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290700 VU Geo-Data and Geo-Data Management (2025W)
Continuous assessment of course work
Labels
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).
- Registration is open from Mo 08.09.2025 08:00 to Mo 22.09.2025 08:00
- Deregistration possible until Fr 31.10.2025 23:59
Details
max. 30 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
No class on 08.01.2026
- Thursday 09.10. 14:30 - 17:30 Multimedia Mapping-Labor, NIG 1.Stock C0110
- Thursday 16.10. 14:30 - 17:30 Multimedia Mapping-Labor, NIG 1.Stock C0110
- Thursday 23.10. 14:30 - 17:30 Multimedia Mapping-Labor, NIG 1.Stock C0110
- Thursday 30.10. 14:30 - 17:30 Multimedia Mapping-Labor, NIG 1.Stock C0110
- Thursday 13.11. 14:30 - 17:30 Multimedia Mapping-Labor, NIG 1.Stock C0110
- Thursday 20.11. 14:30 - 17:30 Multimedia Mapping-Labor, NIG 1.Stock C0110
- Thursday 27.11. 14:30 - 17:30 Multimedia Mapping-Labor, NIG 1.Stock C0110
- Thursday 11.12. 14:30 - 17:30 Multimedia Mapping-Labor, NIG 1.Stock C0110
- Thursday 18.12. 14:30 - 17:30 Multimedia Mapping-Labor, NIG 1.Stock C0110
- Thursday 15.01. 14:30 - 17:30 GIS-Labor Geo NIG 1.OG
- Thursday 22.01. 14:30 - 17:30 Multimedia Mapping-Labor, NIG 1.Stock C0110
Information
Aims, contents and method of the course
This course offers an in-depth exploration of geospatial data and database management. Lectures will cover foundational concepts, such as vector and raster data, spatial data integration, and relational and spatial databases. These topics aim to cultivate an understanding of spatial thinking, focusing on key distinctions such as geometry versus topology and object-based versus field-based data modeling. Hands-on practical sessions will primarily utilize Python and PostgreSQL. Through these labs, students will develop proficiency with essential Python libraries used for spatial data, such as Shapely and GeoPandas, as well as gain experience writing and executing SQL queries. Labs will incorporate volunteered geographic information (VGI) data sources, such as OpenStreetMap.
Assessment and permitted materials
Participation (10%)
4 quizzes (40%)
1 midterm exam (20%)
1 final exam (30%)
4 quizzes (40%)
1 midterm exam (20%)
1 final exam (30%)
Minimum requirements and assessment criteria
Personal contribution that demonstrates an appropriate understanding of the approaches and methods discussed.100 % - 88 % = 1
87 % - 75 % = 2
74 % - 63 % = 3
62 % - 51 % = 4
Below 51 % = 5
87 % - 75 % = 2
74 % - 63 % = 3
62 % - 51 % = 4
Below 51 % = 5
Examination topics
Foundational topics, spatial thinking, and programming and database skills in geospatial data and data management
Reading list
1. Burrough, P. A., & McDonnell, R. A. (1998). Principles of geographical information systems (2nd ed.). Oxford University Press.
2. Introduction to Python for geographic data analysis (https://pythongis.org)
3. PostgreSQL tutorial (https://www.postgresql.org/docs/current/tutorial.html)
4. Presentation and slides (including references)
2. Introduction to Python for geographic data analysis (https://pythongis.org)
3. PostgreSQL tutorial (https://www.postgresql.org/docs/current/tutorial.html)
4. Presentation and slides (including references)
Association in the course directory
(MK1-W1) (MK2) (MSDS-TS)
Last modified: Tu 07.10.2025 10:07