Spatial Statistics for Data Science: Theory and Practice with R

Spatial data is essential for decision-making in various fields, and Spatial Statistics for Data Science: Theory and Practice with R provides a comprehensive guide to analyzing spatial data using R. The book covers theoretical concepts, modeling approaches, and visualization techniques, with examples from real-world data science applications. It utilizes publicly available data and offers clear explanations of R code, making it accessible to students, researchers, and practitioners.

Format: Hardback
Length: 280 pages
Publication date: 08 December 2023
Publisher: Taylor & Francis Ltd

Spatial data holds immense importance in enhancing decision-making across a diverse array of fields, encompassing the environment, health, ecology, urban planning, economy, and society. In order to effectively harness the power of spatial data, Spatial Statistics for Data Science: Theory and Practice with R emerges as a valuable resource. This comprehensive book delves into statistical methodologies, modeling approaches, and visualization techniques, empowering readers to analyze spatial data utilizing the R programming language.

The book offers a comprehensive exploration of the diverse types of spatial data, providing detailed explanations of theoretical concepts in spatial statistics. It accompanies fully reproducible examples, showcasing how to simulate, describe, and analyze spatial data across various applications. By seamlessly combining theory and practice, the book presents real-world data science examples, including disease risk mapping, air pollution prediction, species distribution modeling, crime mapping, and real estate analyses.

Moreover, the book leverages publicly available data, ensuring that its contents are readily accessible and fully reproducible for students, researchers, and practitioners alike. It provides clear explanations of R code for importing, manipulating, analyzing, and visualizing data, accompanied by insightful interpretations of the results. This approach ensures that the book serves as a valuable tool for those seeking to delve into the realm of spatial statistics and apply its principles to real-world problems.

Key Features:
1. Describes R packages for efficient retrieval, manipulation, and visualization of spatial data.
2. Provides a comprehensive overview of spatial statistical methods, encompassing spatial autocorrelation, clustering, spatial interpolation, model-based geostatistics, and spatial point processes.
3. Delves into detailed explanations on fitting and interpreting Bayesian spatial models using the integrated nested Laplace approximation (INLA) and stochastic partial differential equation (SPDE) approaches.

By leveraging the power of spatial statistics and R, this book empowers readers to unlock the hidden insights buried within spatial data, driving informed decision-making and fostering positive societal change in a wide range of fields.

Dimension: 234 x 156 (mm)
ISBN-13: 9781032633510