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Michael Dorman - Learning R for Geospatial Analysis [2014, PDF, ENG]

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Post 08-Jul-2016 03:00


Learning R for Geospatial Analysis
Год издания: 2014
Автор: Michael Dorman
Издательство: Packt Publishing
ISBN: 9781783984367
Язык: Английский
Формат: PDF
Качество: Издательский макет или текст (eBook)
Интерактивное оглавление: Да
Количество страниц: 364
Описание: 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

Примеры страниц


Table of Contents
1: The R Environment
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
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