Activating Location-Based Storytelling in a City: Geofence Identification from Crowdsourced Mobile Sensing

Abstract. Location-based storytelling is a key strategy in smart tourism, enabling immersive engagement through narrative content triggered by user movement. This study proposes a data-driven framework for designing enclosure- and viewpoint-geofences that align with storytelling modes, leveraging GPS horizontal-accuracy clustering and motion-sensor noise filtering to detect meaningful spatial engagement zones. We introduce a lightweight decision-tree classifier using cluster duration and motion variability features to distinguish valid indoor stays from transient or noise-induced clusters. In a field experiment with 12 participants in Akita City, our method achieved 94 percent classification accuracy and a 0.96 F₁-score under leave-one-out cross-validation. Furthermore, our qualitative comparisons imply the geofence identifications can outperform baseline techniques such as HDBSCAN and stay point detection. The results demonstrate the practical potential of the proposed approach for context-sensitive geofencing in urban tourism. This framework advances autonomous, adaptive geofencing for enriched tourist experiences.