Exploring Immersive Analytics for Spatiotemporal Patterns: A Case Study on Origin-Destination Data

Abstract Immersive analytics is an emerging field that enhances the visual analysis of big geodata through large displays and natural interactions. However, existing immersive geovisualizations primarily focus on showing original geographic locations of data, while complex data patterns, such as clusters and networks, remain understudied. In this case study, we enhance an immersive analytical interface by integrating data mining methods, including automated stop detection and descriptive statistics, for origin-destination data. This approach deepens the analytical capabilities of immersive visualizations, enabling more effective pattern recognition. Our prototype shows the potential of combining immersive analytics and data mining for advanced spatiotemporal data exploration.