Spatial Point Patterns: Methodology and Applications with R.pdf

Spatial Point Patterns: Methodology and Applications with R.pdf


The locations of trees in a forest, new cases of a disease, or gold deposits mapped in a geological survey are all examples of a spatial point pattern-a dataset that provides the observed spatial locations of things or events. Spatial Point Patterns: Methodology and Applications with R describes the modern statistical methodology and software used for analyzing spatial point patterns. The book provides clear explanations and practical advice on powerful statistical methodology. Online supplementary code makes it easy for readers to begin analyzing their own data.

"The analysis of spatial point patterns and processes is an exploding field of applied research across many science and social science disciplines. This is thanks in no small part to the development of open-licensed, well-documented, methodologically sophisticated software implementations. For at least a decade, the authors of this book have been at the forefront of a statistical programming revolution. However, with wider academic access to these point pattern-and-process methods, there has also come a pressing need for clearer guidance on good practice for applied researchers at all stages from graduate studies onward. Expressed in an elegant and accessible style, with substantial references for those wanting directions into the more specialist literature, as well as an excellent set of reproducible, multi-disciplinary case studies, this book provides exactly what is needed. It is highly likely to become a classic." -Andrew Bevan, University College London

Introduction. Collecting and Handling Point Pattern Data. Software Essentials. Inspecting and Exploring Data. Point Processes. Intensity. Correlation. Distances and Empty Spaces. Inference for Poisson Processes. Hypothesis Tests. Model-Fitting Using Second Moments. Gibbs Models. Inference for Multitype Patterns. Inference for Multivariate Marks. Replicated Point Patterns. Point Patterns on a Linear Network.


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