Decision Tree Analysis of Tourists' Preferences Regarding Tourist Attractions Using Geotag Data from Social Media

Abstract

This research investigates what characteristics foreign tourists expect of areas near tourist attractions in Japan, compared with what domestic tourists expect, in order to find out how to make the areas more attractive to foreign tourists. We develop a framework to use spatio-temporal data from Twitter and Foursquare to show the difference of preferences between foreign tourists and domestic tourists. First we extract the locations of tourist attractions from the data of domestic tourists. Then we characterize each location by using Foursquare's location information that has categories such as restaurants, shops, historic sites, etc. After characterizing each location, we make decision trees that explain what kind of combination of characteristics is important to attract foreign tourists and domestic tourists. Finally, from the results of the difference of preferences between foreign tourists and domestic tourists, we propose solutions to improve the environments of tourist attractions. In this paper, we apply this framework to the data gathered in August 2014 in Japan, and we conclude that foreign tourists expect nightlife spots (bars, nightclubs, etc) of the neighborhoods of tourist attractions whereas domestic tourists do not.