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Learning R for Geospatial Analysis

You're reading from   Learning R for Geospatial Analysis Leverage the power of R to elegantly manage crucial geospatial analysis tasks

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Product type Paperback
Published in Dec 2014
Publisher Packt
ISBN-13 9781783984367
Length 364 pages
Edition 1st Edition
Languages
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Author (1):
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Michael Dorman Michael Dorman
Author Profile Icon Michael Dorman
Michael Dorman
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Table of Contents (13) Chapters Close

Preface 1. The R Environment FREE CHAPTER 2. Working with Vectors and Time Series 3. Working with Tables 4. Working with Rasters 5. Working with Points, Lines, and Polygons 6. Modifying Rasters and Analyzing Raster Time Series 7. Combining Vector and Raster Datasets 8. Spatial Interpolation of Point Data 9. Advanced Visualization of Spatial Data A. External Datasets Used in Examples
B. Cited References
Index

Chapter 4. Working with Rasters

In this chapter, we move on to the realm of spatial data analysis in R. We begin by introducing the properties and usage principles of the classes used to store raster data in R. For that matter, we are going to first introduce the simpler (nonspatial) structures that are conceptually related to rasters: matrices and arrays. We then cover the more sophisticated classes defined in the raster package to represent spatial raster data. You will learn to create, subset, and save objects of these classes as well as to query the characteristics of rasters we have at hand. Afterwards, you will learn two basic operations involving rasters: overlay and reclassification. At the same time, we will see some examples of visualizing raster data in R to help us get a better understanding of the data we have.

In this chapter, we'll cover the following topics:

  • Using matrices to represent two-dimensional sets of numeric values
  • Using arrays to represent three-dimensional...
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