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Geographical Data Science and Spatial Data Analysis
An Introduction in R
- Lex Comber - University of Leeds, UK
- Chris Brunsdon - National University of Ireland, Maynooth, Ireland
Additional resources:
Series:
Spatial Analytics and GIS
Spatial Analytics and GIS
December 2020 | 360 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 (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.
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.
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
Supplements
Click for online resources
The online resources include:
The online resources include:
· Code Library of up-to-date R scripts from each chapter to help you feel confident using R.
· Data Library with datasets to practice your skills on real-world data.
· Journal Articles on important topics, such as critical spatial data science, to deepen your understanding.
