1st Edition
Spatial Statistics for Data Science Theory and Practice with R
Spatial data is crucial to improve decision-making in a wide range of fields including environment, health, ecology, urban planning, economy, and society. Spatial Statistics for Data Science: Theory and Practice with R describes statistical methods, modeling approaches, and visualization techniques to analyze spatial data using R. The book provides a comprehensive overview of the varying types of spatial data, and detailed explanations of the theoretical concepts of spatial statistics, alongside fully reproducible examples which demonstrate how to simulate, describe, and analyze spatial data in various applications. Combining theory and practice, the book includes real-world data science examples such as disease risk mapping, air pollution prediction, species distribution modeling, crime mapping, and real state analyses. The book utilizes publicly available data and offers clear explanations of the R code for importing, manipulating, analyzing, and visualizing data, as well as the interpretation of the results. This ensures contents are easily accessible and fully reproducible for students, researchers, and practitioners.
Key Features:
- Describes R packages for retrieval, manipulation, and visualization of spatial data.
- Offers a comprehensive overview of spatial statistical methods including spatial autocorrelation, clustering, spatial interpolation, model-based geostatistics, and spatial point processes.
- Provides detailed explanations on how to fit and interpret Bayesian spatial models using the integrated nested Laplace approximation (INLA) and stochastic partial differential equation (SPDE) approaches.
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.
