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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

Data structures for rasters in the raster package

A raster is a rectangular grid of numeric values, referenced to a certain geographical extent. As previously mentioned, spatial referencing is what differentiates a raster from the simpler data structures (matrices and arrays) we have seen previously. A raster can have a single value in each cell (a single band, or single layer, raster—analogous to a matrix) or several values (a multiband, or multilayer, raster—analogous to an array). Rasters conceptually differ from vector layers, which are data structures to represent non-gridded objects such as spatial points, lines, and polygons (these will be covered in the next chapter).

In this book, we are going to work with classes to represent rasters from the raster package. This package does not come with the R installation, so we first have to install it using install.packages (see the previous chapter). We will also need to install the rgdal package since functions in the raster...

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