An Introduction to R for Spatial Analysis and Mapping

There's no better text for showing students and data analysts how to use R for spatial analysis, mapping and reproducible research. If you want to learn how to make sense of geographic data and would like the tools to do it, this is your guide.


Students and other life-long learners need flexible skills to add value to spatial data. This comprehensive, accessible and thoughtful book unlocks the spatial data value chain. It provides an essential guide to the R spatial analysis ecosystem. This excellent state-of-the-art treatment will be widely used in student classes, continuing professional development and self-tuition.

In this second edition, the authors have once again captured the state of the art in one of the most widely used approaches to spatial analysis. Spanning from the absolute beginner to more advanced concepts and underpinned by a strong ‘learn by doing’ ethos, this book is ideally suited for both students and teachers of spatial analysis using R.

A timely update to the de facto reference and textbook for anyone — geographer, planner, or (geo)data scientist — needing to undertake mapping and spatial analysis in R. Complete with self-tests and valuable insights into the transition from sp to sf, this book will help you to develop your ability to write flexible, powerful, and fast geospatial code in R.

Brunsdon and Comber’s 2nd edition of their acclaimed text book is updated with the key developments in spatial analysis and mapping in R and maintains the pedagogic style that made the original volume such an indispensable resource for teaching and research.

The future of GIS is open-source! An Introduction to R for Spatial Analysis and Mapping is an ideal introduction to spatial data analysis and mapping using the powerful open-source language R.  Assuming no prior knowledge, Brunsdon and Comber get the reader up to speed quickly with clear writing, excellent pedagogic material and a keen sense of geographic applications. The second edition is timely and fresh. An Introduction to R for Spatial Analysis and Mapping should be required reading for every Geography and GIS student, as well as faculty and professionals.

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.

Lexs 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 Ma...

Chapter 1 Introduction Chapter 2 Data and Plots Chapter 3 Handling Spatial Data Chapter 4 Programming in R Chapter 5 Using R as a GIS Chapter 6 Point Pattern Analysis Chapter 7 Spatial Attribute Analysis Chapter 8 Localised Spatial Analysis Chapter 9 R and Internet Data Chapter 10 Epilogue