Course Schedule

Veranstaltungen von M.Sc. Simon Thomsen


Lehrveranstaltungen

Introduction to Spatial analysis with GIS (Seminar)

Dozent/in: Simon Thomsen

Termin:
 wöchentlich | Freitag | 10:00 - 14:00 | 21.10.2022 - 02.12.2022 | W 130a Edulab
wöchentlich | Freitag | 10:00 - 14:00 | 21.10.2022 - 02.12.2022 | W 130b Edulab

Inhalt: Sustainability challenges often require analysis and knowledge of spatial data like land-use or patterns and networks of infrastructure. GIS tools offer both mapping and data analysis options and are widely used in science and application. In this course, you will get to know QGIS, an open source software used in both science and the private sector. The seminar addresses the theoretical background of GIS and spatial data as well as its practical application in the software. Students will learn about the following topics: -How to integrate vector and raster layers in GIS -Manipulation of vector data -Creation of professional maps in QGIS -Geoprocessing with vector data in a multi-criterion analysis -Geoprocessing of raster data -Statistics and regressions in a spatial context The software used for the course is QGIS, which is freely available for all operating systems. This course can serve as the basis for the advanced GIS course in the second half of the semester.

Advanced spatial analysis methods with GIS (Seminar)

Dozent/in: Simon Thomsen

Termin:
 Einzeltermin | Fr, 06.01.2023, 09:00 - Fr, 06.01.2023, 17:00 | W 130a Edulab
Einzeltermin | Fr, 06.01.2023, 09:00 - Fr, 06.01.2023, 17:00 | W 130b Edulab
Einzeltermin | Fr, 13.01.2023, 09:00 - Fr, 13.01.2023, 17:00 | W 130a Edulab
Einzeltermin | Fr, 13.01.2023, 09:00 - Fr, 13.01.2023, 17:00 | W 130b Edulab
Einzeltermin | Fr, 20.01.2023, 09:00 - Fr, 20.01.2023, 17:00 | W 130a Edulab
Einzeltermin | Fr, 20.01.2023, 09:00 - Fr, 20.01.2023, 17:00 | W 130b Edulab

Inhalt: In this advanced course for spatial analysis, students will get in touch with more complex spatial problems. These will also be more closely linked to real world socio-environmental problems. As examples, we aim to deal with spatial interpolations, habitat fragmentation, species distribution modelling and basics of remote sensing and land cover change detection. Apart from the application of these methods, we will also discuss their theoretical background and closely look at the uncertainties which come along with them. Different softwares will be used in this context, such as GIS (QGIS) as well as R and Google Earth Engine. It is therefore necessary that students have completed an introductory course to GIS and spatial data. Further knowledge of R is desireable, but not necessary.