01 / Reading Guide
Reading Guide
Who this book is for
For graduate readers and experienced analysts who need to understand what a spatial dataset actually measures before fitting a model. It is useful after introductory GIS or an operational R guide, especially when a technically successful conversion or aggregation may have changed the interpretation of the quantities being analyzed.
Prerequisites
Have experience with R, spatial datasets and regression. Probability and statistical modeling are needed for the later chapters; the short R appendix does not replace learning the language.
What it covers
The first part explains coordinates, geometries, spherical operations, attribute support and data cubes. R tools then lead into spatial statistical models. The book emphasizes the relationship between the shape of an observation and the meaning of its values, providing a conceptual foundation for interpolation, autocorrelation and spatial regression rather than only package recipes.
How to use it
For a dataset, write down whether each value is a point measurement, an area total or another kind of attribute. Predict how aggregation or reprojection should affect it before running a command. Reproduce a small example and check totals, units and geometry. When fitting a spatial model, compare residual patterns with the assumptions and explain why the neighborhood or covariance structure is appropriate.
Edition and sources
The authors cite the 2023 first edition from Chapman & Hall/CRC and provide a free online text. The current R version is distinct from their separate experimental R-and-Python rendering. Publisher contents and the opening chapter’s discussion of geometry and measurement support were inspected. No format-specific ISBN is assigned, and current software behavior may differ from print examples. Publisher or author record Official online resource
