Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Using Volunteered Geographic Information and Nighttime Light Remote Sensing Data to Identify Tourism Areas of Interest
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.
Using Volunteered Geographic Information and Nighttime Light Remote Sensing Data to Identify Tourism Areas of Interest
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.
Using Volunteered Geographic Information and Nighttime Light Remote Sensing Data to Identify Tourism Areas of Interest
Bidur Devkota (Autor:in) / Hiroyuki Miyazaki (Autor:in) / Apichon Witayangkurn (Autor:in) / Sohee Minsun Kim (Autor:in)
2019
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
Metadata by DOAJ is licensed under CC BY-SA 1.0
Volunteered Geographic Information
| Online Contents | 2015
Exploring Massive Volunteered Geographic Information for Geographic Knowledge Discovery
| BASE | 2010
Call for Papers — "Volunteered Geographic Information"
Online Contents | 2009
Volunteered geographic information, urban forests, & environmental justice
| Online Contents | 2014
Volunteered Geographic Information: A Bicycling Enthusiast Perspective
| Online Contents | 2011