[논문]지속가능한 자원관리를 위한 섬 지역 관광자원의 공간정보와 소셜미디어 빅데이터 분석 결과를 활용한 격차분석

이성희; 이주경; 손용훈; 김용진

doi:10.7851/ksrp.2024.30.2.013

[국내논문] 지속가능한 자원관리를 위한 섬 지역 관광자원의 공간정보와 소셜미디어 빅데이터 분석 결과를 활용한 격차분석
A Gap Analysis Using Spatial Data and Social Media Big Data Analysis Results of Island Tourism Resources for Sustainable Resource Management 원문보기

농촌계획 : 韓國農村計劃學會誌, v.30 no.2, 2024년, pp.13 - 24

이성희 (경기연구원) , 이주경 (서울대학교 협동과정조경학) , 손용훈 (서울대학교 환경대학원 환경설계학과) , 김용진 (한국섬진흥원)

Abstract ▼ AI-Helper

This study conducts an analysis of social media big data pertaining to island tourism resources, aiming to discern the diverse forms and categories of island tourism favored by consumers, ascertain predominant resources, and facilitate objective decision-making grounded in scientific methodologies. To achieve this objective, an examination of blog posts published on Naver from 2022 to 2023 was undertaken, utilizing keywords such as 'Island tourism', 'Island travel', and 'Island backpacking' as focal points for analysis. Text mining techniques were applied to sift through the data. Among the resources identified, the port emerged as a significant asset, serving as a pivotal conduit linking the island and mainland and holding substantial importance as a focal point and resource for tourist access to the island. Furthermore, an analysis of the disparity between existing island tourism resources and those acknowledged by tourists who actively engage with and appreciate island destinations led to the identification of 186 newly emerging resources. These nascent resources predominantly clustered within five regions: Incheon Metropolitan City, Tongyeong/Geoje City, Jeju Island, Ulleung-gun, and Shinan-gun. A scrutiny of these resources, categorized according to the tourism resource classification system, revealed a notable presence of new resources, chiefly in the domains of 'rural landscape', 'tourist resort/training facility', 'transportation facility', and 'natural resource'. Notably, many of these emerging resources were previously overlooked in official management targets or resource inventories pertaining to existing island tourism resources. Noteworthy examples include ports, beaches, and mountains, which, despite constituting a substantial proportion of the newly identified tourist resources, were not accorded prominence in spatial information datasets. This study holds significance in its ability to unearth novel tourism resources recognized by island tourism consumers through a gap analysis approach that juxtaposes the existing status of island tourism resource data with techniques utilizing social media big data. Furthermore, the methodology delineated in this research offers a valuable framework for domestic local governments to gauge local tourism demand and embark on initiatives for tourism development or regional revitalization.

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