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Full Description
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.
Contents
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
