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.
1143855771
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.
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Spatial Statistics for Data Science: Theory and Practice with R

Spatial Statistics for Data Science: Theory and Practice with R

by Paula Moraga
Spatial Statistics for Data Science: Theory and Practice with R
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Spatial Statistics for Data Science: Theory and Practice with R

Spatial Statistics for Data Science: Theory and Practice with R

by Paula Moraga

Overview

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.

Product Details

ISBN-13: 9781032633510
Publisher: CRC Press
Publication date: 12/08/2023
Series: Chapman & Hall/CRC Data Science Series
Pages: 298
Product dimensions: 6.12(w) x 9.19(h) x (d)

About the Author

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.

Table of 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

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