With R Data Science, organizations can, for example, analyze traffic flows, spatially model housing prices, study the distribution of animal species, or perform hotspot analyses on geographic data. In this blended learning course on R Data Science, participants learn how to apply advanced spatial statistics, geostatistics, and spatial data analysis within Geo-ICT. The course utilizes powerful packages such as spdep, spatstat, gstat, geoR, and GWmodel.
R Data Science focuses on analyzing, modeling, and interpreting data using the R programming language. Within Geo-ICT, this involves not only tables and statistics, but also spatial patterns, geographic relationships, and data-driven decision-making based on location data.
With R, geographic datasets can be combined with advanced statistical methods. Examples include analyzing hotspots, investigating spatial relationships, predicting values at unknown locations, and modeling the movements of people, animals, or objects. This creates a powerful environment for spatial data science within GIS, research, and policy.
What makes R so powerful is the combination of statistics, programmability, and a large ecosystem of specialized packages. This allows analyses to be performed reproducibly and complex spatial problems to be built up step by step in clear workflows. Within Geo-ICT, R is increasingly being used for spatial statistics, geostatistics, ecological analyses, mobility data, and spatial models.
In this blended learning course, you will work with key packages such as spdep, spatstat, gstat, geoR, tmaptools, spatialreg, GWmodel, adehabitatHR, adehabitatLT, and trajectories. You will learn to analyze spatial dependence, perform point pattern analyses, apply geostatistical models, and investigate trajectories or habitats.
In addition, R offers extensive possibilities for combining spatial data science with visualization, reporting, and automation. This makes this blended learning course particularly relevant for GIS specialists, data analysts, ecologists, researchers, and policy analysts who want to not only display geographic data but also statistically explain and substantiate it.
In this blended learning course, you will be introduced to the key capabilities of R for spatial data science. You will learn how to analyze spatial patterns, investigate relationships between locations, and apply statistical models to geographic datasets. You will work with packages such as spdep, spatstat, gstat, and spatialreg.
Attention is given to spatial autocorrelation, point pattern analysis, geostatistics, kriging, and spatial regression. You will learn how to investigate whether values are spatially correlated, how to recognize patterns in point data, and how to make predictions for locations where no direct measurements are available. Geographically weighted regression and local spatial differences are also covered using GWmodel.
In addition, you will learn how R can be used for ecological and movement data. Using packages such as adehabitatHR, adehabitatLT, and trajectories, you will work on analyses of habitats, movement patterns, and trajectory data. This creates a broad foundation for applications in ecology, mobility, infrastructure, environmental research, and spatial planning.
During the blended learning program, you will work with practical datasets and learn how to set up reproducible spatial data science workflows in R. Upon completion, you will be able to statistically analyze spatial data, explain patterns, and translate results into actionable insights for Geo-ICT projects.
Do you already have experience with R Spatial Basics? Then this blended learning course is a logical next step toward advanced spatial analysis, GeoAI, remote sensing, or data science within GIS projects.
Blended learning combines independent online learning with practical, interactive sessions, allowing you to understand both the statistical background and the practical application of spatial data science in R. In the online modules, you’ll learn how to build spatial patterns, dependencies, and models step by step using modern R packages.
You’ll discover how to analyze geographic datasets using spatial statistics, how to investigate point patterns, and how to apply geostatistical methods such as kriging. You’ll also learn how spatial regression and local models can help you better understand regional differences. Thanks to unlimited access to the course materials, you can review and practice the material at your own pace.
During the hands-on online sessions, you’ll immediately apply the theory to realistic datasets and spatial problems. You’ll receive guidance from experienced instructors and learn how to perform analyses using packages such as spdep, spatstat, gstat, spatialreg, and GWmodel.
The combination of online learning and interactive hands-on experience ensures that you not only understand the techniques behind spatial data science but can also apply them directly within GIS, research, and policy projects. After completing the blended learning program, you will be able to analyze geographic data more deeply and better substantiate spatial patterns using statistics and data science.
You’ll receive 1-on-1 guidance. After signing up, our course coordinator will contact you to schedule your first session.
Do you have questions about the course content? Or are you unsure whether the course aligns with your learning goals or preferences? Would you prefer an in-house or private course? We’d be happy to help.
A basic understanding of statistics is helpful, but not required. During the blended learning course, key concepts such as spatial autocorrelation, regression, and geostatistics are explained step by step using practical Geo-ICT examples.
R Spatial Basics focuses primarily on processing, managing, and analyzing geographic data in R. R Data Science goes a step further and covers statistical models, spatial statistics, hotspot analysis, geostatistics, and spatial data science techniques.
R is used for applications such as hotspot analysis, ecological modeling, mobility research, housing market analysis, traffic flow analysis, environmental analysis, geostatistics, crime analysis, and spatial policy issues.
During the blended learning program, you will work with packages such as spdep, spatstat, gstat, geoR, GWmodel, spatialreg, adehabitatHR, adehabitatLT, and trajectories for spatial statistics, geostatistics, and movement analysis.
Yes. R is often used in conjunction with GIS software such as QGIS and, to a lesser extent, ArcGIS. Geographic datasets can be easily imported, analyzed, and visualized, making R a powerful addition to existing GIS workflows.
