (0000-0002-6804-1875, Ludong University, autor correspondente), Wang Liao (0000-0001-6192-0837, Ningbo University)
Ano2021
Volume10
Fascículo6
Páginas378
Data de publicação2021-06-03
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoISPRS International Journal of Geo-Information (JOURNAL)
Identificadores do periódicoISSN: 2220-9964 • E-ISSN: 2220-9964
EditoraMDPI AG (PUBLISHER • IT)
DOI10.3390/ijgi10060378
OpenAlexW3172311027
IdiomaEN
Citações recebidas15
Referências citadas51

The characteristics of information flow, as represented by the Baidu index, reflect the pattern of tourism flows between different cities. This paper is based on the Baidu index and applies the seasonal concentration index and social network analysis (SNA) methods to study the spatial structure characteristics of tourism flows in China. The results reveal that: (1) both the search volume of the Baidu index in different cities and the online attention to different scenic areas exhibit obvious spatial heterogeneity and seasonal differences; (2) regions with strong tourism flow connections mainly occur in the areas between metropolises or among the inner cities of urban agglomerations, which are largely distributed on the southeast side of the Heihe–Tengchong Line; (3) the development of the whole tourism flow network in China is low, with an unbalanced development between tourism supply and demand, indicating that tourism resources are concentrated in a few cities and that most of the information interaction among cities occurs in core areas, while a weak interaction is observed in peripheral areas; (4) cities like Beijing and Wuhan attain obvious advantages in regard to their tourism resources, whereas other cities, including Beijing, Shanghai, Shenzhen and Guangzhou, exhibit a high demand for tourism. Moreover, tourism information flow networks are concentrated in several cities with an important role in the Chinese urban system, such as Beijing, Wuhan, and Chengdu, because they contain abundant tourism resources, well-developed transportation systems and advanced economic and societal development levels. (5) Cities such as Beijing, Lhasa, Wuhan, and Zhengzhou possess numerous advantages due to structural holes, and they thus occur at an advantageous position in the tourism flow network

Beijing · Business · China · Economic geography · Geography · Regional science · Tourism · Urban agglomeration · Computer Science · Diverse Aspects of Tourism Research · Human Mobility and Location-Based Analysis · Urban Transport and Accessibility

  • Good enough tourism demand forecasting

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    Open Access•Wenbo Yu, Jun Yang et al.•Humanities and Social Sciences…•2023

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    Open Access•Yiwei Jiang, Hsin‐che Wu et al.•Humanities and Social Sciences…•2023

  • Spatial and temporal evolution of tourism flows among 296 Chinese cities in the context of Covid-19

    Open Access•Yibo Tang, Gangmin Weng et al.•Humanities and Social Sciences…•2025

  • The Spatiotemporal Pattern Evolution and Driving Force of Tourism Information Flow in the Chengdu–Chongqing City Cluster

    Open Access•Yang Zhao, Zegen Wang et al.•ISPRS International Journal of…•2023

  • Identification of Metropolitan Area Boundaries Based on Comprehensive Spatial Linkages of Cities

    Open Access•Xiaoyuan Zhang, Hao Wang et al.•ISPRS International Journal of…•2022

  • The Regional and Local Scale Evolution of the Spatial Structure of High-Speed Railway Networks—A Case Study Focused on Beijing-Tianjin-Hebei Urban Agglomeration

    Open Access•Dan He, Zixuan Chen et al.•ISPRS International Journal of…•2021

  • Characterizing Intercity Mobility Patterns for the Greater Bay Area in China

    Open Access•Yanzhong Yin, Qunyong Wu et al.•ISPRS International Journal of…•2022

  • Verification of Geographic Laws Hidden in Textual Space and Analysis of Spatial Interaction Patterns of Information Flow

    Open Access•Lin Liu, Hang Li et al.•ISPRS International Journal of…•2023

  • Does the influence of multidimensional distances on the evolution of tourism information flow network structures differ by urbanisation rate? Evidence from China

    Ziyu Zhao, Yibao Wen et al.•Current Issues in Tourism•2026

  • A study on the spatial characteristics and spatial heterogeneity of influencing factors in the tourist market of coastal scenic area

    Yang Xu, Xu Yang et al.•Current Issues in Tourism•2025

  • Shrinking cities in China's urban network

    Open Access•Yuzhou Chen, Ruiyang Tao et al.•Applied Geography•2025

  • Nonlinear Effects of the Digital Creative Industry on Tourism

    Yang Gao, Yuting Tao et al.•Journal of China Tourism Research•2026

  • From viral to vital

    Open Access•Wenting Ma, Zhijun Gu et al.•Cities•2026

  • Geopolitical influences on spatial network structure of China-Asean tourism flows

