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**Statistical analysis and modelling of spatial point patterns.**
*(English)*
Zbl 1197.62135

Statistics in Practice. Chichester: John Wiley & Sons (ISBN 978-0-470-01491-2/hbk; 978-0-470-72516-0/ebook). xix, 534 p. (2008).

Publisher’s description: Spatial point processes are mathematical models used to describe and analyse the geometrical structure of patterns formed by objects that are irregularly or randomly distributed in one-, two- or three-dimensional spaces. Examples include locations of trees in a forest, blood particles on a glass plate, galaxies in the universe, and particle centres in samples of material. Numerous aspects of the nature of a specific spatial point pattern may be described using appropriate statistical methods. This text provides a practical guide to the use of these specialised methods. The application-oriented approach helps to demonstrate the benefits of this increasingly popular branch of statistics to a broad audience.

The book provides an introduction to spatial point patterns for researchers across numerous areas of applications. It adopts an extremely accessible style, allowing the non-statistician complete understanding, describes the process of extracting knowledge from the data, emphasising marked point processes, demonstrates the analysis of complex data sets, using applied examples from areas including biology, forestry, and materials science, and features a supplementary website containing example datasets.

This text is ideally suited for researchers in many areas of applications, including environmental statistics, ecology, physics, material science, geostatistics, and biology. It is also suitable for students of statistics, mathematics, computer science, biology and geoinformatics.

The book provides an introduction to spatial point patterns for researchers across numerous areas of applications. It adopts an extremely accessible style, allowing the non-statistician complete understanding, describes the process of extracting knowledge from the data, emphasising marked point processes, demonstrates the analysis of complex data sets, using applied examples from areas including biology, forestry, and materials science, and features a supplementary website containing example datasets.

This text is ideally suited for researchers in many areas of applications, including environmental statistics, ecology, physics, material science, geostatistics, and biology. It is also suitable for students of statistics, mathematics, computer science, biology and geoinformatics.