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A Study on the Characteristics of the Seasonal Travel Path of Individual Chinese Travellers in Korea

중국 개인 여행객의 계절별 한국 여행경로 특성분석

  • Wang, Chun-Yan (Faculty of Tourism Management, Jilin Engineering Normal University) ;
  • Jang, Phil-Sik (Dep. of Air Transport and Logistics, Sehan University) ;
  • Kim, Hyung-Ho (Dep. of Air Transport and Logistics, Sehan University)
  • 왕춘염 (길림공정기술사범학원 관광관리학부) ;
  • 장필식 (세한대학교 항공교통물류학과) ;
  • 김형호 (세한대학교 항공교통물류학과)
  • Received : 2019.05.22
  • Accepted : 2019.07.20
  • Published : 2019.07.28

Abstract

In this study, we collected data through online travel notes from January to December 2018 and analyzed the seasonal travel characteristics of individual visiting Chinese by utilizing social network analysis. The analysis showed that Seoul is a hub for Chinese travel to Korea and the main destinations for individual visiting Chinese are concentrated in Seoul, Busan, Jeju Island, Gyeongju and Gangneung, with wide differences in seasons. The research results can be used as basic data for the development of tourism courses for individual Chinese tourists to Korea, provision of tourism services and optimization of tourism facility layout. Future research can consider continuing to use network travel notes to study the tourist destination and the mode of transportation between tourist nodes, which can provide reference for the development of tourist market and the planning and design of tourist traffic.

2016년 한국에 미군의 사드 미사일이 배치된 이후 중국으로부터 단체관광은 전면 중단되었다. 이후 중국인의 한국 관광은 개인 중심의 여행일정으로 이루어지고 있다. 본 연구에서는 2018년 1월부터 12월 까지 온라인 여행노트를 통해 데이터를 수집하고, 소셜 네트워크 분석을 활용하여 개별방문 중국인의 계절별 한국관광 여정 특성을 분석하였다. 분석 결과 서울은 중국인의 한국여행 허브이며 개별 방문 중국인의 주요 방문지는 서울, 부산, 제주도, 경주 및 강릉에 집중되어 있고 계절에 따라 큰 차이가 있는 것으로 나타났다. 연구 결과는 개별 중국인의 한국 관광을 위한 관광코스의 개발, 관광 서비스 제공 및 관광 시설배치의 최적화를 위한 기초자료로 활용될 수 있다. 향후 연구는 여행 방문지와 관광명소 사이의 이동 교통방식을 연구하여 관광 일정 계획 설계에 참고를 제공할 수 있다.

Keywords

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Fig. 1. Number of Chinese tourists to South Korea from 2009 to 2018

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Fig. 2. Network structure of China’s individual traveller flows to South Korea in 2018

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Fig. 3. Tourism flows temporal distribution of China’s individual travellers to South Korea

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Fig. 4. Network structure of China's independent tourism flows to South Korea in Spring (from Mar. 1st to May 31st, 2018)

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Fig. 6. Network structure of China's independent tourism flows to South Korea in Autumn (from Sep. 1st to Nov. 30th, 2018)

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Fig. 5. Network structure of China's independent tourism flows to South Korea in Summer (from Jun. 1st to Aug. 31st, 2018)

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Fig. 7. Network structure of China's independent tourism flows to South Korea in Winter (from Dec. 1st to Feb. 28th, 2018)

Table 1. Degree centrality of tourism nodes (by four seasons)

