Statistical Analysis of Spatial and Spatio Temporal Point Patterns 3rd Edition by Peter J Diggle ISBN 1466560231 ‎ 978-1466560239

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ISBN 10: 1466560231

ISBN 13: ‎ 978-1466560239

Author: Peter J Diggle

Written by a prominent statistician and author, the first edition of this bestseller broke new ground in the then emerging subject of spatial statistics with its coverage of spatial point patterns. Retaining all the material from the second edition and adding substantial new material, Statistical Analysis of Spatial and Spatio-Temporal Point Patterns, Third Edition presents models and statistical methods for analyzing spatially referenced point process data.

Reflected in the title, this third edition now covers spatio-temporal point patterns. It explores the methodological developments from the last decade along with diverse applications that use spatio-temporally indexed data. Practical examples illustrate how the methods are applied to analyze spatial data in the life sciences.

This edition also incorporates the use of R through several packages dedicated to the analysis of spatial point process data. Sample R code and data sets are available on the author’s website.

Statistical Analysis of Spatial and Spatio Temporal Point Patterns 3rd Table of contents:

Part I: Introduction to Point Patterns

1. Introduction to Point Process Theory

   • Definition of Point Patterns

   • Types of Point Processes: Spatial and Spatio-Temporal

   • Applications of Point Process Models in Various Fields (e.g., ecology, epidemiology, geology)

2. Overview of Statistical Methods for Point Patterns

   • Classical Statistical Methods

   • Modern Approaches in Spatial Statistics

   • The Role of Stochastic Processes in Point Pattern Analysis

Part II: Spatial Point Patterns

3. Basic Concepts in Spatial Point Patterns

   • Spatial Homogeneity and Stationarity

   • Intensity and Spatial Distribution

   • Nearest-Neighbor Distances and Their Statistical Properties

4. Analysis of Spatial Point Patterns

   • The K-function and the Pair Correlation Function

   • Estimation of the Intensity Function

   • Methods for Detecting Clustering or Regularity

   • Summary Statistics: Ripley’s K, L-function, and Quadrat Methods

5. Modeling Spatial Point Patterns

   • Poisson Point Processes and Their Properties

   • Neyman-Scott Processes

   • Cox Processes and Their Applications

   • Fitting Point Process Models to Data

6. Testing and Diagnostics for Spatial Point Patterns

   • Goodness-of-fit Tests for Spatial Models

   • Simulation-based Diagnostic Tools

   • Residual Analysis in Spatial Models

Part III: Spatio-Temporal Point Patterns

7. Introduction to Spatio-Temporal Point Patterns

   • The Concept of Spatio-Temporal Data

   • Differences Between Spatial and Spatio-Temporal Processes

   • Examples of Spatio-Temporal Point Patterns in Nature and Society

8. Analysis of Spatio-Temporal Point Patterns

   • Temporal Point Processes: Renewal and Counting Processes

   • Spatio-Temporal K-functions and Their Extensions

   • Space-Time Interaction Models

   • Time-Dependent and Time-Inhomogeneous Point Processes

9. Modeling Spatio-Temporal Point Patterns

   • Spatio-Temporal Poisson Processes

   • Non-Homogeneous Spatio-Temporal Point Processes

   • Multivariate Spatio-Temporal Point Processes

   • Simulation of Spatio-Temporal Point Processes

10. Statistical Inference for Spatio-Temporal Data

    • Maximum Likelihood Estimation (MLE) for Spatio-Temporal Models

    • Bayesian Methods for Spatio-Temporal Point Patterns

    • Computational Methods and Algorithms for Model Fitting

Part IV: Advanced Topics in Point Pattern Analysis

11. Point Process Models for Clustered Data

    • Thomas Processes, Gibbs Processes, and Their Applications

    • Mixed Poisson Models

    • The Role of Interactions in Clustering

12. Spatial-Temporal Covariates and Point Patterns

    • Incorporating Environmental Covariates into Spatial Models

    • Temporal Covariates and Their Influence on Point Patterns

    • Geostatistical Methods for Modeling Covariates

13. Applications of Point Pattern Analysis

    • Ecology and the Study of Animal Movement

    • Epidemiology: Disease Spread and Event Modeling

    • Geophysics: Earthquake Patterns and Fault Lines

    • Urban Studies: Crime Mapping and Infrastructure Development

14. Multivariate Point Processes

    • Modeling Interactions Between Multiple Types of Events

    • Cross-Interactions and Cross-K Functions

    • Multivariate Spatio-Temporal Models

Part V: Computational Methods and Software

15. Simulation and Fitting of Point Process Models

    • Monte Carlo Methods in Point Process Analysis

    • Simulation of Spatial and Spatio-Temporal Models

    • Model Fitting: Practical Approaches and Software Tools

16. Software for Point Pattern Analysis

    • Overview of Statistical Software for Spatial Analysis (e.g., R, Python, Matlab)

    • Point Process Packages: spatstat, spatstat.core, etc.

    • Code Examples and Best Practices for Implementing Point Process Analysis

Conclusion

• Summary of Key Concepts and Methods in Point Pattern Analysis

• Future Directions in Spatial and Spatio-Temporal Point Pattern Analysis

• The Role of Point Patterns in the Emerging Field of Big Data Analytics

Appendices

• Appendix A: Mathematical Foundations of Point Process Theory

• Appendix B:

  Further Reading and References in Point Pattern Analysis

• Appendix C: List of Statistical Tests for Spatial Data
• Index

 

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Peter J Diggle,Statistical Analysis,Spatio Temporal