The book presents a general mathematical framework able to detect and to characterise, from a morphological and statistical perspective, patterns hidden in spatial data. The mathematical tools employed are Gibbs Markov processes, mainly marked point procesess 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.FeaturesPresents mathematical foundations for tackling pattern detection and characterisation in spatial data using marked Gibbs point processes with interactionsIncludes application examples from cosmology, environmental sciences, geology, and social networksPresents theoretical and practical details for the presented algorithms in order to be correctly and efficiently usedProvides access to C++ and R code to encourage the reader to experiment and to develop new ideasIncludes references and pointers to mathematical and applied literature to encourage further studyRandom Patterns and Structures in Spatial Data is primarily aimed at researchers in mathematics, statistics, and the above-mentioned application domains. It is accessible for advanced undergraduate and graduate students and thus 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?
