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An Introduction to R for Spatial Analysis and Mapping
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An Introduction to R for Spatial Analysis and Mapping



© 2015 | 360 pages | SAGE Publications Ltd

"In an age of big data, data journalism and with a wealth of quantitative information around us, it is not enough for students to be taught only 100 year old statistical methods using 'out of the box' software. They need to have 21st-century analytical skills too. This is an excellent and student-friendly text from two of the world leaders in the teaching and development of spatial analysis. It shows clearly why the open source software R is not just an alternative to commercial GIS, it may actually be the better choice for mapping, analysis and for replicable research. Providing practical tips as well as fully working code, this is a practical 'how to' guide ideal for undergraduates as well as those using R for the first time. It will be required reading on my own courses."
- Richard Harris, Professor of Quantitative Social Science, University of Bristol

R is a powerful open source computing tool that supports geographical analysis and mapping for the many geography and ‘non-geography’ students and researchers interested in spatial analysis and mapping.

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.

Brunsdon and Comber take readers from ‘zero to hero’ in spatial analysis and mapping through functions they have developed and compiled into R packages. This enables practical R applications in GIS, spatial analyses, spatial statistics, mapping, and web-scraping. Each chapter includes:

  • Example data and commands for exploring it
  • Scripts and coding to exemplify specific functionality
  • Advice for developing greater understanding - through functions such as locator(), View(), and alternative coding to achieve the same ends
  • Self-contained exercises for students to work through
  • Embedded code within the descriptive text.

 This is a definitive 'how to' that takes students - of any discipline - from coding to actual applications and uses of R.

 
Part 1: Introduction
 
Objectives of this book
 
Spatial Data Analysis in R
 
Chapters and Learning Arcs
 
The R Project for Statistical Computing
 
Obtaining and Running the R software
 
The R interface
 
Other resources and accompanying website
 
Part 2: Data and Plots
 
The basic ingredients of R: variables and assignment
 
Data types and Data classes
 
Plots
 
Reading, writing, loading and saving data
 
Part 3: Handling Spatial Data in R
 
Introduction: GISTools
 
Mapping spatial objects
 
Mapping spatial data attributes
 
Simple descriptive statistical analyses
 
Part 4: Programming in R
 
Building blocks for Programs
 
Writing Functions
 
Writing Functions for Spatial Data
 
Part 5: Using R as a GIS
 
Spatial Intersection or Clip Operations
 
Buffers
 
Merging spatial features
 
Point-in-polygon and Area calculations
 
Creating distance attributes
 
Combining spatial datasets and their attributes
 
Converting between Raster and Vector
 
Introduction to Raster Analysis
 
Part 6: Point Pattern Analysis using R
 
What is Special about Spatial?
 
Techniques for Point Patterns Using R
 
Further Uses of Kernal Density Estimation
 
Second Order Analysis of Point Patterns
 
Looking at Marked Point Patterns
 
Interpolation of Point Patterns With Continuous Attributes
 
The Kringing approach
 
Part 7: Spatial Attribute Analysis With R
 
The Pennsylvania Lung Cancer Data
 
A Visual Exploration of Autocorrelation
 
Moran's I: An Index of Autocorrelation
 
Spatial Autoregression
 
Calibrating Spatial Regression Models in R
 
Part 8: Localised Spatial Analysis
 
Setting Up The Data Used in This Chapter
 
Local Indicators of Spatial Association
 
Self Test Question
 
Further Issues with the Above Analysis
 
The Normality Assumption and Local Moran's-I
 
Getis and Ord's G-statistic
 
Geographically Weighted Approaches
 
Part 9: R and Internet Data
 
Direct Access to Data
 
Using RCurl
 
Working with APIs
 
Using Specific Packages
 
Web Scraping
 
Epilogue

In an age of big data, data journalism and with a wealth of quantitative information around us, it is not enough for students to be taught only 100 year old statistical methods using 'out of the box' software. They need to have 21st-century analytical skills too. This is an excellent and student-friendly text from two of the world leaders in the teaching and development of spatial analysis. It shows clearly why the open source software R is not just an alternative to commercial GIS, it may actually be the better choice for mapping, analysis and for replicable research. Providing practical tips as well as fully working code, this is a practical 'how to' guide ideal for undergraduates as well as those using R for the first time. It will be required reading on my own courses.

Richard Harris, Professor of Quantitative Social Science
University of Bristol

Brunsdon and Comber's An Introduction to R for Spatial Analysis and Mapping is a timely text for students concerned with the exploration of spatial analysis problems and their solutions. The authors combine extensive expertise and practical experience with a clear and accessible pedagogic style in the presentation of problems in spatial analysis. This volume is not only an excellent resource for students in the spatial sciences but should also find a place on the bookshelves of researchers.

Martin Charlton
National University of Ireland Maynooth

If you are new to R and spatial analysis, then this is the book for you. With plenty of examples that are easy to use and adapt, there's something for everyone as it moves comfortably from mapping and spatial data handling to more advanced topics such as point-pattern analysis, spatial interpolation, and spatially varying parameter estimation. Of course, all of this is "free" because R is open source and allows anyone to use, modify, and add to its superb functionality.

Scott M. Robeson
Indiana University

The statistical sections each use "real" data, and each section ends with "Self-Test Questions". Thus the book is suitable not only as a reference for specific spatial data problems, but also for self-study or for training courses, if you want to approach the topic in principle. Overall, the book has a very successful, rounded overview of the analysis and visualization of spatial data.

Dr Thomas Rahlf
Deutsche Forschungsgemeinschaft

Well laid out and easy to follow even for non-technical people.

Professor Iseult Lynch
Schl of Geog, Earth & Env'l Sciences, Birmingham University
July 20, 2016

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