Spatial Statistics for Data Science Theory and Practice with R

Part 1: Spatial data

1. Types of spatial data

2. Spatial data in R

3. The sf package for spatial vector data

4. The terra package for raster and vector data

5. Making maps with R

6. R packages to download open spatial data

Part 2: Areal data

7. Spatial neighborhood matrices

8. Spatial autocorrelation

9. Bayesian spatial models

10. Disease risk modeling

11. Areal data issues

Part 3: Geostatistical data

12. Geostatistical data

13. Spatial interpolation methods

14. Kriging

15. Model-based geostatistics

16. Methods assessment

Part 4: Spatial point patterns

17. Spatial point patterns

18. The spatstat package

19. Spatial point processes and simulation

20. Complete Spatial Randomness

21. Intensity estimation

22. The K-function

23. Point process modeling

Appendix A. The R software

Biography

Paula Moraga is Professor of Statistics at King Abdullah University of Science and Technology (KAUST). She received her Master's in Biostatistics from Harvard University and her Ph.D. in Mathematics from the University of Valencia. Dr. Moraga develops innovative statistical methods and open-source software for spatial data analysis and health surveillance, including R packages for spatio-temporal modeling, detection of clusters, and travel-related spread of disease. Her work has directly informed strategic policy in reducing the burden of diseases such as malaria and cancer in several countries. Dr. Moraga has published extensively in leading journals, and serves as an Associate Editor of the Journal of the Royal Statistical Society Series A. She is the author of the book Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny (Chapman & Hall/CRC). Dr. Moraga received the prestigious Letten Prize for her pioneering research in disease surveillance, and her significant contributions to the development of sustainable solutions for health and environment globally.

"Spatial Statistics for Data Science: Theory and Practice with R is a well-crafted guide that explores visualization techniques and statistical methods, essential for analyzing spatial data using R. The book provides a detailed overview of typical types of spatial data and the R packages necessary for their retrieval, manipulation, and visualization. Then, it delves into the modeling and methodological aspects of spatial statistics while maintaining a focus on practical applications, demonstrated through fully reproducible examples using publicly accessible spatial data."
-Chae Young Lim in Journal of the American Statistical Association, October 2024