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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

Changing the spatial extent or resolution of rasters


With reference to raster geometry, so far we have only dealt with operations where the raster extent is manually reduced by selecting a certain combination of rows and columns to retain (refer to Chapter 4, Working with Rasters). In this chapter, we will review more operations that provide us with the freedom to modify the raster geometry of datasets according to our specific requirements.

In this section, we will see how we can change the extent or resolution of rasters without modifying the underlying grid arrangement. This category includes operations such as merging rasters, cropping, or aggregating/disaggregating raster cells. In the next section, we will see how the underlying grid (and possibly the CRS) can be modified through resampling and reprojection.

In the first few examples of this chapter, we will work with a DEM of the area surrounding Haifa, and experiment with the modification of raster extent and resolution. A DEM is a...

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