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Full Description
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.
Contents
BASICS: Introduction. Software Essentials. Collecting and Handling Point Pattern Data. Inspecting and Exploring Data. Point Process Methods. EXPLORATORY DATA ANALYSIS: Intensity. Correlation. Spacing. STATISTICAL INFERENCE: Poisson Models. Hypothesis Tests and Simulation Envelopes. Model Validation. Cluster and Cox Models. Gibbs Models. Patterns of Several Types of Points. ADDITIONAL STRUCTURE: Higher-Dimensional Spaces and Marks. Replicated Point Patterns and Designed Experiments. Point Patterns on a Linear Network.
