1 Introduction to Geographical Information Systems (GIS)
This course provides an overview of Geographic Information System (GIS) concepts and their components. Both the theory and practical application of GIS will be emphasized in this course. The course’s objective is to teach students the concepts for managing geographic information systems, as well as the use of QGIS geographic information systems software and its application in environmental engineering.
Learning Objectives
Demonstrate theoretical understanding and proficiency in Geographic Information Systems;
Demonstrate understanding and proficiency in recording, formatting, storing, and editing spatial data;
Demonstrate understanding and proficiency in map design;
Demonstrate proficiency in using the GIS software QGIS.
2 Advanced Geographical Information Systems (GIS) with R Programming
This introductory course offers a hands-on foundation in Geographical Information Systems (GIS) using the R programming language. Designed for students and professionals with little or no prior experience in spatial analysis or coding, the course guides participants through the essential concepts, tools, and techniques for working with geographic data.
What You’ll Learn:
How to import, explore, and visualize spatial data (vector and raster) using R
Basic geoprocessing operations: buffering, clipping, spatial joins, and overlays
Creating simple, publication-ready maps using R packages such as sf, terra, and tmap
Introduction to spatial data structures and reproducible GIS workflows in R
Designed For:
This course is ideal for beginners in GIS or R who are interested in environmental sciences, urban studies, geography, or data science, and want to develop practical skills in open-source spatial analysis.
Course Format:
Interactive lectures and coding labs using real-world examples
Focus on free and open-source tools: RStudio and key spatial R packages
No prior experience in GIS or R required — just curiosity and motivation to learn
3 Programming and Spatial Analysis with R
Master’s in Remote Sensing – Universidad Mayor
This course provides an applied introduction to spatial data analysis and interpolation techniques using the R programming language. Aimed at postgraduate students in environmental and earth sciences, the course combines lectures with hands-on computing labs to develop practical skills in geospatial data handling, visualization, and modeling.
Key topics include:
Fundamentals of spatial data structures and coordinate reference systems
Handling and visualizing raster and vector data using R packages such as sf, terra, ggplot2, and tmap
Introduction to spatial interpolation using deterministic methods (e.g., Inverse Distance Weighting, regression) and geostatistical techniques (e.g., Kriging, Regression-Kriging)
Exploratory spatial data analysis, including autocorrelation and variogram modeling
Spatial prediction using machine learning with random forest
By the end of the course, students will be able to process and analyze spatial datasets, create publication-quality maps, and apply reproducible workflows for spatial modeling in R.