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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

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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Learning R for Geospatial Analysis
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