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.

As the population is shrinking in many municipalities in Japan, one of its effects is the vacant house crisis. The rise of empty houses profoundly affects the city’s society and economy, e.g., property value reduction, increased crime rate, poor sanitation, and housing market stagnation. To better understand the mechanism of the vacant house crisis, the present study proposes to examine the determinants of housing vacancy spatial distribution with the case study of the city of Sapporo. The results highlight the severe vacant cluster in the central city, which would seem to link to the disequilibrium housing market rather than the urban decline. Regarding vacancy determinants, demographic features were the most influential factors, followed by housing and neighborhood characteristics. Specifically, the vacancy correlated strongly with a high density of single households, children, the elderly (in the center), and a high share of offices. The surplus in housing supply and the inelasticity in housing structures also affected the vacancy significantly. On the contrary, a high percentage of private property, household ownership, and the elderly (in suburban) would reduce the vacancy. For other facilities, clinics, parking, public transportation, and educational institutions had a medium effect on the vacancy. Finally, the influence factors varied, across city areas, in magnitude and direction. These outcomes would be helpful for decision-making to alleviate the rise of vacant houses and their effect on the urban area.

Post-disaster waste management is one of the most crucial tasks in the recovery phase of the disaster cycle, and it was created to assist affected communities in returning to a stable state following a disaster. To develop an efficient post-disaster waste management strategy, this study presents a multi-objective two-stage stochastic mixed integer linear programming model for post-disaster waste management. The proposed mathematical model was developed based on a mixed strategy of on-site and off-site waste separation in the supply chain. This study aims to minimize not only the total cost and the environmental impact to provide waste flow decisions and choose collection and separation sites, recycling sites, landfill sites, and incineration sites throughout the supply chain under the uncertain situation. To solve a multi-objective problem, a normalized weighted sum method is used to find the solution. A numerical case based on realistic data is presented to validate and verify the proposed model. Based on the numerical example, the results demonstrated that the implementation of the mixed strategy for waste separation with the consideration of uncertain situations can reduce the total cost, balance the environmental impact, and determine the unexpected situation in the post-disaster waste supply chain efficiently.

Emergency service facilities play a pivotal role in mitigating the impact of fire disasters in urban areas. This research article delves into the critical aspects of analyzing service coverage for emergency facilities in relation to fire disaster risk in Chiang Rai city—a strategic hub in northern Thailand. Focusing on fire disaster risk merchandise and shops, categorized by the type of hazardous materials they store and sell, this study leverages facility location data obtained through web scraping from Google Maps. Utilizing spatial analysis and Geographic Information Systems (GISs), this research evaluates the reachability of emergency services, assessing travel times and coverage efficiency. The findings reveal significant disparities, particularly within the critical 3 min response window, highlighting the need for strategic improvements. This study offers actionable insights for urban planners and policymakers, advancing the integration of spatial technology in urban disaster management to enhance public safety and resilience.