Shape-based methods in mobility data analysis: effectiveness and limitations

Although Mobility Data Analysis (MDA) has been explored for a long time, it still lags behind advancements in other fields. A common issue in MDA is the lack of methods’ standardization and reusability. On the other hand, for instance, in time series analysis, the existing methods are typically general-purpose, and it is possible to apply them across diverse datasets and applications without extensive customization. Still, in MDA, most contributions are ad-hoc and designed to address specific research questions, which limits their generalizability and reusability. Recently, some researchers explored the application of shapelet transform to trajectory data, i.e., extracting discriminatory sub-trajectories from training data to be used as classification features. Unlike current MDA methods, this line of research eliminates the need for feature engineering, greatly improving its ability to generalize. While shapelets on mobility data have shown state-of-the-art performance on public classification datasets, it is still not clear why they work. Are these subtrajectories merely proxies for geographic location, or do they also capture motion dynamics? We empirically show that shapelet-based approaches are a viable alternative to classical methods and flexible enough to solve MDA tasks related solely to trajectory shape, solely to movement dynamics, and those related to both. Additionally, we investigate the problem of Geographic Transferability, showing that such approaches offer a promising starting point for tackling this challenge.