IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v8y2019i5p73-d226108.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/8/5/73/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2073-445X/8/5/73/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Paulina Guerrero & Maja Steen Møller & Anton Stahl Olafsson & Bernhard Snizek, 2016. "Revealing Cultural Ecosystem Services through Instagram Images: The Potential of Social Media Volunteered Geographic Information for Urban Green Infrastructure Planning and Governance," Urban Planning, Cogitatio Press, vol. 1(2), pages 1-17.
    2. Adamowicz, Wiktor & Swait, Joffre & Boxall, Peter & Louviere, Jordan & Williams, Michael, 1997. "Perceptions versus Objective Measures of Environmental Quality in Combined Revealed and Stated Preference Models of Environmental Valuation," Journal of Environmental Economics and Management, Elsevier, vol. 32(1), pages 65-84, January.
    3. Steenbruggen, John & Tranos, Emmanouil & Nijkamp, Peter, 2015. "Data from mobile phone operators: A tool for smarter cities?," Telecommunications Policy, Elsevier, vol. 39(3), pages 335-346.
    4. Andy Jones & Jan Wright & Ian Bateman & Marije Schaafsma, 2010. "Estimating Arrival Numbers for Informal Recreation: A Geographical Approach and Case Study of British Woodlands," Sustainability, MDPI, vol. 2(2), pages 1-18, February.
    5. Nick Hanley & Sergio Colombo & Dugald Tinch & Andrew Black & Ashar Aftab, 2006. "Estimating the benefits of water quality improvements under the Water Framework Directive: are benefits transferable?," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 33(3), pages 391-413, September.
    6. Feather Peter & Hellerstein Daniel & Tomasi Theodore, 1995. "A Discrete-Count Model of Recreational Demand," Journal of Environmental Economics and Management, Elsevier, vol. 29(2), pages 214-227, September.
    7. Ward, Frank A. & Loomis, John B., 1986. "The Travel Cost Demand Model As An Environmental Policy Assessment Tool: A Review Of Literature," Western Journal of Agricultural Economics, Western Agricultural Economics Association, vol. 11(2), pages 1-15, December.
    8. Keeler, Bonnie L. & Wood, Spencer A. & Polasky, Stephen & Kling, Catherine L. & Filstrup, Christopher T. & Downing, John A., 2015. "Recreational demand for clean water: evidence from geotagged photographs by visitors to lakes," ISU General Staff Papers 201501290800001557, Iowa State University, Department of Economics.
    9. Ruckelshaus, Mary & McKenzie, Emily & Tallis, Heather & Guerry, Anne & Daily, Gretchen & Kareiva, Peter & Polasky, Stephen & Ricketts, Taylor & Bhagabati, Nirmal & Wood, Spencer A. & Bernhardt, Joanna, 2015. "Notes from the field: Lessons learned from using ecosystem service approaches to inform real-world decisions," Ecological Economics, Elsevier, vol. 115(C), pages 11-21.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    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. 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.
    7. 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.
    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Havinga, Ilan & Bogaart, Patrick W. & Hein, Lars & Tuia, Devis, 2020. "Defining and spatially modelling cultural ecosystem services using crowdsourced data," Ecosystem Services, Elsevier, vol. 43(C).
    2. Mat Alipiah, Roseliza & Anang, Zuraini & Abdul Rashid, Noorhaslinda Kulub & Smart, James C. R. & Wan Ibrahim, Wan Noorwatie, 2018. "Aquaculturists Preference Heterogeneity towards Wetland Ecosystem Services: A Latent Class Discrete Choice Model," Jurnal Ekonomi Malaysia, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, vol. 52(2), pages 253-266.
    3. Cati Torres & Nick Hanley, 2016. "Communicating Research on the Economic Valuation of Coastal and Marine Ecosystem Services," Discussion Papers in Environment and Development Economics 2016-12, University of St. Andrews, School of Geography and Sustainable Development.
    4. Depietri, Yaella & Ghermandi, Andrea & Campisi-Pinto, Salvatore & Orenstein, Daniel E., 2021. "Public participation GIS versus geolocated social media data to assess urban cultural ecosystem services: Instances of complementarity," Ecosystem Services, Elsevier, vol. 50(C).
    5. Gugulica, Madalina & Burghardt, Dirk, 2023. "Mapping indicators of cultural ecosystem services use in urban green spaces based on text classification of geosocial media data," Ecosystem Services, Elsevier, vol. 60(C).
