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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

Exploring vector layer properties and subsetting

This section is going to be devoted to the examination of spatial vector layer properties, and to subsetting them based on their attribute tables. Some of the presented procedures will be analogous to those presented for rasters in the previous chapter (for example, plotting and querying CRS information), while others are generally relevant only to vector layers (for example, calculating areas and creating subsets according to the attribute table). As will quickly become apparent, many operations involving attribute tables of vector layers are conveniently analogous to operations on data.frame objects.

Examining vector layer properties

The summary function produces a useful textual summary of the properties of a vector layer, including its class, bounding box coordinates, CRS, and attribute table column types. For example, using summary on airports produces the following textual output:

> summary(airports)
Object of class SpatialPointsDataFrame...
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