• Title: An Introduction to R for Spatial Analysis and Mapping
• Author(s): Chris Brunsdon, Lex Comber
• Publisher: Geospatial Analysis Online
• Paperback/Hardcover: N/A
• eBook: HTML
• Language: English
• ISBN-10: N/A
• ISBN-13: N/A
• Share This:  

Book Description

This book provides an introduction to the use of R for spatial statistical analysis, geocomputation and the analysis of geographical information for researchers collecting and using data with location attached, largely through increased GPS functionality.

• N/A

• Amazon

• Geographic Information Science and Systems (GIS), Spatial Analysis, etc.
• The R Programming Language
• Data Analysis and Data Mining
• Data Science
• Statistics

• An Introduction to R for Spatial Analysis and Mapping (Chris Brunsdon, et al.)
• The Mirror Site (1) - ePub

• An Introduction to Spatial Data Analysis and Statistics in R

  This book provides an introduction to the use of R for spatial statistical analysis, geocomputation and the analysis of geographical information for collecting and using data with location attached.

• An Introduction to Spatial Data Analysis and Visualization in R

  This book provides a balance between concepts and practicums of spatial statistics with a comprehensive coverage of the most important approaches to understand spatial data, analyze spatial relationships and patterns, and predict spatial processes.

• Hands-On Spatial Data Science with R (Luc Anselin, et al.)

  Spatial data is crucial to improve decision-making in a wide range of fields. This book describes statistical methods, modeling approaches, and visualization techniques to analyze spatial data using R.

• Spatial Statistics for Data Science with R (Paula Moraga)

  Spatial data is crucial to improve decision-making in a wide range of fields. This book describes statistical methods, modeling approaches, and visualization techniques to analyze spatial data using R.

• Geographic Data Science with R: Visualizing and Analyzing

  This book provides a series of tutorials aimed at teaching good practices for using Time Series and geospatial data to address topics related to environmental change. It is based on the R language and aims at data scientists.

• R for Geographic Data Science (Stefano De Sabbata)

  This book introduces readers to tools in the R programming language for accessing and analyzing Census data from the United States Census Bureau and shows how to carry out demographic analyses in a single computing environment.

• Applied Spatial Data Analysis with R (Roger S. Bivand, et al)

  It presents R packages, functions, classes and methods for handling spatial data, and showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics, areal data analysis and disease mapping.

• Geocomputation with R (Robin Lovelace, et al.)

  This book is for people who want to analyze, visualize and model geographic data with open source software. It is based on R, a statistical programming language that has powerful data processing, visualization, and geospatial capabilities.

• R for Data Science: Visualize, Model, Transform, Tidy, Import

  This book teaches you how to do data science with R: You'll learn how to get your data into R, get it into the most useful structure, transform it, visualize it and model it, how data science can help you work with the uncertainty and capture the opportunities.

• R Graphics Cookbook: Practical Recipes for Visualizing Data

  This cookbook provides more than 150 recipes to help scientists, engineers, programmers, and data analysts generate high-quality graphs quickly - without having to comb through all the details of R's graphing systems.

• Geospatial Analysis with Python (Ujaval Gandhi)

  Suitable for GIS practitioners with no programming background or python knowledge. The course will introduce basic Python programming concepts, libraries for spatial analysis, geospatial APIs and techniques for building spatial data processing pipelines.

• Python Scripting for Spatial Data Processing (Pete Bunting, et al)

  This book is a Python tutorial for beginners aiming at teaching spatial data processing. It is used as part of the courses taught in Remote Sensing and GIS, using psycopg2, and ogr2ogr, etc., at Aberystwyth University, UK.

• Geospatial Analysis: Principles, Techniques and Software Tools

  This book provides a compehensive guide to concepts, methods and tools, with many examples being provided using a variety of software tools such as ArcGIS, Idrisi, Grass, Surfer and many others to clarify the concepts discussed

• Efficient R Programming: Practical Guide to Smarter Programming

  This book is about increasing the amount of work you can do with R in a given amount of time. It's about both computational and programmer efficiency. This book is for anyone who wants to make their use of R more reproducible, scalable, and faster.

• Cookbook for R: Best R Programming TIPs (Winston Chang)

  The goal of this cookbook is to provide solutions to common tasks and problems in analyzing data. Each recipe tackles a specific problem with a solution you can apply to your own project, and includes a discussion of how and why the recipe works.

• Hands-On Programming with R: Functions and Simulations

  This book not only teaches you how to program, but also shows you how to get more from R than just visualizing and modeling data. You’ll gain valuable programming skills and support your work as a data scientist at the same time.

• R Programming for Data Science (Roger D. Peng)

  This book is about the fundamentals of R programming. Get started with the basics of the language, learn how to manipulate datasets, how to write functions, and how to debug and optimize code. You will have a solid foundation on data science toolbox.

• R Packages: Organize, Test, Document, and Share Your Code

  Turn your R code into packages that others can easily download and use. This practical book shows you how to bundle reusable R functions, sample data, and documentation together by applying author's package development philosophy.

• Forecasting, Principles and Practice, Using R (R. J. Hyndman)

  This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly. Examples use R with many data sets taken from the authors' own consulting experience.