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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

Creating vector layers from a raster


The opposite operation to rasterization, which has been presented in the previous section, is the creation of vector layers from raster data. The procedure of extracting features of interest out of rasters, in the form of vector layers, is often necessary for analogous reasons underlying rasterization—when the data held in a raster is better represented using a vector layer, within the context of specific subsequent analysis or visualization tasks. Scenarios where we need to create points, lines, and polygons from a raster can all be encountered. In this section, we are going to see an example of each.

Raster-to-points conversion

In raster-to-points conversion conversion, each raster cell center (excluding NA cells) is converted to a point. The resulting point layer has an attribute table with the values of the respective raster cells in it.

Conversion to points can be done with the rasterToPoints function. This function has a parameter named spatial that...

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