Universität Wien

290006 PS Open GIS and Open Data in Geoinformation (2025S)

3.00 ECTS (2.00 SWS), SPL 29 - Geographie
Continuous assessment of course work
Tu 29.04. 12:30-15:00 GIS-Labor Geo NIG 1.OG

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 35 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Tuesday 04.03. 12:30 - 15:00 GIS-Labor Geo NIG 1.OG
  • Tuesday 11.03. 12:30 - 15:00 GIS-Labor Geo NIG 1.OG
  • Tuesday 18.03. 12:30 - 15:00 GIS-Labor Geo NIG 1.OG
  • Tuesday 25.03. 12:30 - 15:00 GIS-Labor Geo NIG 1.OG
  • Tuesday 01.04. 12:30 - 15:00 GIS-Labor Geo NIG 1.OG
  • Tuesday 08.04. 12:30 - 15:00 GIS-Labor Geo NIG 1.OG
  • Tuesday 06.05. 12:30 - 15:00 GIS-Labor Geo NIG 1.OG
  • Tuesday 20.05. 12:30 - 15:00 GIS-Labor Geo NIG 1.OG

Information

Aims, contents and method of the course

The course provides participants with an in-depth exploration of Open Source software within the realm of Geographic Information Systems (GIS) and the principles of Open Data initiatives. Participants will gain practical skills and knowledge of Open Source GIS tools and leverage Open Data resources effectively. Throughout the course, participants will learn from the fundamental concepts of Open GIS and Open Data to master the complete workflow of geospatial data processing and publication.

Assessment and permitted materials

Course participation (10%)
Assignment (30%)
Quiz (30%)
Final project and presentation (30%)

Minimum requirements and assessment criteria

At the end of the course participants should:
- understand the concepts of Open Source and Open Data
- know about Open source alternatives to commercial products and be able to use them
- work with Open Data and publish processed data
- be able to perform a complete workflow from the data source to the publication on the Web exclusively using Open Source software and Open Data

100 % - 90 % = 1
89 % - 80 % = 2
79 % - 70 % = 3
69 % - 60 % = 4
Below 60 % = 5

Examination topics

Reading list


Association in the course directory

(MK2-PI)

Last modified: We 26.02.2025 16:27