Extraction of Tourist Destinations and Comparative Analysis of Preferences Between Foreign Tourists and Domestic Tourists on the Basis of Geotagged Social Media Data
1
Graduate School of Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo, Tokyo 113-8656, Japan
2
Department of Computer Science and Engineering, Toyohashi University of Technology, 1-1 Hibarigaoka, Tempaku-cho, Toyohashi, Aichi 441-8580, Japan
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2018, 7(3), 99; https://doi.org/10.3390/ijgi7030099
Received: 29 January 2018 / Revised: 28 February 2018 / Accepted: 12 March 2018 / Published: 13 March 2018
(This article belongs to the Special Issue Geospatial Big Data and Urban Studies)
Inbound tourism plays an important role in local economies. To stimulate local economies, it is necessary to attract foreign tourists to various areas of a country. This research aims to develop a method of extracting the locations of tourist destinations in a country and to understand what characteristics foreign tourists expect of areas near tourist attractions compared with what domestic tourists expect. In this paper, a tourist destination is defined as a small area that has places of interests for tourists such as historic sites, theme parks, hotels, and restaurants. The methods proposed in this paper are applied to data acquired from Twitter and Foursquare in Japan. The proposed method successfully extracts the locations of tourist destinations and characterizes those locations based on the points of interest in the neighborhood. The results indicate that foreign tourists who come to Japan expect nightlife spots (bars, nightclubs, etc.) to be located in the neighborhood of tourist destinations, in contrast to the expectations of domestic tourists. The proposed methods are applicable to not only Japan, but to any country.
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Keywords:
tourism; human mobility; geotagged data
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MDPI and ACS Style
Maeda, T.N.; Yoshida, M.; Toriumi, F.; Ohashi, H. Extraction of Tourist Destinations and Comparative Analysis of Preferences Between Foreign Tourists and Domestic Tourists on the Basis of Geotagged Social Media Data. ISPRS Int. J. Geo-Inf. 2018, 7, 99.
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