Spatial Data Science with R Applications

This resource is the digital version of the book titled Spatial Data Science: With Applications in R by Pebesma and Bivand.

The authors introduce the book as follows:

This book introduces and explains the concepts underlying spatial data: points, lines, polygons, rasters, coverages, geometry attributes, data cubes, reference systems, as well as higher-level concepts including how attributes relate to geometries and how this affects analysis.

The target user of the resource is:

The book aims at data scientists who want to get a grip on using spatial data in their analysis. To exemplify how to do things, it uses R. In future editions we hope to extend this with examples using Python (see, e.g., Bivand 2022a) and Julia.

Topics covered

Spatial Data

1. Getting Started
2. Coordinates
3. Geometries
4. Spherical Geometries
5. Attributes and Support
6. Data Cubes

R for Spatial Data Science

7. Introduction to sf and stars
8. Plotting spatial data
9. Large data and cloud native

Models for Spatial Data

10. Statistical modelling of spatial data
11. Point Pattern Analysis
12. Spatial Interpolation
13. Multivariate and Spatiotemporal Geostatistics
14. Proximity and Areal Data
15. Measures of Spatial Autocorrelation
16. Spatial Regression
17. Spatial Econometrics Models

Full reference
Pebesma, E.; Bivand, R. (2023). Spatial Data Science: With Applications in R (1st ed.). 314 pages. Chapman and Hall/CRC, Boca Raton. https://doi.org/10.1201/9780429459016