Name: R for geospatial predictive mapping
Start: 2025-11-27T18:00:00+01:00
End: 2025-11-27T20:00:00+01:00

**R for geospatial predictive mapping: practical workflows for reliable spatial predictions**

This talk introduces practical workflows for building reliable spatial predictions in R, illustrated with a real-world case study on predicting plant species richness across South America. We’ll start with classic interpolation methods like IDW and kriging, then move to machine learning approaches using Random Forests. Along the way, we will discuss common pitfalls, such as unrealistic predictions and spatial bias, and explore practical solutions like the Area of Applicability (AoA) and prediction-domain adaptive cross-validation methods (kNNDM). We will also look at R’s spatial machine learning ecosystem, including CAST, caret, mlr3, and tidymodels, along with key steps in feature engineering, hyperparameter tuning, and uncertainty estimation. All examples include reproducible R code, making it easy to adapt the workflows to different spatial prediction tasks.

Federica Gazzelloni

Lucy M

lucio.colonna

Rome R Users Group

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Associate Professor and Researcher & Educator in Computational Geography 

Author of geocompx.org

Dr. Jakub Nowosad is an associate professor in the Institute of Geoecology and Geoinformation at the Adam Mickiewicz University in Poznan, Poland. His main research is focused on developing and applying data mining and pattern-based spatial methods to large datasets in order to broaden our understanding of processes and patterns in the environment. He has been involved in various interdisciplinary collaboration on topics of automatic global ecoregionalizations, extreme precipitation modeling, terrain classifications, phenology forecasting, and soil properties determination. His research interests also include spatial analysis, statistics, and programming. He has an extensive teaching experience in the fields of spatial analysis, geostatistics, statistics, and machine learning. Jakub is an avid R user and an active member of the R community.

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R for geospatial predictive mapping

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