1st Edition

Spatial Statistics for Data Science Theory and Practice with R

By Paula Moraga Copyright 2024
    298 Pages 86 Color & 42 B/W Illustrations
    by Chapman & Hall

    298 Pages 86 Color & 42 B/W Illustrations
    by Chapman & Hall

    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


    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.