Random Patterns and Structures in Spatial Data

The book presents a general mathematical framework able to detect and to characterize, from a morphological and statistical perspective, patterns hidden in spatial data. The mathematical tool employed is a Gibbs point process with interaction, which permits us to reduce the complexity of the pattern. It presents the framework, step by step, in three major parts: modeling, simulation, and inference. Each of these parts contains a theoretical development followed by applications and examples.

Features:



Presents mathematical foundations for tackling pattern detection and characterisation in spatial data using marked Gibbs point processes with interactions
Proposes a general methodology for morphological and statistical characterisation of patterns based on three branches, probabilistic modeling, stochastic simulation, and statistical inference
Includes application examples from cosmology, environmental sciences, geology, and social networks
Presents theoretical and practical details for the presented algorithms in order to be correctly and efficiently used
Provides access to C++ and R code to encourage the reader to experiment and to develop new ideas
Includes references and pointers to mathematical and applied literature to encourage further study

The book is primarily aimed at researchers in mathematics, statistics, and the above-mentioned application domains. It is accessible for advanced undergraduate and graduate students, so could be used to teach a course. It will be of interest to any scientific researcher interested in formulating a mathematical answer to the always challenging question: what is the pattern hidden in the data?