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References

  1. F. Liu, J. Zhang & D. Chen. (2010). The Characteristics and Dynamical Factors of Chinese Inbound Tourist Flow Network. Acta Geographica Sinica. 65(8), 1013-1024. http://www.cnki.com.cn/Article/CJFDTotal-DLXB201008014.htm
  2. N. Gao. (2010). Research on Network Structure of Tourist Flow in Yangtze River Delat: Evolution and Optimization. Master's dissertation. Shanghai Normal University, Shanghai. http://cdmd.cnki.com.cn/Article/CDMD-10270-2010084701.htm
  3. F. Balli, H. O. Balli & R. J. Louis. (2016). The impacts of immigrants and institutions on bilateral tourism flows. Tourism Management, 52, 221-229. https://doi.org/10.1016/j.tourman.2015.06.021
  4. T. Hong, T. Ma & T. C. Huan. (2015). Network behavior as driving forces for tourism flows. Journal of Business Research, 68(1), 146-156. https://doi.org/10.1016/j.jbusres.2014.04.006
  5. H. Liu. (2010). The effect analysis of Yangtze River delta's inbound tourism flow's west diffusion: take Shaanxi province as an example. Areal Research and Development, 29(4), 93-98. http://www.cnki.com.cn/Article/CJFDTOTAL-DYYY201004019.htm https://doi.org/10.3969/j.issn.1003-2363.2010.04.019
  6. M. S. Gallego, F. J. L. Rodríguez & J. V. P. Rodríguez. (2015). International trade and tourism flows: an extension of the gravity model. Economic Modelling, 52(15), 1026-1033. https://doi.org/10.1016/j.econmod.2015.10.043
  7. D. E. Lee, S. H. Kang & D. H. Park. (2017). Analyzing multi-destination travel of Chinese free independent tourists using social network analysis techniques: The case of Seoul, Incheon, and Gyeonggi Province. International Journal of Tourism and Hospitality Research, 31(5), 37-48. http://dx.doi.org/10.21298/IJTHR.2017.05.31.5.37
  8. J. Wang, J. Hu, Y. Y. Jia, D. J. Liu, X. T. Xu & L. Zhu. (2016). City tourism flow network structure and transportation mode-Taking Whhan DIY tourists for example. Economic Geography, 36(6), 176-184. http://www.cnki.com.cn/Article/CJFDTotal-JJDL201606024.htm
  9. D. East, P. Osborne, S. Kemp & T. Woodfine. (2017). Combining GPS & survey data improves understanding of visitor behaviour. Tourism Management, 61, 307-320. https://dx.doi.org/10.1016/j.tourman.2017.02.021
  10. L. C. Freeman. (2004). The development of social network analysis: a study in the sociology of science. Vancouver, B. C.: Empirical Press. ISBN 1-59457-714-5
  11. T. Opsahl, F. Agneessens & J. Skvoretz. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks. 32(3), 245-251. DOI: 10.1016/j.socnet.2010.03.006.
  12. K. Choi & J. A. Yoo. (2015). A reviews on the social network analysis using R. Journal of the Korea Convergence Society, 6(1), 77-83. https://doi.org/10.15207/JKCS.2015.6.1.077
  13. J. C. Choi. (2018). Big Data Patent Analysis Using Social Network Analysis. Journal of the Korea Convergence Society, 9(2), 251-257. https://doi.org/10.15207/JKCS.2018.9.2.251
  14. J. Y. Lee & P. S. Jang. (2017). Study on Research Trends in Airline Industry using Keyword Network Analysis: Focused on the Journal Articles in Scopus. Journal of the Korea Convergence Society, 8(5), 169-178. https://doi.org/10.15207/JKCS.2017.8.5.169
  15. H. Y. Shih. (2006). Network characteristics of drive tourism destinations: an application of network analysis in tourism. Tourism Management, 27(5), 1029-1039. https://doi.org/10.1016/j.tourman.2005.08.002
  16. N. Scott, C. Cooper & R. Baggio. (2008). Destination Networks: Four Australian Cases. Annals of Tourism Research, 35(1), 169-188. https://doi.org/10.1016/j.annals.2007.07.004
  17. W. Ruan, S. Zhang & X. Zheng. (2018). A study on the network structure of Chinese tourists' traveling to Thailand and its formation mechanism. World Regional Studies, 27(4), 34-44. DOI:10.3969/j.issn.1004-9479.2018.04.004