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Modern Statistical Methodology and Software for Analyzing Spatial
Point Patterns Spatial Point Patterns: Methodology and Applications
with R shows scientific researchers and applied statisticians from
a wide range of fields how to analyze their spatial point pattern
data. Making the techniques accessible to non-mathematicians, the
authors draw on their 25 years of software development experiences,
methodological research, and broad scientific collaborations to
deliver a book that clearly and succinctly explains concepts and
addresses real scientific questions. Practical Advice on Data
Analysis and Guidance on the Validity and Applicability of Methods
The first part of the book gives an introduction to R software,
advice about collecting data, information about handling and
manipulating data, and an accessible introduction to the basic
concepts of point processes. The second part presents tools for
exploratory data analysis, including non-parametric estimation of
intensity, correlation, and spacing properties. The third part
discusses model-fitting and statistical inference for point
patterns. The final part describes point patterns with additional
"structure," such as complicated marks, space-time observations,
three- and higher-dimensional spaces, replicated observations, and
point patterns constrained to a network of lines. Easily Analyze
Your Own Data Throughout the book, the authors use their spatstat
package, which is free, open-source code written in the R language.
This package provides a wide range of capabilities for spatial
point pattern data, from basic data handling to advanced analytic
tools. The book focuses on practical needs from the user's
perspective, offering answers to the most frequently asked
questions in each chapter.
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