From Pattern Discovery to Pattern Interpretation in Movement Data

The widespread use of positioning technologies ranging from GSM and GPS to WiFi devices, tend to produce large-scale datasets of trajectories, representing the movement of travelling entities. Several applications may benefit from mining such datasets. However, mining results only become truly useful and meaningful for the end user when the intrinsically complex nature of the movement data in terms of context is taken into account during the discovery process. For this reason we propose a pattern interpretation framework that consists of three main steps, namely, pattern discovery, semantic annotation and pattern analysis. The framework supports the understanding of movement patterns that were extracted using some trajectory mining algorithm. To demonstrate the feasibility and effectiveness of the framework, we performed an experiment with the Dwingelderveld National Park (DNP) dataset, which contains records of observations on the movement of visitors in the park. As a result, some forms of interaction, such as certain groups of visitors following the most popular path in the park, were inferred after completing the steps of the framework.