Spatial Data Science 1st Edition

Capture the power of location with Spatial Data Science, 1st Edition by Edzer Pebesma and Roger Bivand — an essential guide for anyone working with geospatial data. This authoritative textbook brings together principles of spatial statistics, geospatial analysis, and practical R-based workflows to help you turn maps and coordinates into actionable insight.

Begin with a clear and compelling overview of spatial data types, coordinate reference systems, and data structures. Delve into modern tools in the R ecosystem (including sf and established spatial packages) to manipulate, visualize, and analyze location-based data. Real-world examples — from mapping urban change to modeling environmental gradients — show how to detect patterns, quantify spatial relationships, and build reproducible analytical pipelines.

Designed for practitioners, researchers, and advanced students, this book balances rigorous theory with hands-on application. You’ll learn spatial regression, geostatistics, clustering, and spatial prediction methods with approachable explanations and code-driven demonstrations. Emphasis on data quality, interoperability, and scalable workflows ensures relevance across GIS, environmental science, public health, and location analytics.

If you need a comprehensive, up-to-date resource that bridges GIS practice and data science, Spatial Data Science, 1st Edition provides the tools and techniques to elevate your geospatial work. Invest in a reference that makes complex spatial concepts accessible, actionable, and ready for real-world problems.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.