Regional Ecosystems Driven by Mobile Big Data/Services
From the perspectives of spatial informatics and mobile computing, this research aims to develop and implement IT technologies that help re-evaluate the diversity, appeal, and civic pride of local communities. In particular, as rural areas face population decline, sustainable community development increasingly depends on collaboration among residents, visitors, and local organizations. This study explores methodologies for building a people-centered information infrastructure for community development, utilizing AI and data science to integrate and leverage local history and wisdom, while also facilitating human-to-human collaboration throughout the process.
Walking tourism is gaining popularity across the world, but evaluating whether visitors are truly satisfied with a planned route is difficult. Questions such as “Where do tourists stop?” or “Which places do they pass without interest?” are hard to answer objectively. Traditional surveys require significant cost and effort, making continuous improvement of tourist routes challenging.
📊 Mobile Sensing for Context-Aware Tourism Analytics
This study leverages sensor data automatically collected by smartphones to build a sustainable improvement cycle for tourism content. By combining GPS traces with accelerometer data (walking vs. stopping), GPS accuracy information (indoor vs. outdoor estimation), and app interaction logs (photo-taking events), our system automatically infers the context of tourist behavior—where visitors are and what they are doing. Using these inferred contexts, we generate density maps to analyze overall movement patterns and provide quantitative evaluation scores that measure how well actual tourist behavior aligns with planned routes. This enables continuous, data-driven refinement of walking tourism experiences.
Many navigation and sightseeing apps automatically play audio such as “You are now near X” when you approach a landmark. This push-based location service relies on geofencing, which triggers content when users cross virtual boundaries on a map. Designing geofences, however, is challenging: GPS accuracy varies, tourists move unpredictably, and service goals differ. Manual design cannot fully accommodate these factors.
📍 Data-Driven Optimization for Tourist Guidance
In this study, we propose a data-driven approach to automatically generate geofence parameters using tourist behavior data. Machine learning techniques identify how users interact with sightseeing spots—such as entering a building versus viewing it from outside—while evolutionary computation refines geofence placement to trigger audio where many users naturally pass. This combination enables adaptive, efficient, and context-aware geofence design, improving the reliability and relevance of location-based audio guides across diverse tourism scenarios.
Conventional POI (Point-of-Interest)–based audio tours often play guidance only at specific spots. In many regions—especially where prominent landmarks are sparse—this leads to long stretches of silence while users walk between points. If visitors deviate from the predefined route, the narrative breaks, and the overall experience loses coherence.
🎙️ Turning Every Step Into a Story
Our research introduces Onsite Radio, a generative audio tour system that transforms the entire act of walking through a city into a continuous narrative. The system combines hierarchical geofencing with large language models to provide context-aware, seamless storytelling. Hierarchical geofencing is a location-recognition algorithm capable of handling multiple spatial scales at once—such as entire districts, individual streets, or specific buildings. Just as an experienced tour guide adjusts their commentary depending on what is around the bus at any moment—explaining a nearby landmark when it appears, or shifting to broader historical context when there is nothing particular in sight—the system can smoothly switch the geographic scale of its commentary based on the user’s current position. Building on this spatial understanding, generative AIs draw from local archives of history and culture to produce natural, conversational audio guidance in real time. By generating narration that adapts to the user’s movement patterns and the changing environment, the system creates an immersive storytelling experience in which the city itself seems to speak.