Book Image

Learning R for Geospatial Analysis

By : Michael Dorman
Book Image

Learning R for Geospatial Analysis

By: Michael Dorman

Overview of this book

Table of Contents (18 chapters)
Learning R for Geospatial Analysis
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
External Datasets Used in Examples
Cited References
Index

Summary


In this chapter, you learned additional methods and procedures for working with raster layers in R. We now know how to crop, aggregate, or reproject a raster to bring it to the desired extent, resolution, and CRS. We discussed how focal filtering and clumping can be applied to highlight patterns of interest in a raster. We also discussed how topography-related variables can be derived from a DEM, and how spatio-temporal raster data can be aggregated.

In the next chapter, you will learn about the interface between rasters and vector layers, and the different ways in which both can be combined in a spatial analysis.