Geographical data science and spatial data analysis : an introduction in R / Lex Comber, Chris Brunsdon.

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

"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. These include the questioning of classical statistical hypothesis testing (with Big Data almost everything is significant), the importance of data visualizations to support robust hypothesis development and the role of spatial data analytics to link different big spatial datasets and to support trend identification. This book builds on the tools and techniques described in An Introduction to R for Spatial Analysis and Mapping by Brunsdon and Comber, extending these into Big Spatial Data and Data Analytics. It reflects a number of recent developments in both thinking about Big Spatial Data and in handling such data in R, the open source statistical software, which have significantly increased R's ability to handle, process and visualize big data. As yet there are no text books which reflect these recent developments in data handling in R, that develop robust inferential methods for Big Data analysis, that include spatial operations in data analytics or that describe advanced spatial manipulations and visualizations of highly dimensional, spatially referenced data. This book addresses these gaps"--

"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. These include the questioning of classical statistical hypothesis testing (with Big Data almost everything is significant), the importance of data visualizations to support robust hypothesis development and the role of spatial data analytics to link different big spatial datasets and to support trend identification. This book builds on the tools and techniques described in An Introduction to R for Spatial Analysis and Mapping by Brunsdon and Comber, extending these into Big Spatial Data and Data Analytics. It reflects a number of recent developments in both thinking about Big Spatial Data and in handling such data in R, the open source statistical software, which have significantly increased R's ability to handle, process and visualize big data. As yet there are no text books which reflect these recent developments in data handling in R, that develop robust inferential methods for Big Data analysis, that include spatial operations in data analytics or that describe advanced spatial manipulations and visualizations of highly dimensional, spatially referenced data. This book addresses these gaps"--