Lex Comber & Chris Brunsdon 
Geographical Data Science and Spatial Data Analysis [PDF ebook] 
An Introduction in R

We are in an age of big data where all of our everyday interactions and transactions generate data. Much of this data is spatial – it is collected some-
where – and identifying analytical insight from trends and patterns in these increasing rich digital footprints presents a number of challenges.



Whilst other books describe different flavours of Data Analytics in R and other programming languages, there are none that consider 
Spatial Data (i.e. the location attached to data), or that consider issues of inference, linking Big Data, Geography, GIS, Mapping and Spatial Analytics. 



This is a ‘learning by doing’ textbook, building on the previous book by the same authors,  
An Introduction to R for Spatial Analysis and Mapping. It details the theoretical issues in analyses of Big Spatial Data and developing practical skills in the reader for addressing these with confidence.

Table des matières

Chapter 1: Introduction to Geographical Data Science and Spatial Data Analytics

Chapter 2: Data and Spatial Data in R

Chapter 3: A Framework for Processing Data: The Piping Syntax and dplyr

Chapter 4: Creating Databases and Queries in R

Chapter 5: EDA and Finding Structure in Data

Chapter 6: Modelling and Exploration of Data

Chapter 7: Applications of Machine Learning to Spatial Data

Chapter 8: Alternative Spatial Summaries and Visualisations

Chapter 9: Epilogue on the Principles of Spatial Data Analytics

A propos de l’auteur

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.