DOI QR코드

DOI QR Code

A Study on the Revitalization of Local Tourism in Yongin City Based on Tourism Bigdata Analytics: Focusing on Geographic Information System Analytics Combining Mobile Communication and Credit Card Data

관광 빅데이터 기반의 용인시 관내 관광 활성화 방안: 이동통신과 신용카드 데이터를 결합한 지리정보시스템 분석을 중심으로

  • An, Eunhee (School of Business, Yonsei University) ;
  • An, Jungkook (Department of Business Administration, Sun Moon University)
  • Received : 2020.12.28
  • Accepted : 2021.04.20
  • Published : 2021.04.28

Abstract

Recently, there is increasing interest in attracting local tourist in the city to revitalize the local economy. For this purpose, customized tourism strategies based on the analysis of travel routes and consumption patterns are becoming important. However, existing studies either focused on limited mainstream tourist analysis or lacked analysis of tourists' behavior-based data perspectives. Therefore, this study aims to present a big data-based tourism strategy that provides customized information by analyzing the demand of individual travelers in details based on mobile service data and card expenditure data generated by the travelers in Yongin city. By tracing those data, this study visualized the tourists' itinerary and their expenditure patterns. The analysis of data from July 2017 to June 2018 shows that men tend to consume in various areas compared to women. It also shows consumption areas for people in their 30s and 40s are similar, whereas those in their 20s do not vary. Using the big data based on Geographic Information system, this study provides strategic insights to administrative personnel who are in charge of tour policy.

최근 지역경제 활성화를 위해 관내 관광객 유치에 관한 관심이 높아지고 있으며, 이에 관내 관광객들을 이동경로 및 소비 패턴 분석에 기반한 맞춤형 관광 전략이 중요하다. 하지만 기존의 연구들은 한정된 주류 관광객 분석에 초점을 두어, 관광객들의 행위 기반 데이터 관점의 분석이 부족하였다. 이에 본 연구는 빅데이터 분석과 지리정보시스템을 결합하여 관내 관광객들의 이동 경로 및 소비 패턴을 분석하여 빅데이터 기반의 관광 전략을 제시하고자 한다. 본 연구는 용인시에서 발생한 카드 지출 데이터 및 통신 데이터를 바탕으로 관내 관광객들의 이동 패턴 및 소비 패턴을 분석하여 시각화하였다. 2017년 7월부터 2018년 6월까지 1년간의 데이터 분석을 통해 여성보다 남성이 다양한 지역에서 소비하는 경향이 있고, 나이별로는 30대와 40대가 소비지역이 비슷하게 나타나는 것을 알 수 있었다. 본 연구는 관광 및 소비 패턴을 지리정보시스템을 활용하여 가시화함으로써 관광, 행정 및 정책의 실무자들에게 전략적 방안을 제시하는데 시사점이 있다.

Keywords

References

  1. S. R. Kim, M. M. Kang & S. M. Park. (2012). The Future of Big Data. Communications of the Korean Institute of Information Scientists and Engineers, 30(6), 18-24.
  2. T. I. Kwon & C. H. Lee. (2017). A Study on the Utilization System and Empirical Analysis of Big Data in Tourism. Policy Research: Korea Culture & Tourism Institute
  3. S. Lee, Y. K. Huh & H. M. Kim. (2014). Transportation Planning Using Big Data: Reducing Late Night Buses and Accidents. Seoul Solution
  4. H. J. Jeon & Y. H. Yoo. (2014). The Big Data Era in Tourism Industry. TourGo Focus: Tour Korea Culture & Tourism Institute
  5. S. K. Bae. (2015). Building Smart Tourism Information based on Big Data. LSXSIRI Report: Spatial Information Research Institute
  6. W. S. Shim, S. M. Choi & C. S. Shim. (2018). Identifying Major Issues of Tourism Analysis Using Big Data: Focused on Mobile and Credit Card Data. Journal of Tourism Studies, 30(3), 3-22. DOI : 10.21581/jts.2018.08.30.3.3
  7. S. S. Lee. (2011). Applications of Geographic Information Systems in Public Library Marketing. Journal of the korean Society for Information Management, 28(3), 179-195. DOI : 10.3743/KOSIM.2011.28.3.179
  8. Korea National Spatial Data Infrastructure Portal. http://www.nsdi.go.kr/
  9. J. W. Kim & B. K. Yoon. (2013). Theoretical Background and Domestic and International Research Trend of GIS in Tourism. Journal of Tourism Sciences, 37(10), 137-162.
  10. B. K. Yoon & J. W. Kim. (2010). Exploring the Role of Web-based Public Participation GIS Information in Sustainable Coastal Tourism -A Case Example of U.S.A Wisconsin Coastal Management Program(WCMP). Journal of the Association of Korean Photo-Geographers, 20(2), 117-128. https://doi.org/10.35149/jakpg.2010.20.2.010
  11. Y. Noh. (2007). Study on Architecture of the Tourism Information System using Web-Based GIS. Journal of Finance & Knowledge Studies, 5(1), 159-177.
  12. U. W. Sun & J. K. Min. (2017). The Analysis of Consumption Trend of Tourists to Resorts by Big Data Application. Journal of Hotel & Resort. 16(2), 5-26.
  13. M. S. Kang. (2017). A Study on the Development of Topic Map for Analysis of Customer Satisfaction in Tourism industry. Journal of the Korea Convergence Society, 8(10), 249-255. DOI : 10.15207/JKCS.2017.8.10.249
  14. J. D. Lim, S. G. Kim & C. Y. Shin. (2020). A Study on the Improvement for Industrial Land Information System. Journal of the Korea Convergence Society, 11(10), 97-106. DOI : 10.15207/JKCS.2019.10.1.035
  15. J. H. Kim. (2018). An Analysis of Factors and and Satisfaction analysis of choice of Traveling Places. Policy Research: Korea Culture & Tourism Institute
  16. M. K. Lee. (2018). Analysis of Korean Travel Trends Using Social Big Data: Focused on Family Travel and Solo Travel. Journal of Tourism Sciences, 42(10), 111-134. DOI : 10.17086/JTS.2018.42.10.111.134
  17. J. K. An & H. W. Kim. (2015). Building a Korean Sentiment Lexicon Using Collective Intelligence, Journal of Intelligence and Information Systems, 21(2), 49-67. DOI : 10.13088/jiis.2015.21.2.49
  18. J. K. An, S. H. Lee, E. H. An & H. W. Kim. (2016). Fintech Trends and Mobile Payment Service Analysis in Korea: Application of Text Mining Techniques, Informatization Policy, 23(3), 26-42. DOI : 10.22693/NIAIP.2016.23.3.026
  19. E. H. An & J. K. An. (2020). An Analysis of the 2017 Korean Presidential Election Using Text Mining. Journal of the Korea Convergence Society, 11(5), 199-207. DOI : 10.15207/JKCS.2020.11.5.199