Students are encouraged to critically analyse the performance of classical, statistical approaches for modelling spatial data under the presence of big and/or high-dimensional data. In this regard, students learn key concept of spatial and spatiotemporal statistics. Furthermore, an own simulation study is performed and described in a seminar paper.
In a first part, important concepts of spatial and spatiotemporal statistics are introduced/repeated. In particular, the focus is on kriging and modelling spatial and spatiotemporal dependence by linear approaches, like autoregressive models. Further, we examine these approaches under the presence are large/big spatial data (incl. data streams) and discuss different approaches for reducing complexity and dimensionality. In the second half of the semester, all students work on a seminar paper assessing the performance of one of the introduced concepts for data with increasing size/complexity/dimensionality. Generally, these papers should include a small simulation study. There are individual obligatory meetings during the second half. The results of the seminar papers are presented in a colloquium.