    Siyue Chen, Yang Tan et al.•Tourism Geographies•2025

  • Social Network Analysis

    Open Access•Stanley Wasserman, Katherine Faust•Social Network Analysis•1994

  • Structural Holes

    R S Burt•Structural Holes•1992

  • Forecasting private consumption

    Open Access•Simeon Vosen, Thomas C Schmidt et al.•Journal of Forecasting•2011

  • Google Trends

    Herman Anthony Carneiro, Herman Carneiro et al.•Clinical Infectious Diseases•2009

  • Real-Time Measurement of Tourists’ Objective and Subjective Emotions in Time and Space

    Open Access•Noam Shoval, Yonatan Schvimer et al.•Travel Research Bulletin•2018

  • Correlation Studies between Land Cover Change and Baidu Index

    Open Access•Yongqing Zhao, Rendong Li et al.•ISPRS International Journal of…•2020

  • Improving unemployment rate forecasts at regional level in Romania using Google Trends

    Open Access•Mihaela Simionescu, Simionescu Mihaela•Technological Forecasting and…•2020

  • The Geography of Tourism and Recreation

    C M Hall, Stephen J Page•Geography of Tourism and Recreation•1999

  • Centrality in social networks conceptual clarification

    Open Access•Linton C Freeman•Social Networks•1978

  • Models of core/periphery structures

    Open Access•P Borgatti, Stephen P Borgatti et al.•Social Networks•2000

  • Identifying cohesive subgroups

    Open Access•K A Frank•Social Networks•1995

  • Progress in Tourism Management

    Open Access•C M Hall, Stephen J Page•Tourism Management•2009

  • Modeling the Fluctuation Patterns of Monthly Inbound Tourist Flows to China

    Yongrui Guo, Jie Zhang et al.•Asia Pacific Journal of Tourism…•2014

  • Analyzing urban development patterns based on the flow analysis method

    Open Access•Feng Zhen, Tashi Lobsang et al.•Cities•2019

  • Delineation of an urban agglomeration boundary based on Sina Weibo microblog ‘check-in’ data

    Open Access•Feng Zhen, Tashi Lobsang et al.•Cities•2017

  • Forecasting Chinese tourist volume with search engine data

    Open Access•Xin Yang, Bingyu Pan et al.•Tourism Management•2015

  • The Baidu Index

    Open Access•Xiankai Huang, Lifeng Zhang et al.•Tourism Management•2017

  • Forecasting tourist arrivals with machine learning and internet search index

    Open Access•Shaolong Sun, Yunjie Wei et al.•Tourism Management•2019

  • A dynamic linear model to forecast hotel registrations in Puerto Rico using Google Trends data

    Open Access•Roberto Rivera•Tourism Management•2016

  • Japanese tourists in transition countries of Central Europe

    Open Access•Vladimir Baláž, Miyuki Mitsutake•Tourism Management•1998

  • Tourists’ digital footprint

    Open Access•Naixia Mou, Yunhao Zheng et al.•Tourism Management•2020

  • Exploring spatio-temporal changes of city inbound tourism flow

    Open Access•Naixia Mou, Rongzheng Yuan et al.•Tourism Management•2020

  • Determinants of demand for international tourist flows to Turkey

    Open Access•Muzaffer Uysal, Joln L Crompton•Tourism Management•1984

  • Impact of incentives on tourist activity in space-time

    Open Access•Noam Shoval, Alon Kahani et al.•Annals of Tourism Research•2020

  • Persistence, long memory and seasonality in Kenyan tourism series

    Open Access•Luis A Gil-Alana, Robert Mudida et al.•Annals of Tourism Research•2014

  • Online Information Search

    Open Access•Bingyu Pan, Daniel R Fesenmaier•Annals of Tourism Research•2006

  • Factors affecting bilateral Chinese and Japanese travel

    Open Access•Sung‐su Kim, Bruce Prideaux et al.•Annals of Tourism Research•2016

  • Multi-destination trip patterns

    Open Access•Susan I Stewart, Christine A Vogt•Annals of Tourism Research•1997

  • The Flow of Information in a Global Economy

    Ronald L Mitchelson, James O Wheeler•Annals of the Association of…•1994

  • Movement Patterns of Tourists within a Destination

    Bob Mckercher, Gigi Lau•Tourism Geographies•2008

  • Pattern of Chinese tourist flows in Japan

    Bindan Zeng•Tourism Geographies•2018

  • Network analysis of tourist flows

    Hongsong Peng, Jinhe Zhang et al.•Tourism Geographies•2016

  • Sensing tourists

    Noam Shoval•Tourism Geographies•2018

Obras citantes distintas15
Citações por ano3
Intervalo de citações2021 - 2026 (6)
Velocidade de citaçãocurrent
Altamente citadoNão
Tipos de citaçãoNeutras: 15
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