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The Geographic Spread and Preferences of Tourists Revealed by User-Generated Information on Jeju Island, South Korea

Author

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  • David M. Fisher

    (Natural Capital Project, Woods Institute for the Environment, Stanford University, Stanford, CA 94305, USA)

  • Spencer A. Wood

    (Natural Capital Project, Woods Institute for the Environment, Stanford University, Stanford, CA 94305, USA
    School for Environmental and Forest Sciences, University of Washington, Seattle, WA 98195, USA
    eScience Institute, University of Washington, Seattle, WA 98195, USA)

  • Young-Hee Roh

    (The Institute for Korean Regional Studies, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea)

  • Choong-Ki Kim

    (Division for Natural Environment, Korea Environment Institute, 370 Sicheong-daero, Sejong-si 30147, Korea)

Abstract

Recreation and tourism are important ways that people interact with and derive benefits from natural environments. Understanding how and where nature provides recreational opportunities and benefits is necessary for management decisions that impact the environment. This study develops and tests an approach for mapping tourism patterns, and assessing people’s preferences for cultural and natural landscapes, using user-generated geographic content. The volume of geotagged images and tweets shared publicly on Flickr and Twitter and proprietary mobile phone traffic provided by a telecommunications company, are used to map visitation rates to potential tourist destinations across Jeju Island, South Korea. We find that densities of social media posts and mobile phone traffic are all correlated with ticket sales and counts of gate entries at tourist sites. Using multivariate linear regression, we measure the degree to which attributes of the natural and built environment explain variation in visitation rates, and find that tourists to Jeju Island prefer to recreate near beaches, sea cliffs, golf courses and hiking trails. We conclude that high-resolution and spatially-explicit visitation data provided by user-generated content open the door for statistical models that can quantify recreation demand. Managers and practitioners could combine these flexible and relatively inexpensive user-generated data with more traditional survey data to inform sustainable tourism development plans and policy decisions. These methods are especially useful in the context of landscape or regional-scale ecosystem service assessments, where there is a need to map the multiple ecological, economic, and cultural benefits of the environment.

Suggested Citation

  • David M. Fisher & Spencer A. Wood & Young-Hee Roh & Choong-Ki Kim, 2019. "The Geographic Spread and Preferences of Tourists Revealed by User-Generated Information on Jeju Island, South Korea," Land, MDPI, vol. 8(5), pages 1-17, April.
  • Handle: RePEc:gam:jlands:v:8:y:2019:i:5:p:73-:d:226108
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    References listed on IDEAS

    as
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    Cited by:

    1. Bianca E. Lopez & Nicholas R. Magliocca & Andrew T. Crooks, 2019. "Challenges and Opportunities of Social Media Data for Socio-Environmental Systems Research," Land, MDPI, vol. 8(7), pages 1-18, July.
    2. Cheng Chai & Yao Song & Zhenzhen Qin, 2021. "A Thousand Words Express a Common Idea? Understanding International Tourists’ Reviews of Mt. Huangshan, China, through a Deep Learning Approach," Land, MDPI, vol. 10(6), pages 1-15, May.
    3. Zoltán Kovács & György Vida & Ábel Elekes & Tamás Kovalcsik, 2021. "Combining Social Media and Mobile Positioning Data in the Analysis of Tourist Flows: A Case Study from Szeged, Hungary," Sustainability, MDPI, vol. 13(5), pages 1-15, March.
    4. Kim, Eui-Jin & Kim, Youngseo & Jang, Sunghoon & Kim, Dong-Kyu, 2021. "Tourists’ preference on the combination of travel modes under Mobility-as-a-Service environment," Transportation Research Part A: Policy and Practice, Elsevier, vol. 150(C), pages 236-255.
    5. Víctor García-Díez & Marina García-Llorente & José A. González, 2020. "Participatory Mapping of Cultural Ecosystem Services in Madrid: Insights for Landscape Planning," Land, MDPI, vol. 9(8), pages 1-15, July.
    6. Wood, Spencer A & Winder, Samantha & Lia, Emilia & White, Eric & Crowley, Christian & Milnor, Adam, 2020. "Next-generation Visitation Models using Social Media to Estimate Recreation on Public Lands," SocArXiv 4wm97, Center for Open Science.
    7. Stefano Bruzzese & Wasim Ahmed & Simone Blanc & Filippo Brun, 2022. "Ecosystem Services: A Social and Semantic Network Analysis of Public Opinion on Twitter," IJERPH, MDPI, vol. 19(22), pages 1-15, November.
    8. Richards, Daniel Rex & Lavorel, Sandra, 2022. "Integrating social media data and machine learning to analyse scenarios of landscape appreciation," Ecosystem Services, Elsevier, vol. 55(C).

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