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.
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
