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At the time of writing, the world is facing the new coronavirus pandemic, which has been declared one of the most dangerous disasters of the 21st century. All nations and communities have applied many countermeasures to control the spread of the epidemic. In terms of countermeasures, lockdowns and reductions of social activities are meant to flatten the curve of infection. Nevertheless, to date, there has been no evaluation of the effectiveness of these methods. Thus, the present study aims to interpret the change in the population density of Sapporo city in the emergency's period declaration using big data obtained from mobile spatial statistics. The results indicate that, in the time of refraining from traveling, the city's residents have been more likely to stay home and less likely to travel to the center area. This has led to a decrease of up to 90% of the population density in crowded areas. The study's outcomes partly explain the statement of reducing 70%–80% of contact between people in line with the purpose of the emergency declaration. Moreover, these findings establish the primary step for further analysis of estimating the efficiency of policy in controlling the epidemic.

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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.

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