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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


In this chapter, you learned some of the most useful methods for advanced visualization of spatial data in R, using the packages ggplot2, ggmap, and lattice. It was shown how these tools can be used to conclude a spatial analysis procedure and create publishable maps and plots of the results, all within the R environment. In this context, it has been noted that not everything can be accomplished in R, and at times we need to migrate to traditional GIS software or graphic editors for interactive customization of the graphic output. Nevertheless, visualization in R is extremely flexible, while at the same time bringing all of the benefits of programming. Once you become more familiar with the techniques presented in this chapter, it is almost inevitable that R will become the primary tool of choice for data visualization. I sincerely hope that after completing this book you feel the same way about geospatial data analysis in R.

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