Spatial Point Patterns

<p><em>Modern Statistical Methodology and Software for Analyzing Spatial Point Patterns</em></p><p><strong>Spatial Point Patterns: Methodology and Applications with R</strong> 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.</p><p><em>Practical Advice on Data Analysis and Guidance on the Validity and Applicability of Methods</em></p><p>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.</p><p><em>Easily Analyze Your Own Data</em></p><p>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.</p>