How Can Big Data Support Smart Scenic Area Management? An Analysis of Travel Blogs on Huashan
Jun Shao (),
Xuesong Chang () and
Alastair M. Morrison ()
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Jun Shao: School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China
Xuesong Chang: Department of Tourism and Scenic Area Planning & Research, Beijing Tsinghua Tongheng Urban Planning & Design Institute, Beijing 100085, China
Alastair M. Morrison: School of Hospitality and Tourism Management, Purdue University, West Lafayette, IN 47907, USA
Sustainability, 2017, vol. 9, issue 12, 1-17
Data from travel blogs represent important travel behavior and destination resource information. Moreover, technological innovations and increasing use of social media are providing accessible ‘big data’ at a low cost. Despite this, there is still limited big data analysis for scenic tourism areas. This research on Huashan (Mount Hua, China) data-mined user-contributed travel logs on the Mafengwo and Ctrip websites. Semantic analysis explored tourist movement patterns and preferences within the scenic area. GIS provided a visual distribution of blogger origins. The relationship between Huashan and adjoining tourism areas revealed a multi-destination pattern of tourist movements. Emotional analysis indicated tourist satisfaction levels, while content analysis explored more deeply into dissatisfying aspects of tourist experiences. The results should provide guidance for scenic areas in destination planning and design.
Keywords: travel blogs; big data; smart destination management; scenic areas; Huashan; China; data mining; GIS (search for similar items in EconPapers)
JEL-codes: Q Q0 Q2 Q3 Q5 Q56 O13 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:9:y:2017:i:12:p:2291-:d:122320
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