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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

Appendix A. External Datasets Used in Examples

Most of the code examples in this book use data from external files. To reproduce the examples, you are encouraged to download these files from the book's website and then copy them to a single directory on the hard drive. In the example code, the files are assumed to reside in C:\Data. To use a different directory, the examples code should be modified accordingly.

The external files in this book's examples, in an alphabetical order of first filenames, are listed in the following table:

Dataset

Associated files

Description

Daily meteorological data for Albuquerque International Airport

338284.csv

GHCND_documentation.pdf

Daily climatic records from the Albuquerque International Airport, New Mexico, United States meteorological station. Downloaded from the NOAA Climate Data Online website at http://www.ncdc.noaa.gov/cdo-web. Accessed May 2014.

Monthly meteorological data for Spain

343452.csv

GHCNDMS_documentation...

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