Geographical Data Science and Spatial Data Analysis
An Introduction in R
Lex Comber
- University of Leeds, UK
If you’re in North America, please visit our Sage College Publishing website to purchase or sample this book:
Go to College Publishing WebsiteDescription
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.
Contents
Chapter 1: Introduction to Geographical Data Science and Spatial Data Analytics
Chapter 1: Introduction to Geographical Data Science and Spatial Data Analytics
Chapter 2: Data and Spatial Data in R
Chapter 2: Data and Spatial Data in R
Chapter 3: A Framework for Processing Data: The Piping Syntax and dplyr
Chapter 3: A Framework for Processing Data: The Piping Syntax and dplyr
Chapter 4: Creating Databases and Queries in R
Chapter 4: Creating Databases and Queries in R
Chapter 5: EDA and Finding Structure in Data
Chapter 5: EDA and Finding Structure in Data
Chapter 6: Modelling and Exploration of Data
Chapter 6: Modelling and Exploration of Data
Chapter 7: Applications of Machine Learning to Spatial Data
Chapter 7: Applications of Machine Learning to Spatial Data
Chapter 8: Alternative Spatial Summaries and Visualisations
Chapter 8: Alternative Spatial Summaries and Visualisations
Chapter 9: Epilogue on the Principles of Spatial Data Analytics
Chapter 9: Epilogue on the Principles of Spatial Data Analytics
Description
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.
Contents
Chapter 1: Introduction to Geographical Data Science and Spatial Data Analytics
Chapter 1: Introduction to Geographical Data Science and Spatial Data Analytics
Chapter 2: Data and Spatial Data in R
Chapter 2: Data and Spatial Data in R
Chapter 3: A Framework for Processing Data: The Piping Syntax and dplyr
Chapter 3: A Framework for Processing Data: The Piping Syntax and dplyr
Chapter 4: Creating Databases and Queries in R
Chapter 4: Creating Databases and Queries in R
Chapter 5: EDA and Finding Structure in Data
Chapter 5: EDA and Finding Structure in Data
Chapter 6: Modelling and Exploration of Data
Chapter 6: Modelling and Exploration of Data
Chapter 7: Applications of Machine Learning to Spatial Data
Chapter 7: Applications of Machine Learning to Spatial Data
Chapter 8: Alternative Spatial Summaries and Visualisations
Chapter 8: Alternative Spatial Summaries and Visualisations
Chapter 9: Epilogue on the Principles of Spatial Data Analytics
Chapter 9: Epilogue on the Principles of Spatial Data Analytics
Reviews
Geographical Data Science and Spatial Data Analysis
An Introduction in R
December 2020 | 360 pages | Sage UK
| Format | Published Date | ISBN | Price |
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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.
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 Of Contents:
- 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
Recent Product Reviews:
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