"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.
Chris Brunsdon is Professor of Geocomputation at the National University of Ireland, Maynooth. He studied Mathematics at the University of Durham and Medical Statistics at the University of Newcastle upon Tyne, and has worked in a number of universities, holding the Chair in Human Geography at Liverpool University before taking up his current position. His research interests are in health, crime and environmental data analysis, and in the development of spatial analytical tools, including Geographically Weighted Regression approach. He also has interests in the software tools used to develop such approaches, including R. Lex Comber is a Professor of Geographical Information Sciences at the University of Leicester. After studying for a BSc in Plant and Crop Sciences at Nottingham, he did his PhD at the Macaulay Land Use Research Institute (now the Hutton Institute) and the University of Aberdeen. His research covers all areas of spatial analyses and the application and development of quantitative geographical. These have been applied across topic areas that straddle both the social and environmental and include accessibility analyses, land cover / land use monitoring and handling uncertainty in geographic information and spatial data.
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