Available courses

Agent Based Modelling in agricultural and resource economics

This course is offered in the Doctoral Certificate Program in Agricultural Economics. 

The objective of the course is to introduce Agent-Based modelling (Agent Based Modelling or ABM) and simulation of complex systems especially in the field of agricultural and resource economics. The course is a five-day learning module, divided into theory and practical sessions. NetLogo will be used in this course. No previous experience is required. 

Automation of Spatial Data Analysis

Geodata Visualization

Introduction to the Model Designer and Python Console in QGIS

Introduction to Remote Sensing and Google Earth Engine

Spatial Analysis of Climatic Data in R (eLearning)

Online Maps - Design, Implementation and More

Summer School 2025

Visualizing and Analyzing Spatial Data in R (eLearning)

This course introduces you to working with the software R and R Studio and puts emphasis on visualizing geodata, performing and automating spatial data analysis, interpolation techniques, and creating interactive maps.

Knowledge Base

You can find here a compilation of background material that is relevant for the other GEO-WB6 courses.

Spatial Analysis of Climatic Data in R

In this course, you will learn how to use R for data visualization and spatial data analysis, with a particular emphasis on analyzing climatic data for the study of climate change and extreme weather events in the Western Balkans. 

Visualizing and Analyzing Spatial Data in R

This course introduces you to working with the software R and R Studio and puts emphasis on visualizing geodata, performing and automating spatial data analysis, interpolation techniques, and creating interactive maps.

Introduction to GIS and Spatial Data Analysis

This class is a module offered to doctoral researchers under the framework "Docotral Certificate Program in Agricultural Economics". The class is an introductory course to GIS and spatial data analysis.

This course introduces the basic concepts, methods, and software tools for the acquisition, manipulation, visualisation, exploration, and analysis of spatial data. The ultimate aim of the course of to make students interested in spatial data analysis and to raise awareness of the potentials and pitfalls in spatial data analysis. We will provide a mix of lectures addressing basic theoretical knowledge of GIS and spatial data analysis, presentations with applications of spatial data analysis, and practical hands-on exercises using mainly QGIS (open source software) and GeoDa.