Changes in the spatial and temporal characteristics of inbound tourism flows in Tibet based on geotagged photographs

HuaJian Gao; NaiXia Mou

doi:10.1051/e3sconf/202125103009

All issues

Volume 251 (2021)

E3S Web Conf., 251 (2021) 03009

References

Open Access

Issue		E3S Web Conf. Volume 251, 2021 2021 International Conference on Tourism, Economy and Environmental Sustainability (TEES 2021)


Article Number		03009
Number of page(s)		7
Section		Research on Earth Climate and Land and Ocean Resources
DOI		https://doi.org/10.1051/e3sconf/202125103009
Published online		15 April 2021

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[1] Jinfeng, W. Xuli, P. (2010) Characteristic of Beijing and Shanghai Inbound Tourists Flow Network. Scientia Geographica Sinica, 15(3): 370–376. [Google Scholar]

[2] Xiuqiong, C. Fucai, H. (2006) Temporal change of regional disparity in chinese inbound tourism. Acta Geographica Sinica, 61(12): 1271–1280. [Google Scholar]

[3] HU, S. (2005) Modeling of tourist flow to huangshan. Modern Physics Letters B, 19(28n29): 1691–1694. [Google Scholar]

[4] Lew, A. Mckercher, B. (2006) Modeling tourist movements: a local destination analysis. Annals of Tourism Research, 33(2): 403–423. [Google Scholar]

[5] Fabien, G. et al. (2008) Leveraging explicitly disclosed location information to understand tourist dynamics: a case study. Journal of Location Based Services, 2(1): 41–56. [Google Scholar]

[6] Oender, I. Koerbitz, W. Hubmann-Haidvogel, A. (2016) Tracing tourists by their digital footprints: the case of austria. Journal of Travel Research, 55(5): 566–573. [Google Scholar]

[7] Ning, C. Xia, P. & Zhou, H. (2016) Popularity analysis of tourist attraction based on geotagged social media big data. Science of Surveying and Mapping, 3(12):67–171 [Google Scholar]

[8] Tibet Autonomous Region Bureau of Statistics. (2005) Statistical Bulletin of National Economic and Social Development of Tibet Autonomous Region in 2004. Tibet Development Forum, 000(003): 55–56. [Google Scholar]

[9] Lu, G. Tianling, L. & Xiaohui, W. (2013) The empirical study of domestic tourist’s perceived Tibet tourism lmage. Tourism Science, (02):73–82. [Google Scholar]

[10] National Bureau of Statistics. (2021) 2020 China Statistical Yearbook. Statistical theory and practice, 01: 2. [Google Scholar]

[11] Sancho, M.T.M. et al. (2018) Using the spatial knowledge of map users to personalize city maps: a case study with tourists in madrid, spain. International Journal of Geo-Information, 7(8): 1–16. [Google Scholar]

[12] Yongsai, M. et al. (2018) Multilevel Visualization of Travelogue Trajectory Data. ISPRS International Journal of Geo-Information, 7(1): [Google Scholar]

[13] Imran, M. et al. (2015) Travel Recommendation Using Geo-tagged Photos in Social Media for Tourist. Wireless Personal Communications, 80(4) 1347–1362. [Google Scholar]

[14] Juan, C. Javier, G. & Carmen, M. (2015) Identification of tourist hot spots based on social networks: A comparative analysis of European metropolises using photo-sharing services and GIS. Applied Geography, 63: 408–417. [Google Scholar]

[15] Kuo, C. Chan, T. Fan, I. et al. (2018) Efficient method for POI/ROI discovery using Flickr geotagged photos. ISPRS International Journal of Geo-Information, 7(3): 121. [Google Scholar]

[16] Abdul, M. Ling, C. Gencai, C. et al. (2013) A context-aware personalized travel recommendation system based on geotagged social media data mining. International Journal of Geographical Information Science, 27(4): 662–684. [Google Scholar]

[17] Ruiz, C. Spiliopoulou, M. Menasalvas, E. (2010) Density-based semi-supervised clustering. Data Mining & Knowledge Discovery, 21(3): 345–370. [Google Scholar]

[18] Cristóbal, C. Ángeles, G. María-Rosa, G. (2016) Social network analysis in tourism. Current Issues in Tourism, 19(12): [Google Scholar]

[19] Naixia, M. Rongzheng, Y. Tengfei, Y. et al. (2020) Exploring spatio-temporal changes of city inbound tourism flow: The case of Shanghai, China. Tourism Management, 76: [Google Scholar]