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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

Summary


To summarize, in this chapter you learned how to interpolate point data in space to produce continuous rasters using four different methods. The first of these, nearest-neighbor interpolation, we coded ourselves, while the other three (IDW, OK, and UK) were applied using functions in the gstat and automap packages. You also learned how to perform LOOCV to calculate RMSE and assess prediction accuracy. Finally, we saw two examples of how to automate a complex procedure to perform it repeatedly over an array of parameter sets.

In the next chapter, you are going to learn how to produce more elaborate plots involving spatial data in R with ggplot2.

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