Participants learn the skills to handle trajectory data, understand their dimensionalities, their metrics, their challenges and limitations but also their potentials. An important learning aim is to develop a base knowledge on which kind of ecological or environmental analyes using trajectory data could be well supplemented by Earth observation data and vice versa. Understanding trajectory data and what is special about it compared to other spatio-temporal data and understanding the applicable methods are key to later-on be able to use trajecotry data of any kind in scientifc work.
This course focuses on the joint analysis of different spatio-temporal data. It introduces (1) methods to process, visualize and analyse spatio-temporal trajectory data such as animal movement data, traffic movement data or other kinds of tracking data and (2) methods to combine Earth observation data such as remote sensing imagery with trajectory data for joint analysis. The course focuses on techniques form both the discrete and the continous time modelling approaches. It uses such to derive and quaintify common trajectory metrics such as sampling frequency or telemetry error, space use, corridors, stopping sites etc. in an automatized manner. The course lays a practical focus on implementing the learned methods with a programming language such as R or Python.