    6. Phaneuf, Daniel J. & Smith, V. Kerry, 2006. "Recreation Demand Models," Handbook of Environmental Economics, in: K. G. Mäler & J. R. Vincent (ed.), Handbook of Environmental Economics, edition 1, volume 2, chapter 15, pages 671-761, Elsevier.
    7. Sant'Anna, Ana Claudia & Bergtold, Jason & Shanoyan, Aleksan & Caldas, Marcellus & Granco, Gabriel, 2021. "Deal or No Deal? Analysis of Bioenergy Feedstock Contract Choice with Multiple Opt-out Options and Contract Attribute Substitutability," 2021 Conference, August 17-31, 2021, Virtual 315289, International Association of Agricultural Economists.
    8. Liao, Chih-Chien & Houston, Jack E., Jr. & Bergstrom, John C., 1989. "Recreation Demand Factor Indices: A Principal Components Analysis," 1989 Annual Meeting, July 30-August 2, Baton Rouge, Louisiana 270497, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    9. Stephen Hynes & Nick Hanley & Cathal O’Donoghue, 2006. "Using Continuous and Finite Mixture Models to Account for Preference Heterogeneity in a group of Outdoor Recreationalists," Working Papers 0602, Rural Economy and Development Programme,Teagasc.
    10. Parsons, George R. & Jakus, Paul M. & Tomasi, Ted, 1999. "A Comparison of Welfare Estimates from Four Models for Linking Seasonal Recreational Trips to Multinomial Logit Models of Site Choice," Journal of Environmental Economics and Management, Elsevier, vol. 38(2), pages 143-157, September.
    11. Stephen Hynes & Nick Hanley & Eoghan Garvey, 2007. "Up the Proverbial Creek without a Paddle: Accounting for Variable Participant Skill Levels in Recreational Demand Modelling," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 36(4), pages 413-426, April.
    12. Herriges, Joseph A. & Phaneuf, Daniel J., 1999. "Controlling for Correlation Across Choice Occasions and Sites in a Repeated Mixed Logit Model of Recreation Demand," Western Region Archives 321717, Western Region - Western Extension Directors Association (WEDA).
    13. Andrés M. García & Inés Santé & Xurxo Loureiro & David Miranda, 2020. "Spatial Planning of Green Infrastructure for Mitigation and Adaptation to Climate Change at a Regional Scale," Sustainability, MDPI, vol. 12(24), pages 1-22, December.
    14. Gürlük, Serkan & Ward, Frank A., 2009. "Integrated basin management: Water and food policy options for Turkey," Ecological Economics, Elsevier, vol. 68(10), pages 2666-2678, August.
    15. Lienhoop, Nele & Ansmann, Till, 2011. "Valuing water level changes in reservoirs using two stated preference approaches: An exploration of validity," Ecological Economics, Elsevier, vol. 70(7), pages 1250-1258, May.
    16. Christina W. Lopez & Madeline T. Wade & Jason P. Julian, 2023. "Nature–Human Relational Models in a Riverine Social–Ecological System: San Marcos River, TX, USA," Geographies, MDPI, vol. 3(2), pages 1-49, March.
    17. Klein, Thomas Michael & Drobnik, Thomas & Grêt-Regamey, Adrienne, 2016. "Shedding light on the usability of ecosystem services–based decision support systems: An eye-tracking study linked to the cognitive probing approach," Ecosystem Services, Elsevier, vol. 19(C), pages 65-86.
    18. Caroline Roussy & Aude Ridier & Karim Chaïb, 2014. "Adoption d’innovations par les agriculteurs : rôle des perceptions et des préférences," Post-Print hal-01123427, HAL.
    19. Pang, Arwin, 2017. "Incorporating the effect of successfully bagging big game into recreational hunting: An examination of deer, moose and elk hunting," Journal of Forest Economics, Elsevier, vol. 28(C), pages 12-17.
    20. Swait, Joffre & Adamowicz, Wiktor, 2001. "Choice Environment, Market Complexity, and Consumer Behavior: A Theoretical and Empirical Approach for Incorporating Decision Complexity into Models of Consumer Choice," Organizational Behavior and Human Decision Processes, Elsevier, vol. 86(2), pages 141-167, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlands:v:8:y:2019:i:5:p:73-:d:226108. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.