How to describe spatiotemporal patterns of moving objects: a new classification framework
Jianbo Tang, Min Deng, Ju Peng, Zhiyuan Hu, Xingxiang Jiang, Jianbing Xiang, Xia Ning, Wenzhe Zhao · International Journal of Geographical Information Systems · 2025
Rapid advances in positioning and monitoring technologies have significantly enhanced the ability to track dynamic moving objects for studying movement patterns. As an emerging field in spatiotemporal data mining, a well-established taxonomy of movement patterns facilitates tasks that mine movement patterns. In this study, we proposed a novel classification framework with the 5W1H1R (who, what, when, where, why, how, and relationships) principle and a bottom-up multi-level cognitive model to support the taxonomy of movement patterns. Guided by first principles thinking and combinatorics, we differentiated basic patterns categorized along spatial, temporal, and motion attribute dimensions from compound patterns composed of these basic patterns. We summarized five key constraints to refine movement patterns over recently studied pattern types. We validated our framework in three domains and compared it with four existing frameworks in five aspects. The results demonstrate the broader coverage, extensibility, and adaptability of the framework. Our classification framework can adapt to various moving objects, application domains, and movement data at different scales and resolutions. It can serve as a conceptual and ontological foundation for guiding the mining and analysis of movement patterns and, especially, for developing models to detect multimodal movement patterns.