Spatial Analysis with R

Researchers gained new insights from data using statistical learning approaches and learned how to programmatically incorporate statistical learning and spatial analysis methods in data analysis using R.

In collaboration with UC Davis, CGIAR-CSI organized a two-day technical training workshop on “Spatial Data Analysis with R” on August 27-28, 2019, led by Prof. Robert Hijmans.

The first part of the workshop provided a practical introduction to the machine learning with R. The training started with a general discussion of machine learning and its difference from more classic regression approaches (based on the textbook, “An Introduction to Statistical Learning with Applications in R”), followed by a session on the random forest algorithm and its use-cases.

The second part of the workshop was opened with a brief introduction on the basics of using R for spatial data analysis, followed by a suite of predictive modeling approaches (from interpolation to remote sensing image classification). Participants worked on guided exercises and discussed more details on how they could incorporate the approaches in their research.

About 50 CGIAR researchers participated on-site at IFPRI HQ and online. All training materials, including data files and R codes for self-learning, are available online at https://gfc.ucdavis.edu/events/ifpri/html.

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