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Spatial Statistics for Data Science_ Theory and Practice with R (Chapman & Hal.pdf
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Preface
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 starts by providing a comprehensive overview of the types of spatial data and R packages for spatial data retrieval, manipulation, and visualization. Then, it provides a detailed explanation of the theoretical concepts of spatial statistics, along with fully reproducible examples demonstrating how to simulate, describe, and analyze areal, geostatistical, and point pattern data in various applications.
The book combines theory and practice using real-world data science examples such as disease risk mapping, air pollution prediction, species distribution modeling, crime mapping, and real state analyses. The book covers the following topics:
- Spatial data including areal, geostatistical, and point patterns
- Coordinate reference systems and geographical data storages
- R packages for retrieval, manipulation, and visualization of spatial data
- Statistical methods to simulate, describe, and analyze spatial data
- Areal data: neighborhood matrices, spatial autocorrelation, Bayesian spatial models
- Geostatistical data: Gaussian random fields, spatial interpolation, Kriging, model-based geostatistics
- Point patterns: kernel intensity estimation, clustering, log-Gaussian Cox processes
- Fitting and interpreting Bayesian spatial models using the integrated nested Laplace approximation (INLA) and stochastic partial differential equation (SPDE) approaches
- Model assessment criteria and cross-validation
- Effective communication using interactive visualizations and dashboards
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 reproducible for students, researchers, and practitioners.
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