Meaningful maps are based on a solid foundation of information. But what does this look like? Data formats such as CSV, JSON or GeoPackage help to store information in a structured way, but how is it best processed? OpenStreetMap offers a wealth of geoinformation, but how can this information be accessed and easily processed?

Beyond applications like QGIS (GIS Basics), information can be customized, adapted, visualized and analysed by using Python programming. In this course, we show the basics of object-oriented modelling as well as different data formats. We also deal with data transformation and scripting, data modelling and work out basic algorithms of spatial analysis. We will query OSM for information and create simple analysis maps and graphs. The aim is to understand the structure and composition of spatial data in order to be able to process and analyse it.

Objective (Expected Results of Study and Acquired Competences)

• Analysis and modelling of data
• Basic understanding of data structures generic / GIS-specific
• Basic Python programming skills
• Data transformation and linking
• Data examination and investigation

Course Criteria & Registration

The course is available in summer and winter semester. Registration is done via RWTH-Online. Students who need to repeat the subject will find all the necessary information on this website: https://dc.rwth-aachen.de/de/lehre/bachelor/wiederholung The course will be held in English.

Reading recommendations

McGregor, Susan E. Practical Python Data Wrangling and Data Quality. " O'Reilly Media, Inc.", 2021.

Kresse, Wolfgang, and David M. Danko, eds. Springer handbook of geographic information. Berlin: Springer, 2012.

Borrmann, André, et al._ _Building Information Modeling-Technologische Grundlagen und industrielle Praxis. 2021.

Bishop, Wade, and Tony H. Grubesic. Geographic information. Springer International Publishing Switzerland, 2016.

Dates