This book argues that complexity theory offers new departures for (spatial-) economic modelling. It offers a broad overview of recent advances in non-linear dynamics (catastrophe theory, chaos theory, evolutionary theory and so forth) and illustrates the relevance of this new paradigm on the basis of several illustrations in the area of space-economy. The empirical limitations - inherent in the use of non-linear dynamic systems approaches - are also addressed. Next, the application potential of biocomputing (in particular, neural networks and evolutionary algorithms) is stressed, while various empirical model results are presented. The book concludes with an agenda for further research.

This ground-breaking Handbook presents a state-of-the-art exploration of entropy, complexity and spatial dynamics from fundamental theoretical, empirical and methodological perspectives. It considers how foundational theories can contribute to new advances, including novel modeling and empirical insights at different sectoral, spatial and temporal scales.

Simulation models are increasingly used to investigate processes and solve practical problems in a wide variety of disciplines eg. climatology, ecology, hydrology, geomorphology, engineering. Environmental Modelling: A Practical Approach addresses the development, testing and application of such models, which apply across traditional boundaries, and demonstrate how interactions across these boundaries can be beneficial. Provides a general overview of methods and approaches as well as focusing on key subject areas written by leading practitioners in the field Assesses the advantages and disadvantages of different models used and provides case studies supported with data, output, tutorial exercises and links to the model and/or model applications via the book's website Covers major developments in the field, eg. the use of GIS and remote sensing techniques, and scaling issues As associated website contains colour images, as well as links to www resources

Introduction to Modeling and Simulation with MATLAB and Python is intended for students and professionals in science, social science, and engineering that wish to learn the principles of computer modeling, as well as basic programming skills. The book content focuses on meeting a set of basic modeling and simulation competencies that were developed as part of several National Science Foundation grants. Even though computer science students are much more expert programmers, they are not often given the opportunity to see how those skills are being applied to solve complex science and engineering problems and may also not be aware of the libraries used by scientists to create those models. The book interleaves chapters on modeling concepts and related exercises with programming concepts and exercises. The authors start with an introduction to modeling and its importance to current practices in the sciences and engineering. They introduce each of the programming environments and the syntax used to represent variables and compute mathematical equations and functions. As students gain more programming expertise, the authors return to modeling concepts, providing starting code for a variety of exercises where students add additional code to solve the problem and provide an analysis of the outcomes. In this way, the book builds both modeling and programming expertise with a "just-in-time" approach so that by the end of the book, students can take on relatively simple modeling example on their own. Each chapter is supplemented with references to additional reading, tutorials, and exercises that guide students to additional help and allows them to practice both their programming and analytical modeling skills. In addition, each of the programming related chapters is divided into two parts – one for MATLAB and one for Python. In these chapters, the authors also refer to additional online tutorials that students can use if they are having difficulty with any of the topics. The book culminates with a set of final project exercise suggestions that incorporate both the modeling and programming skills provided in the rest of the volume. Those projects could be undertaken by individuals or small groups of students. The companion website at http://www.intromodeling.com provides updates to instructions when there are substantial changes in software versions, as well as electronic copies of exercises and the related code. The website also offers a space where people can suggest additional projects they are willing to share as well as comments on the existing projects and exercises throughout the book. Solutions and lecture notes will also be available for qualifying instructors.

Human behavior and action is closely related to a corresponding social and spatial environment. The nature of relations in their structures, functions, and temporal domains is characterized by a duality of contexts, an interrelatedness of the individual and local with the social and global. Causes, influences, and effects are mutual - and this at different scales. This book presents approaches that deal with interactions within and between the social and spatial realm, focusing on human behavior, land use change, and energy and infrastructure issues from an agent-based perspective. (Series: Geosimulation - Vol. 2)

For today's students, learning to model the dynamics of complex systems is increasingly important across nearly all engineering disciplines. First published in 2001, Forbes T. Brown's Engineering System Dynamics: A Unified Graph-Centered Approach introduced students to a unique and highly successful approach to modeling system dynamics using bond g

There are various techniques to optimize either structural parameters, or structural controllers, but there are not many techniques that can simultaneously optimize the structural parameters and controller. The advantage of integrating the structural and controller optimization problems is that structure and controller interaction is taken into account in the design process and a more efficient overall design (lower control force/lighter weight) can be achieved, and also multidisciplinary design optimization can be performed. The down side is that the combined optimization problem is more difficult to formulate and solve, and computations are increased. This volume is a comprehensive treatment of dynamic analysis and control techniques in structural dynamic systems and the wide variety of issues and techniques that fall within this broad area, including the interactions between structural control systems and structural system parameters.

'Geographical information science' is not merely a technical subject but also poses theoretical questions on the nature of geographic representation and whether there exist limits on the ability of GI systems to deal with certain objects and issues. This book presents the debate surrounding technical GIS and theory of representation from an 'inside' GIS perspective. Chapters are authored by leading researchers from a range of fields including geographers, planners, ecologists and computer scientists from Europe and North America.