In this section we introduce a number of approaches and techniques that are commonly used in spatial data analysis and modelling.
Spatial data are mostly like other data. The same general principles apply. But there are few things that are rather important to consider when using spatial data that are not common with other data types. These are discussed in Chapters 2 and 3 and include issues of scale and zonation (the modifiable areal unit problem), distance and spatial autocorrelation.
The other chapters, introduce methods in different areas of spatial data analysis. These include the three classical area of spatial statistics (point pattern analysis, regression and inference with spatial data, geostatistics (interpolation using Kriging), as well some other methods (local and global regression and classification with spatial data).
Some of the material presented here is based on examples in the book “Geographic Information Analysis” by David O’Sullivan and David J. Unwin. This book provides an excellent and very accessible introduction to spatial data analysis. It has much more depth than what we present here. But the book does not show how to practically implement the approaches that are discussed — which is the main purpose of this website.
The spatial statistical methods are treated in much more detail in “Applied Spatial Data Analysis with R” by Bivand, Pebesma and Gómez-Rubio.
This section builds on our Introduction to Spatial Data Manipulation R, that you should read first.