Interpolation of Spatial Data: Some Theory for Kriging - Scholarly

In this review of Interpolation of Spatial Data: Some Theory for Kriging, the reviewer finds a focused, theoretical treatment best suited to researchers and graduate students working with spatial prediction. The book's principal strength is its clear framing of kriging as a mean squared error minimizing predictor and the careful synthesis of past work with new perspectives, which makes it valuable for anyone needing a rigorous foundation in spatial statistics. It is not a how-to manual for applied practitioners who need step-by-step software guidance.

Key Features

• Theoretical focus: Presents kriging as a class of predictors defined by mean squared prediction error, helping readers understand the rationale behind common spatial methods.
• Interdisciplinary relevance: Discusses prediction of random fields with examples and motivations drawn from mining, hydrology, atmospheric sciences, and geography, which helps place methods in context.
• Historical synthesis: Summarizes past work on kriging so readers can see how modern approaches evolved and why certain modeling choices persist.
• New perspectives: Describes new approaches to thinking about kriging that prompt readers to reexamine standard assumptions and modeling frameworks.
• Compact presentation: Stays focused on core theory rather than extensive software or applied case studies, which benefits readers seeking conceptual clarity.

Who It's For

The book is primarily aimed at graduate students, academic researchers, and advanced practitioners in spatial statistics who need a rigorous theoretical account of kriging and spatial prediction. Those working in environmental sciences, geostatistics, or regional modeling will find the synthesis and new conceptual approaches particularly useful.

Practitioners seeking a practical manual with tutorials, code examples, or step-by-step software guidance should look elsewhere; this volume assumes familiarity with probability and statistical theory and focuses on conceptual development rather than applied workflows.

Pros & Cons

Pros

• Clear theoretical framing of kriging as an MSE-minimizing predictor that aids deep understanding.
• Relevant cross-disciplinary examples connect theory to fields like hydrology and atmospheric science.
• Concise summary of past work that helps situate current methods within a historical context.
• Introduces thoughtful new perspectives that encourage critical examination of standard models.

Cons

• Limited practical content and no software guidance, which reduces utility for readers wanting immediate applied instruction.

Specifications

Our Verdict

Interpolation of Spatial Data is a strong, theory-forward resource for those who need a clear, synthesized account of kriging and spatial prediction. It offers good value to academics and advanced students who want conceptual depth, though applied users seeking tutorials or software examples should supplement it with practical guides.

Frequently Asked Questions

Is this book suitable for beginners?
The book assumes a background in probability and statistics, so beginners without that foundation may struggle.

Does it include software examples or code?
No, the focus is theoretical and it does not provide software tutorials or code examples.

Which fields will benefit most?
Researchers in hydrology, atmospheric sciences, mining, and geography will find the theoretical perspectives most applicable.