Essential Information
By focusing on computational approaches to working with geospatial data, you will continue and deepen your understanding of classical GIS workflows.
You will primarily use R as the scripting environment, drawing on your existing programming experience. Script-based workflows are complemented by graphical GIS tools to support the exploration, analysis, and visualization of spatial data. In addition, we will leverage AI and large language models (LLMs) as supporting tools for geospatial problem-solving, coding, and analytical reasoning.
Through emphasizing applied, problem-oriented learning, you will be prepared to handle complex geospatial tasks in environmental and spatial analysis contexts.
Module Contents
Importing and Managing Geospatial Data
- Importing spatial data from diverse sources and formats (vector, raster, databases)
- Geospatial data models and data quality assessment
Manipulating and Analyzing Geospatial Data
- Script-based processing, transformation, and analysis of geospatial datasets
- Integration of scripting workflows with GUI-based GIS tools
Visualization and Communication
- Visualization and interactive exploration of spatial data
- Effective communication of spatial information
Automation, AI, and Applications
- Automation and reproducibility of geospatial workflows
- AI-assisted approaches to geospatial analysis and coding
- Applied case studies in environmental and spatial analysis
Prerequisite Modules
4. Semester: none
3. Semester: Geoinformatik und GIS or similar knowledge is recommended
2. Semester: Daten und Information II
1. Semester: Daten und Information I
Study Information
- This module is part of the minor 'Spatial Data Science', but can also be attended independently, provided the prerequisite modules have been attended.
- The language of instruction is English.
The module descriptions provide further information on the competencies to be achieved, the teaching methods, the assessment requirements, and the attendance policy.