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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. This book is a must-read for anyone wishing to use R to analyse large spatial datasets. It is suitable for teachers and learners at all levels, building knowledge from the ground-up using relevant, real-world examples and easy to follow instructions. -- Jonathan Huck Written by two renowned international experts, this is an excellent introductory book for students, teachers and researchers alike who have experience of using R and who want to further develop their skills in big data spatial science. -- Scott Orford

Geographical Data Science and Spatial Data Analysis: An Introduction in R

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