Journal of Environmental Impact Assessment (환경영향평가)

Korean Society of Environmental Impact Assessment (한국환경영향평가학회)
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Identification of Visitation Density and Critical Management Area Regarding Marine Spatial Planning: Applying Social Big Data

해양공간계획 수립을 위한 방문밀집도 및 중점관리지역 규명: 소셜 빅데이터를 활용하여

  • Received : 2020.02.12
  • Accepted : 2020.04.03
  • Published : 2020.04.30
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Abstract

Marine Spatial Planning is an emerging strategy that promoting sustainable development at coastal and marine areas based on the concept of ecosystem services. Regarding its methodology, usage rate of resources and its impact should be considered in the process of spatial planning. Particularly, considering the rapid increase of coastal tourism, visitation pattern is required to be identified across coastal areas. However, actions to quantify visitation pattern have been limited due to its required high cost and labor for conducting extensive field-study. In this regard, this study aimed to pose the usage of social big data in Marine Spatial Planning to identify spatial visitation density and critical management zone throughout coastal areas. We suggested the usage of GPS information from Flickr and Twitter, and evaluated the critical management zone by applying spatial statistics and density analysis. This study's results clearly showed the coastal areas having relatively high visitors in the southern sea of South Korea. Applied Flickr and Twitter information showed high correlation with field data, when proxy excluding over-estimation was applied and appropriate grid-scale was identified in assessment approach. Overall, this study offers insights to use social big data in Marine Spatial Planning for reflecting size and usage rate of coastal tourism, which can be used to designate conservation area and critical zones forintensive management to promote constant supply of cultural services.

해양공간계획은 생태계서비스 개념에 기초하며 최근 연안 및 해양지역의 지속가능한 개발을 촉진하기 위한 방안으로 주목받고 있다. 정책개발자는 해양공간계획 개념에 기반을 둔 의사결정을 위해 각 해역별 자원의 이용현황과 그 특성을 규명할 필요가 있다. 특히, 해변관광은 연안에서 이루어지는 자원 이용활동 중 가장 빠르게 성장하는 활동이며 다수의 문화서비스 수혜를 유도하여 중요하다. 그러나 해변관광의 규모와 방문현황의 공간적 특성을 광역단위로 평가할 수 있는 정보가 부재하며, 현장조사의 경우 높은 비용과 노동력이 요구되어 적용이 어렵다. 그러므로 본 연구는 신규 대안으로 소셜 빅데이터의 해양공간계획 적용방안을 제안하고 트위터, 플리커 정보에 기초한 중점관리지역 도출 방안을 제시하였다. 본 연구는 남해 연안육역 일대를 대상으로 수행되었으며 소셜미디어에서 추출한 플리커, 트위터 정보를 대상으로 과다추정 방지 전처리, 적합 격자단위 규명 과정을 통해 광역단위 방문밀집도를 도출하였다. 더불어 공간통계분석 및 밀도분석을 통해 남해 일대의 집중관리가 필요한 연안육역 구역을 제시하였다. 본 연구는 중점관리구역, 보전구역 지정 등 해양공간계획의 수립과정에서 해변관광 및 문화서비스 규모의 고려를 위한 시사점을 제공한다.

