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Abstract
This course provides an in-depth exploration of advanced spatial data modeling and analysis, including e.g. network or least-cost analyses. It introduces geoprocessing techniques using Python and ArcGIS, emphasizing the automation and scripting of complex GIS workflows.Upon completion, students will have acquired the technical and methodological foundations for advanced GIS analysis.
Objective
Upon successful completion of the course, students will be able to: 1. Apply advanced spatial data modelling and analytical methods to more complex geographic task and problems. 2. Design and implement automated geoprocessing workflows using ArcGIS Pro and Python to enhance analytical efficiency and reproducibility. 3. Critically evaluate the suitability of analytical techniques for specific spatial questions and datasets. 4. Integrate methodological, technical, and conceptual GIS competencies to solve real-world spatial planning and decision-making tasks.
Content
The course covers advanced spatial data modelling and analysis techniques, including suitability, visibility, shadow, network, and least-cost analyses. Emphasis is placed on geoprocessing and automation of complex workflows using ArcGIS (arcpy) and Python. Students also integrate open-source GIS packages into their scripts. Through applied project work, they combine multiple data sources, develop reproducible GIS methods, and critically evaluate analytical approaches in spatial planning and environmental contexts.
Resources
Literature
1) Chang Kang-tsun (2016): Introduction to Geographic Information Systems. University of Idaho - Eighth edition. 2) Silas T. (2022): Python for ArcGIS Pro: automate cartography and data analysis using ArcPy, ArcGIS API for Python, notebooks, and pandas. Packt Publishing. 3) Crawford D., Yaw D. (2025): Advanced Guide To Python in ArcGIS. ESRI Press.