Spatial Statistics for Data Science with R by Paula Moraga
Spatial Statistics for Data Science with R - Table of Contents
- PART I: 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 II: AREAL DATA
- 7. Spatial Neighborhood Matrices
- 8. Spatial Autocorrelation
- 9. Bayesian Spatial Models
- 10. Disease Risk Modeling
- 11. Areal Data Issues
- PART III: GEOSTATISTICAL DATA
- 12. Geostatistical Data
- 13. Spatial Interpolation Methods
- 14. Kriging
- 15. Model-Based Geostatistics
- 16. Methods Assessment
- PART IV: 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
What You Will Learn in Spatial Statistics for Data Science with R
Spatial Statistics for Data Science with R by Paula Moraga is an essential, cutting-edge textbook designed to give researchers and analysts the theoretical and computational tools needed to analyze geographically referenced data. Designed for advanced courses and professional practice, this comprehensive guide breaks down complex spatial modeling techniques—including point pattern analysis, spatial autocorrelation, geostatistical interpolation, areal disease mapping, and Bayesian spatial modeling using INLA and sf—into clear, reproducible lessons.
Perfect for graduate students, biostatisticians, epidemiologists, data scientists, and environmental researchers, this text bridges the gap between spatial statistical theory and hands-on implementation. Dr. Moraga systematically walks readers through spatial data handling, interactive web maps, spatial regression models, and spatio-temporal modeling using modern R packages like sf, terra, and R-INLA. Whether you are mapping disease risk, analyzing climate variables, or quantifying spatial clustering, this text provides a clear, structured roadmap through every topic.
Recognized globally for its clarity, practical code workflows, and real-world applications, it stands as one of the best spatial statistics with R books pdf available for self-study. It systematically equips learners with advanced analytical methods for mastering spatial data science with confidence.
Book Details & Specifications
Title:
Spatial Statistics for Data Science with R by Paula Moraga
Publisher:
Chapman and Hall/CRC
Year:
2023
Pages:
285
Type:
PDF
Language:
English
ISBN-10 #:
1032633514
ISBN-13 #:
978-1032633510
License:
CC BY-NC-ND 4.0
Amazon:
Amazon
About the Author: Paula Moraga
The author Paula Moraga
is an Associate Professor of Statistics at King Abdullah University of Science and Technology (KAUST) and Principal Investigator of the GeoHealth research group. She earned her Ph.D. in Mathematics from the University of Valencia, specializing in spatial biostatistics, disease mapping, and Bayesian modeling.
An internationally recognized leader in spatial epidemiology and open-source software development, Dr. Moraga has developed multiple R packages for spatial data analysis and interactive visualization. Her pioneering work on spatial statistics and spatio-temporal disease mapping equips researchers worldwide with powerful computational frameworks for public health surveillance and environmental decision-making.
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