Spatial Data Science: With Applications in R

Spatial Data Science provides an introduction to fundamental aspects of spatial data, illustrative examples using the R language, and statistical modeling approaches using real-world data examples. It covers geometries, coordinate reference systems, the Earth's roundness, and attribute relationships, equipping readers to avoid common errors in spatial data analysis. The book also discusses core spatial software packages for R and various plotting approaches for spatial maps.

Format: Hardback
Length: 300 pages
Publication date: 10 May 2023
Publisher: Taylor & Francis Ltd

Spatial Data Science is a comprehensive guide that delves into the fundamental aspects of spatial data that every data scientist should possess before embarking on their journey with spatial data. It encompasses a wide range of topics, including the representation of geometries, coordinate reference systems (projections, datums), the spherical nature of the Earth and its implications for analysis, and the relationship between attributes of geometries.

In the second part of the book, these concepts are illustrated through practical data science examples using the R language. The authors showcase how these principles can be applied to real-world scenarios, providing valuable insights and techniques for analyzing spatial data.

The third part of the book focuses on statistical modeling approaches, employing real-world data examples to demonstrate their effectiveness. By the end of this book, readers will have gained a deep understanding of spatial data analysis and will be well-equipped to avoid common errors and pitfalls.

Furthermore, the book provides a detailed explanation of the core spatial software packages for R, namely sf for simple feature access and stars for raster and vector data cubes. It showcases how geometrical operations change when transitioning from a flat space to the surface of a sphere, which is a common assumption in spatial analysis. Separate chapters are dedicated to various plotting approaches for spatial maps, offering a range of options to visualize and interpret spatial data effectively.

Moreover, the book addresses the challenges of handling large vector or raster datasets, locally, in databases, or in the cloud. It provides practical solutions and techniques for managing and analyzing these datasets efficiently, ensuring that data scientists can work with large-scale spatial data without compromising performance.

To enhance the learning experience, the data used and all code examples are freely available online from the book's official website at https://r-spatial.org/book/. Additionally, the solutions to the exercises are provided in a separate repository at https://edzer.github.io/sdsr_exercises/, allowing readers to practice and apply the concepts learned in the book.

In summary, Spatial Data Science is an essential resource for data scientists, researchers, and practitioners interested in spatial data analysis. It offers a comprehensive and practical approach to understanding spatial data, covering a wide range of topics and providing hands-on examples using the R language. By reading this book, readers will gain the skills and knowledge necessary to effectively analyze and interpret spatial data, enabling them to make informed decisions and drive innovation in their fields.

Weight: 740g
Dimension: 234 x 156 (mm)
ISBN-13: 9781138311183