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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

Product type Paperback

Published in Dec 2014

Publisher Packt

ISBN-13 9781783984367

Length 364 pages

Edition 1st Edition

Languages

R

Concepts

Geospatial Analysis

Author (1):

Michael Dorman

View More author details

Table of Contents (13) Chapters

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 B. Cited References

Now that we have covered everything, let's take a look at some external references for further reading:

Bivand, R. S., Pebesma, E. J., and Gómez-Rubio, V. Applied Spatial Data Analysis with R Second Edition. Springer. 2013.
Chang, W. R Graphics Cookbook. O'Reilly Media. 2012.
Cowpertwait, P. S. P. and Metcalfe, A. V. Introductory Time Series with R. Springer. 2009.
Diggle, P. and Ribeiro, P. J. Model-based Geostatistics. Springer. 2007.
Grolemund, G. and Wickham, H. "Dates and Times Made Easy with lubridate". Journal of Statistical Software. 2011. http://www.jstatsoft.org/v40/i03/paper.
Hengl, T. "A practical guide to geostatistical mapping of environmental variables". JRC Scientific and Technical Reports. 2007. http://spatial-analyst.net/book/.
Hengl, T., Roudier, P., Beaudette, D., and Pebesma, E. "plotKML: Scientific Visualization of Spatio-Temporal Data". Journal of Statistical Software. 2014. http://cran.r...

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Authors (1)

Dorman

Dorman

Michael Dorman is currently a PhD candidate at the Department of Geography and Environmental Development, Ben-Gurion University of the Negev. His research explores the response of planted pine forests to changing climate through remote sensing and dendrochronology. He uses R extensively for time series and spatial statistical analyses and visualization. In spring 2013, he prepared and taught a course named Introduction to Programming for Spatial Data Analysis at the Ben-Gurion University of the Negev, introducing R as an environment for spatial data analysis to undergraduate Geography students. The course material served as a foundation for this book. Michael holds a Master's degree in Life Sciences from the Ben-Gurion University of the Negev and a Bachelor's degree in Plant Sciences in Agriculture from The Hebrew University of Jerusalem. He has authored or coauthored eight papers in scientific literature and actively participated in 18 scientific conferences.

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