Modeling and prediction of moving region trajectories

Data about moving objects is being collected in many different application domains with the help of sensor networks, GPS-enabled devices, and in particular airborne sensors and satellites. Such moving objects often represent not just point-based objects, but rather moving regions like hurricanes, oil-spills, or animal herds. One key application feature users are often interested in is the exploration and prediction of moving object trajectories. While there exist models and techniques that help to predict the movement of moving point objects, no such method for moving regions has been proposed yet. In this paper, we present an approach to model and predict the development of moving regions. Our method not only predicts the trajectory of regions, but also the evolution of a region's spatial extent and orientation. For this, moving regions are modelled using minimum enclosing boxes, and evolution patterns of regions are determined using linear regression and a recursive motion function. We demonstrate the functionality and effectiveness of the proposed technique using real-world sensor data from different application domains.