Geoai-Driven Tourism Planning: A Framework And Educational Implications
Abstract
The integration of Geographic Information Systems (GIS) and Artificial Intelligence (AI) has created transformative opportunities for tourism destination planning. This paper presents the GeoAI Tourism Intelligence Framework (GTIF), a conceptual framework integrating geospatial big data, AI algorithms, and spatial analytics to support evidence-based destination planning and management. Drawing upon advances in GeoAI, machine learning, and spatial data science, the framework synthesizes diverse geospatial data sources including satellite imagery, GPS trajectories, and social media data to enable tourist flow prediction, demand forecasting, hotspot analysis, and destination resilience assessment. The paper reviews the evolution of GIS applications in tourism, examines Spatial Decision Support Systems, and evaluates AI integration in tourism research. Additionally, the paper addresses the educational implications of GeoAI for geography and tourism education, proposing curriculum development strategies and pedagogical approaches for preparing the next generation of tourism professionals. The GTIF is structured around three interconnected layers: an Input Layer comprising multi-source geospatial big data, a GeoAI Engine Layer incorporating deep learning and ensemble methods, and a Spatial Analytics and Decision Support Layer delivering actionable intelligence. The paper articulates a future research agenda encompassing explainable AI, privacy-preserving analytics, and human-AI collaboration. The proposed framework contributes to GeoAI by providing a structured approach to intelligent tourism destination planning that balances economic development with environmental sustainability and educational capacity building.