Keywords

References

  1. Chan KM, Satterfield T and Goldstein J. 2012. Rethinking ecosystem services to better address and navigate cultural values, Ecological economics, 74: 8-18. https://doi.org/10.1016/j.ecolecon.2011.11.011
  2. Earle SA. 1996. Sea change: a message of the oceans, Ballantine Books.
  3. European Commission. 2014. A European Strategy for more Growth and Jobs in Coastal and Maritime Tourism.
  4. Fisher DM, Wood SA, Roh YH, and Kim CK. 2019. The Geographic Spread and Preferences of Tourists Revealed by User-Generated Information on Jeju Island, South Korea, Land, 8(5): 73. https://doi.org/10.3390/land8050073
  5. Getis A. 1996. Local spatial statistics: an overview. Spatial analysis: Modelling in a GIS environment, 261-277.
  6. Guerry AD, Ruckelshaus MH, Arkema KK, Bernhardt JR, Guannel G, Kim CK and Wood SA. 2012. Modeling benefits from nature: using ecosystem services to inform coastal and marine spatial planning, International Journal of Biodiversity Science, Ecosystem Services & Management, 8(1-2): 107-121. https://doi.org/10.1080/21513732.2011.647835
  7. Hall CM. 2001. Trends in ocean and coastal tourism: the end of the last frontier?, Ocean & coastal management, 44(9-10): 601-618. https://doi.org/10.1016/S0964-5691(01)00071-0
  8. Heikinheimo V, Minin ED, Tenkanen H, Hausmann A, Erkkonen J and Toivonen T. 2017. User-generated geographic information for visitor monitoring in a national park: A comparison of social media data and visitor survey, ISPRS International Journal of Geo-Information, 6(3): 85. https://doi.org/10.3390/ijgi6030085
  9. Kim YJ, Kim CK, Lee DK, Lee HW and Andrada RIT. 2019. Quantifying naturebased tourism in protected areas in developing countries by using social big data, Tourism Management, 72: 249-256. https://doi.org/10.1016/j.tourman.2018.12.005
  10. Kim YJ, Lee DK and Kim CK. 2020. Spatial tradeoff between biodiversity and naturebased tourism: Considering mobile phonedriven visitation pattern, Global Ecology and Conservation, 21: e00899. https://doi.org/10.1016/j.gecco.2019.e00899
  11. Kurniawan F, Adrianto L, Bengen DG and Prasetyo LB. 2016. Vulnerability assessment of small islands to tourism: The case of the Marine Tourism Park of the Gili Matra Islands, Indonesia, Global ecology and conservation, 6: 308-326. https://doi.org/10.1016/j.gecco.2016.04.001
  12. Li J, Xu L, Tang L, Wang S and Li L. 2018. Big data in tourism research: A literature review, Tourism Management, 68: 301-323. https://doi.org/10.1016/j.tourman.2018.03.009
  13. Noble M, Harasti D, Pittock J and Doran B. 2019. Understanding the spatial diversity of social uses, dynamics, and conflicts in marine spatial planning, Journal of environmental management, 246: 929-940. https://doi.org/10.1016/j.jenvman.2019.06.048
  14. Orams M. 2002. Marine tourism: development, impacts and management, Routledge.
  15. Papageorgiou M. 2016. Coastal and marine tourism: A challenging factor in Marine Spatial Planning, Ocean & coastal management, 129: 44-48. https://doi.org/10.1016/j.ocecoaman.2016.05.006
  16. Schagner JP, Maes J, Brander L, Paracchini ML, Hartje V and Dubois G. 2017. Monitoring recreation across European nature areas: A geo-database of visitor counts, a review of literature and a call for a visitor counting reporting standard, Journal of outdoor recreation and tourism, 18: 44-55. https://doi.org/10.1016/j.jort.2017.02.004
  17. Sessions C, Wood SA, Rabotyagov S and Fisher DM. 2016. Measuring recreational visitation at US National Parks with crowd-sourced photographs, Journal of environmental management, 183: 703-711. https://doi.org/10.1016/j.jenvman.2016.09.018
  18. Sonter LJ, Watson KB, Wood SA and Ricketts TH. 2016. Spatial and temporal dynamics and value of nature-based recreation, estimated via social media, PLoS one, 11(9).
  19. Steven R, Castley JG and Buckley R. 2013. Tourism revenue as a conservation tool for threatened birds in protected areas, PloS one, 8(5).
  20. Strickland-Munro J, Kobryn H, Brown G and Moore SA. 2016. Marine spatial planning for the future: Using Public Participation GIS (PPGIS) to inform the human dimension for large marine parks, Marine Policy, 73: 15-26. https://doi.org/10.1016/j.marpol.2016.07.011
  21. Van Zanten BT, Van Berkel DB, Meentemeyer RK, Smith JW, Tieskens KF and Verburg PH. 2016. Continental-scale quantification of landscape values using social media data, Proceedings of the National Academy of Sciences, 113(46): 12974-12979. https://doi.org/10.1073/pnas.1614158113
  22. Wood SA, Guerry AD, Silver JM. and Lacayo M. 2013. Using social media to quantify naturebased tourism and recreation, Scientific reports, 3: 2976. https://doi.org/10.1038/srep02976
  23. Tourism Knowledge & Information System [Internet]. Statistics on major tourist attractions; [cited: 2020 Feb 12]. Available from: www.tour.go.kr