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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

Data structures for vector layers in R


Spatial vector layers have two components: the geometry and the attribute table. The geometry component holds the spatial coordinates and information regarding their arrangement in separate features, while the attribute table holds additional information regarding each feature. For example, in a point layer of capital cities, the record for London may be composed of a geometric component (a point coordinate, such as 51.5072°N, 0.1275°W) and a row in an attribute table holding additional data regarding each city (for example, population size, built area, and so on).

The geometry part in a vector layer is obligatory and there are three types of geometries: points, lines, and polygons. The attribute table is optional. Classes for the six spatial vector layers types, constituting all possible combinations of these two properties, have been defined in the sp package. They are summarized in the following table:

Geometry type

Attribute table

Class

Points...

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