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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

Making 3D plots with lattice

Three-dimensional visualization, although undesirable for certain purposes (where precise interpretation is important, such as in the scientific literature), can nevertheless be particularly impressive and aesthetically appealing. In this section, we are going to use lattice to create three-dimensional plots of spatial and nonspatial data, which is not possible to do with ggplot2 since it only allows two-dimensional plotting. The lattice graphics framework and syntax are no less complex than those of ggplot2, and a single section is far too short to comprehensibly review the subject. Our purpose here is much more modest: to show some of the things that can be achieved and inspire interested readers to investigate further. For more information on lattice, readers are referred to the authoritative overview in the book by package author Deepayan Sarkar, Lattice: Multivariate Data Visualization with R, Springer, which was published in 2008.

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