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



January 2021 | 336 pages | SAGE Publications Ltd
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 (ie 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’ text book, 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.


 
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

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
University of Manchester

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
Cardiff University

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