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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 7. Combining Vector and Raster Datasets

Generating new insight by overlaying several layers of spatial information, one on top of the other, constitutes one of the main concepts of spatial data analysis, as we have already seen in the previous chapters. So far, however, we have only used operations involving either rasters alone or vector layers alone, but not a combination of both. Although the two types of spatial layers have their characteristic uses (such as rasters for DEMs and vector layers for administrative borders), combining them in a single analysis is often desired. As we shall see, this is a less straightforward task, characterized by specific procedures and decisions.

In this chapter, we are going to explore the interplay between vector and raster layers, and the way it is implemented in the raster package. The way rasters and vector layers can be interchanged and queried, one according to the other, will be demonstrated through examples.

Note

In this chapter...

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