Extraction of Tourist Destinations and Comparative Analysis of Preferences Between Foreign Tourists and Domestic Tourists on the Basis of Geotagged Social Media Data

Abstract

Abstract: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.

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

“…Though there have been concerns regarding information bias and lack of standard quality control, several studies have empirically revealed that VGI is of equally good quality as authoritative data [8,28,29]. Several researchers have used VGI resources including geo-tagged contents (e.g., tweets and photos) [16][17][18]30], check-in data [18,30], OSM [31] and so on. VGI has been used in a variety of applications such as tourism studies [16][17][18]30], urban studies [19,25,32,33], land use and land cover detection [11,34] and mobility analysis [35].…”

“…Moreover, this proposed algorithm applied local maximum to extract attractive footprints to deal with the problem in dense areas. Maeda et al extracted the location of tourist destinations in Japan using geo-tagged tweets via attractiveness and originality of each place of interest [18]. Attractiveness was computed using DBSCAN clustering and gravity modeling.…”

“…Therefore, the positional changes in the tweet points may affect the formation of the clusters. Many previous studies (e.g., [17,18,50,58]) which use manually selected static values for the clustering parameters may not adapt well in such cases. However, our proposed approach works well even in such scenarios because the parameters for clustering algorithm are estimated by using objective functions StrictParameters and MaxParameters.…”

“…In recent years, many studies have proposed various ways to discover tourism destinations and activities. Geo-tagged data from online social media have been extensively used in studies locating popular touristic sites [16][17][18][19][20], map tourist behaviors [21,22], comparing domestic and foreign tourists [18,21] and discovering obscure sightseeing locations [23]. This is possible because more and more data, i.e., digital footprint, is being generated by the unprecedented use of GPS-equipped smart devices by the users (both residents and tourists) and uploaded to the Internet as geo-tagged information.…”

Exploring Group Movement Pattern through Cellular Data: A Case Study of Tourists in Hainan

“…In addition, the Twitter and Foursquare data were also widely used in the travel behaviors analysis. For example, Maeda, T. et al [33,34] extracted the different preferences of tourists from different countries by characterizing each location. And Sun, Y. et al [35] empirically investigated the travel and activity patterns of active local Foursquare users in New York City.…”