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  • Book Overview & Buying Learning R for Geospatial Analysis
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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

Creating rasters from vector layers


One of the main reasons to convert a vector layer to a raster is that we are interested in employing raster analysis tools or procedures on data that is currently held in vector form (and vice versa). For example, when preparing a multiband raster with various environmental characteristics of a given area, such as slope or NDVI, we may wish to add layers that are commonly given in the vector format, such as built area polygons or road lines. To do this, we first need to convert these vector layers to rasters, and then supplement the multiband raster with the additional layers.

The process of converting a vector layer to a raster is called rasterizing, in the raster package terminology, and it is performed with the rasterize function. In this section, we will see an example of how to rasterize a point vector layer, while keeping in mind that the procedures to rasterize lines and polygons are analogous. You will also learn the related operations of raster...

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Learning R for Geospatial Analysis
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