Learning R for Geospatial Analysis - Dorman, Michael

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Résumé :
Leverage the power of R to elegantly manage crucial geospatial analysis tasks Key Features 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 Book Description 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. What you will learn Make inferences from tables by joining, reshaping, and aggregating Familiarize yourself with the R geospatial data analysis ecosystem Prepare reproducible, publication-quality plots and maps Efficiently process numeric data, characters, and dates Reshape tabular data into the necessary form for the specific task at hand Write R scripts to automate the handling of raster and vector spatial layers Process elevation rasters and time series visualizations of satellite images Perform GIS operations such as overlays and spatial queries between layers Spatially interpolate meteorological data to produce climate maps

Biographie:
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.