The course communicates the basics of the Python programming language and provides a general introduction to the ArcGIS Pro Python scripting framework. It also introduces several Python libraries (pandas, numpy, scipy, statsmodels, geopandas, rasterio) that greatly extend the capabilities of spatial data analysis and modelling.
Learning objective
Students will learn the basics of geographic data processing using the Python programming language and ArcGIS Pro (arcpy). They will be able to implement their own geoprocessing scripts for spatial data analysis and modelling. Students will be able to integrate open source libraries into their Python scripts and know how to apply the libraries to geospatial datasets.
Content
The course covers basic Python language concepts such as data types, control structures and functions. These concepts are then used to gain a deeper understanding of ArcGIS Pro's geoprocessing framework (arcpy). This includes vector data processing functions as well as geoprocessing functions for raster data analysis. It also introduces the use of key Python libraries in conjunction with geospatial datasets.
Lecture notes
Lecture notes, exercises and worked-out solutions will be provided.
Literature
Lutz M. (2013): Learning Python, 5th Edition, O'Reilly Media Zandbergen P. A. (2020): Python Scripting for ArcGIS Pro. Esri Press. Zandbergen P. A. (2020): Advanced Python Scripting for ArcGIS Pro. Esri Press. De Smith M., Goodchild, M.F., Longley, P. A. (2018): Geospatial Analysis, 6th Edition, Troubador Publishing Ltd.
Prerequisites / Notice
Basic knowledge of ArcGIS is assumed.
Competencies
Subject-specific Competencies
Concepts and Theories
fostered
Techniques and Technologies
fostered
Method-specific Competencies
Analytical Competencies
fostered
Decision-making
fostered
Media and Digital Technologies
fostered
Problem-solving
fostered
Personal Competencies
Critical Thinking
fostered
Performance assessment
Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course