A study for understanding of tourist person trip pattern based on log data of Wi-Fi access points

Abstract

Statistical tourism data are critical to supporting tourism policy decisions. Such data are normally compiled via questionnaires or interviews, which requires both time and money, thus making such data unusable for policy decision-making to occur in real-time. To support real-time policy decision-making, we focused on person trip data for promoting tourism. The city government can reflect such data on tourism events and guidebooks if it has a grasp of tourist person trip patterns. In this paper, we focused on using the log data of Wi-Fi access points available at each tourist spot. Wi-Fi access points have log data that keep track of the timestamps of users connecting and disconnecting from the Wi-Fi. We can visualize the movements of tourists using these data; however, there is bias among Wi-Fi users. Therefore, we must evaluate the Wi-Fi log data to verify they are representative of almost all tourists. More specifically, we evaluated log data for Goto City to determine whether the government's data were equivalent to the log data. We also showed the possibility of visualizing tourist movement history based on our log data.