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Learning R for Geospatial Analysis

Learning R for Geospatial Analysis

By : Michael Dorman
3.9 (7)
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Learning R for Geospatial Analysis

Learning R for Geospatial Analysis

3.9 (7)
By: Michael Dorman

Overview of this book

This book is intended for anyone who wants to learn how to efficiently analyze geospatial data with R, including GIS analysts, researchers, educators, and students who work with spatial data and who are interested in expanding their capabilities through programming. The book assumes familiarity with the basic geographic information concepts (such as spatial coordinates), but no prior experience with R and/or programming is required. By focusing on R exclusively, you will not need to depend on any external software—a working installation of R is all that is necessary to begin.
Table of Contents (13 chapters)
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10
A. External Datasets Used in Examples
11
B. Cited References
12
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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Learning R for Geospatial Analysis
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