The seminar can only be taken together with the exercise part!
This course will introduce advanced analysis methods for typical spatio-temporal data sets such as tracking data, time series of remotely sensed images and/or data coming from monitoring networks with fixed/mobile ground sensors. The advanced analysis methods include selected stochastic, deterministic and combined modelling approaches, and will touch upon the visual analytics framework. Special attention is paid to uncertainty quantification and error propagation in the analysis process (data, model, output analysis and visualisation). The practical exercises are given with the open source statistical environment R. The students are expected to analyse a spatio-temporal dataset. The assignment includes the following tasks:
● Formulate a research question and hypothesis
● Identify an appropriate spatio-temporal dataset
● Apply one or more of the advanced methods presented during the lecture part of the course
● Reproducible analysis, i.e. implement all analysis steps using a script language like R, and a document system like Sweave
● Summarise and visualise results
● Discuss most important findings with regard to research question.
- Lehrende/r: Edzer Pebesma