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

Author

Listed:
  • 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
    Center for Spatial Information Science, Tokyo University, Chiba 277-8568, Japan)

  • Apichon Witayangkurn

    (School of Engineering and Technology, Department of Information and Communication Technologies, Asian Institute of Technology, Post Box No 4, Pathumthani 12120, Thailand
    Center for Spatial Information Science, Tokyo University, Chiba 277-8568, Japan)

  • 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)

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.

Suggested Citation

  • Bidur Devkota & Hiroyuki Miyazaki & Apichon Witayangkurn & Sohee Minsun Kim, 2019. "Using Volunteered Geographic Information and Nighttime Light Remote Sensing Data to Identify Tourism Areas of Interest," Sustainability, MDPI, vol. 11(17), pages 1-29, August.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:17:p:4718-:d:262192
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    2. Guillermo Rey Gozalo & Enrique Suárez & Alexandra L. Montenegro & Jorge P. Arenas & Juan Miguel Barrigón Morillas & David Montes González, 2020. "Noise Estimation Using Road and Urban Features," Sustainability, MDPI, vol. 12(21), pages 1-18, November.
    3. Pengpeng Chang & Xueru Pang & Xiong He & Yiting Zhu & Chunshan Zhou, 2022. "Exploring the Spatial Relationship between Nighttime Light and Tourism Economy: Evidence from 31 Provinces in China," Sustainability, MDPI, vol. 14(12), pages 1-22, June.
    4. Pattama Krataithong & Chutiporn Anutariya & Marut Buranarach, 2022. "A Taxi Trajectory and Social Media Data Management Platform for Tourist Behavior Analysis," Sustainability, MDPI, vol. 14(8), pages 1-18, April.

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