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6 Ways to Get Started With R for Spatial Machine Learning Analysis
R is a potent machine learning program whose features flow naturally into the study of geographical data, opening up new avenues for research in disciplines like agriculture, urban planning, and environmental science.
The advantages of R’s machine learning libraries, such caret, mlr3, and randomForest, and how they expedite processes from data preprocessing to model evaluation, have been covered in earlier articles. However, R’s real flexibility comes into play when we use specialized packages like raster and sf to introduce spatial data, enabling sophisticated spatial machine learning applications.
This guide presents 10 ways to get started with R for spatial machine learning analysis, covering essential tools, preprocessing techniques, model selection, and visualization strategies. Whether you’re new to spatial data science or looking to deepen your expertise, these steps will help you build a solid foundation for integrating machine learning with spatial data in R.
I have written about R and ML here.
-Getting Started
- To set up R and RStudio for spatial machine learning
Install R
Download R from CRAN, selecting the correct version for your OS. Follow the installation prompts to…
