Learning R for Geospatial Analysis

Leverage the power of R to elegantly manage crucial geospatial analysis tasks

About This Book

• Write powerful R scripts to manipulate your spatial data
• Gain insight from spatial patterns utilizing R’s advanced computation and visualization capabilities
• Work within a single spatial analysis environment from start to finish

Who This Book Is For

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.

In Detail

R is a simple, effective, and comprehensive programming language and environment that is gaining ever-increasing popularity among data analysts.

This book provides you with the necessary skills to successfully carry out complete geospatial data analyses, from data import to presentation of results.

Learning R for Geospatial Analysis is composed of step-by-step tutorials, starting with the language basics before proceeding to cover the main GIS operations and data types. Visualization of spatial data is vital either during the various analysis steps and/or as the final product, and this book shows you how to get the most out of R’s visualization capabilities. The book culminates with examples of cutting-edge applications utilizing R’s strengths as a statistical and graphical tool.

Table of Contents

Chapter 1: The R Environment
Chapter 2: Working with Vectors and Time Series
Chapter 3: Working with Tables
Chapter 4: Working with Rasters
Chapter 5: Working with Points, Lines, and Polygons
Chapter 6: Modifying Rasters and Analyzing Raster Time Series
Chapter 7: Combining Vector and Raster Datasets
Chapter 8: Spatial Interpolation of Point Data
Chapter 9: Advanced Visualization of Spatial Data
Appendix A: External Datasets Used in Examples
Appendix B: Cited References