An Introduction to R for Spatial Analysis and Mapping

"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 Brist#strong /strong# 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.

Table of Contents:
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

About the Author :
Chris Brunsdon is Professor of Geocomputation and Director of the National Centre for Geocomputation at the National University of Ireland, Maynooth, having worked previously in the Universities of Newcastle, Glamorgan, Leicester and Liverpool, variously in departments focusing on both geography and computing. He has interests that span both of these disciplines, including spatial statistics, geographical information science, and exploratory spatial data analysis, and in particular the application of these ideas to crime pattern analysis, the modelling of house prices, medical and health geography and the analysis of land use data. He was one of the originators of the technique of geographically weighted regression (GWR). He has extensive experience of programming in R, going back to the late 1990s, and has developed a number of R packages which are currently available on CRAN, the Comprehensive R Archive Network. He is an advocate of free and open source software, and in particular the use of reproducible research methods, and has contributed to a large number of workshops on the use of R and of GWR in a number of countries, including the UK, Ireland, Japan, Canada, the USA, the Czech Republic and Australia. When not involved in academic work he enjoys running, collecting clocks and watches, and cooking – the last of these probably cancelling out the benefits of the first. Alexis Comber, Lex, is Professor of Spatial Data Analytics at Leeds Institute for Data Analytics (LIDA) the University of Leeds. He worked previously at the University of Leicester where he held a chair in Geographical Information Science. His first degree was in Plant and Crop Science at the University of Nottingham and he completed a PhD in Computer Science at the Macaulay Institute, Aberdeen (now the James Hutton Institute) and the University of Aberdeen. This developed expert systems for land cover monitoring from satellite imagery and brought him into the world of spatial data, spatial analysis, and mapping. Lex’s research interests span many different application areas including environment, land cover / land use, demographics, public health, agriculture, bio-energy and accessibility, all of which require multi-disciplinary approaches. His research draws from methods in geocomputation, mathematics, statistics and computer science and he has extended techniques in operations research / location-allocation (what to put where), graph theory (cluster detection in networks), heuristic searches (how to move intelligently through highly dimensional big data), remote sensing (novel approaches for classification), handling divergent data semantics (uncertainty handling, ontologies, text mining) and spatial statistics (quantifying spatial and temporal process heterogeneity). He has co-authored (with Chris Brunsdon) An Introduction to R for Spatial Analysis and Mapping, the first ‘how to book’ for spatial analyses and mapping in R, the open source statistical software, now in its second edition. Outside of academic work and in no particular order, Lex enjoys his vegetable garden, walking the dog and playing pinball (he is the proud owner of a 1981 Bally Eight Ball Deluxe). 

Review :
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 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 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