Association Rule Mining Tourist-Attractive Destinations for the Sustainable Development of a Large Tourism Area in Hokkaido Using Wi-Fi Tracking Data

Tosporn Arreeras, Mikiharu Arimura, Takumi Asada and Saharat Arreeras
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Tosporn Arreeras: Division of Sustainable and Environmental Engineering, Muroran Institute of Technology, Muroran 050-8585, Japan
Mikiharu Arimura: Division of Sustainable and Environmental Engineering, Muroran Institute of Technology, Muroran 050-8585, Japan
Takumi Asada: Division of Sustainable and Environmental Engineering, Muroran Institute of Technology, Muroran 050-8585, Japan
Saharat Arreeras: Division of Sustainable and Environmental Engineering, Muroran Institute of Technology, Muroran 050-8585, Japan

Sustainability, 2019, vol. 11, issue 14, 1-17

Abstract: The rise of radiofrequency scanner technology has led to its potential application in the observation of people’s movements. This study used a Wi-Fi scanner device to track tourists’ traveling behavior in Hokkaido’s tourism area, which occupies a large region that features a unique natural landscape. Inbound tourists have significantly increased in recent years; thus, tourism’s sustainability is considered to be important for maintaining the tourism atmosphere in the long term. Using internet-enabled technology to conduct extensive area surveys can overcome the limitations imposed by conventional methods. This study aims to use digital footprint data to describe and understand traveler mobility in a large tourism area in Hokkaido. Association rule mining (ARM)—a machine learning methodology—was performed on a large dataset of transactions to identify the rules that link destinations visited by tourists. This process resulted in the discovery of traveling patterns that revealed the association rules between destinations, and the attractiveness of the destinations was scored on the basis of visiting frequency, with both inbound and outbound movements considered. A visualization method was used to illustrate the relationships between destinations and simplify the mathematical descriptions of traveler mobility in an attractive tourism area. Hence, mining the attractiveness of destinations in a large tourism area using an ARM method integrated with a Wi-Fi mobility tracking approach can provide accurate information that forms a basis for developing sustainable destination management and tourism policies.

Keywords: destination; Wi-Fi tracking data; massive area; tourist movement; survey technology; movement patterns; association rule mining; sustainability development (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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