This book introduces the Topological Weighed Centroid approach and describes some applications in the study of the dynamics of various spatial phenomena with a special emphasis on the spatial analysis of the relationship, influence, and dynamics of geographical phenomena. Offering a comprehensive introduction to the theory and illustrative examples from various kinds of geographical data, this book also takes an in-depth look at more complex case studies, such as the applications of the topological weighed centroid approach in the study of epidemic patterns, cultural processes, criminality, and environmental phenomena.
This book presents the theory and methods of flexible and generalized uncertainty optimization. Particularly, it describes the theory of generalized uncertainty in the context of optimization modeling. The book starts with an overview of flexible and generalized uncertainty optimization. It covers uncertainties that are both associated with lack of information and that more general than stochastic theory, where well-defined distributions are assumed. Starting from families of distributions that are enclosed by upper and lower functions, the book presents construction methods for obtaining flexible and generalized uncertainty input data that can be used in a flexible and generalized uncertainty optimization model. It then describes the development of such a model in detail. All in all, the book provides the readers with the necessary background to understand flexible and generalized uncertainty optimization and develop their own optimization model.
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