Using Volunteered Geographic Information and Nighttime Light Remote Sensing Data to Identify Tourism Areas of Interest

Bidur Devkota, Hiroyuki Miyazaki, Apichon Witayangkurn and Sohee Minsun Kim
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Bidur Devkota: School of Engineering and Technology, Department of Information and Communication Technologies, Asian Institute of Technology, Post Box No 4, Pathumthani 12120, Thailand
Hiroyuki Miyazaki: School of Engineering and Technology, Department of Information and Communication Technologies, Asian Institute of Technology, Post Box No 4, Pathumthani 12120, Thailand
Apichon Witayangkurn: School of Engineering and Technology, Department of Information and Communication Technologies, Asian Institute of Technology, Post Box No 4, Pathumthani 12120, Thailand
Sohee Minsun Kim: School of Environment, Resources, and Development, Department of Development and Sustainability, Asian Institute of Technology, Post Box No 4, Pathumthani 12120, Thailand

Sustainability, 2019, vol. 11, issue 17, 1-29

Abstract: Easy, economical, and near-real-time identification of tourism areas of interest is useful for tourism planning and management. Numerous studies have been accomplished to analyze and evaluate the tourism conditions of a place using free and near-real-time data sources such as social media. This study demonstrates the potential of volunteered geographic information, mainly Twitter and OpenStreetMap, for discovering tourism areas of interest. Active tweet clusters generated using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm and building footprint information are used to identify touristic places that ensure the availability of basic essential facilities for travelers. Furthermore, an investigation is made to examine the usefulness of nighttime light remotely sensed data to recognize such tourism areas. The study successfully discovered important tourism areas in urban and remote regions in Nepal which have relatively low social media penetration. The effectiveness of the proposed framework is examined using the F1 measure. The accuracy assessment showed F1 score of 0.72 and 0.74 in the selected regions. Hence, the outcomes of this study can provide a valuable reference for various stakeholders such as tourism planners, urban planners, and so on.

Keywords: social media; Twitter; tourism; volunteered geographic information; OpenStreetMap; nighttime light remote sensing (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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