In the real world, there are numerous and various events that occur on and alongside networks, including the occurrence of traffic accidents on highways, the location of stores alongside roads, the incidence of crime on streets and the contamination along rivers. In order to carry out analyses of those events, the researcher needs to be familiar with a range of specific techniques. Spatial Analysis Along Networks provides a practical guide to the necessary statistical techniques and their computational implementation. Each chapter illustrates a specific technique, from Stochastic Point Processes on a Network and Network Voronoi Diagrams, to Network K-function and Point Density Estimation Methods, and the Network Huff Model. The authors also discuss and illustrate the undertaking of the statistical tests described in a Geographical Information System (GIS) environment as well as demonstrating the user-friendly free software package SANET. Spatial Analysis Along Networks: Presents a much-needed practical guide to statistical spatial analysis of events on and alongside a network, in a logical, user-friendly order. Introduces the preliminary methods involved, before detailing the advanced, computational methods, enabling the readers a complete understanding of the advanced topics. Dedicates a separate chapter to each of the major techniques involved. Demonstrates the practicalities of undertaking the tests described in the book, using a GIS. Is supported by a supplementary website, providing readers with a link to the free software package SANET, so they can execute the statistical methods described in the book. Students and researchers studying spatial statistics, spatial analysis, geography, GIS, OR, traffic accident analysis, criminology, retail marketing, facility management and ecology will benefit from this book.
Evolving from years of teaching experience by one of the top experts in vegetation ecology, Data Analysis in Vegetation Ecology aims to explain the background and basics of mathematical (mainly multivariate) analysis of vegetation data. The book lays out the basic operations involved in the analysis, the underlying hypotheses, aims and points of views. It conveys the message that each step in the calculations has a specific, straightforward meaning and that patterns and processes known by ecologists often find their counterpart in mathematical operations and functions. The first chapters introduce the elementary concepts and operations and relate them to real-world phenomena and problems. Later chapters concentrate on combinations of methods to reveal surprising features in data sets. Showing how to find patterns in time series, how to generate simple dynamic models, how to reveal spatial patterns and related occurrence probability maps.
In the spatial or spatio-temporal context, specifying the correct covariance function is fundamental to obtain efficient predictions, and to understand the underlying physical process of interest. This book focuses on covariance and variogram functions, their role in prediction, and appropriate choice of these functions in applications. Both recent and more established methods are illustrated to assess many common assumptions on these functions, such as, isotropy, separability, symmetry, and intrinsic correlation. After an extensive introduction to spatial methodology, the book details the effects of common covariance assumptions and addresses methods to assess the appropriateness of such assumptions for various data structures. Key features: An extensive introduction to spatial methodology including a survey of spatial covariance functions and their use in spatial prediction (kriging) is given. Explores methodology for assessing the appropriateness of assumptions on covariance functions in the spatial, spatio-temporal, multivariate spatial, and point pattern settings. Provides illustrations of all methods based on data and simulation experiments to demonstrate all methodology and guide to proper usage of all methods. Presents a brief survey of spatial and spatio-temporal models, highlighting the Gaussian case and the binary data setting, along with the different methodologies for estimation and model fitting for these two data structures. Discusses models that allow for anisotropic and nonseparable behaviour in covariance functions in the spatial, spatio-temporal and multivariate settings. Gives an introduction to point pattern models, including testing for randomness, and fitting regular and clustered point patterns. The importance and assessment of isotropy of point patterns is detailed. Statisticians, researchers, and data analysts working with spatial and space-time data will benefit from this book as well as will graduate students with a background in basic statistics following courses in engineering, quantitative ecology or atmospheric science.
This book addresses the stochastic modeling of telecommunication networks, introducing the main mathematical tools for that purpose, such as Markov processes, real and spatial point processes and stochastic recursions, and presenting a wide list of results on stability, performances and comparison of systems. The authors propose a comprehensive mathematical construction of the foundations of stochastic network theory: Markov chains, continuous time Markov chains are extensively studied using an original martingale-based approach. A complete presentation of stochastic recursions from an ergodic theoretical perspective is also provided, as well as spatial point processes. Using these basic tools, stability criteria, performance measures and comparison principles are obtained for a wide class of models, from the canonical M/M/1 and G/G/1 queues to more sophisticated systems, including the current “hot topics” of spatial radio networking, OFDMA and real-time networks. Contents 1. Introduction. Part 1: Discrete-time Modeling 2. Stochastic Recursive Sequences. 3. Markov Chains. 4. Stationary Queues. 5. The M/GI/1 Queue. Part 2: Continuous-time Modeling 6. Poisson Process. 7. Markov Process. 8. Systems with Delay. 9. Loss Systems. Part 3: Spatial Modeling 10. Spatial Point Processes.
A ground-up approach to explaining dynamic spatial modelling for an interdisciplinary audience. Across broad areas of the environmental and social sciences, simulation models are an important way to study systems inaccessible to scientific experimental and observational methods, and also an essential complement to those more conventional approaches. The contemporary research literature is teeming with abstract simulation models whose presentation is mathematically demanding and requires a high level of knowledge of quantitative and computational methods and approaches. Furthermore, simulation models designed to represent specific systems and phenomena are often complicated, and, as a result, difficult to reconstruct from their descriptions in the literature. This book aims to provide a practical and accessible account of dynamic spatial modelling, while also equipping readers with a sound conceptual foundation in the subject, and a useful introduction to the wide-ranging literature. Spatial Simulation: Exploring Pattern and Process is organised around the idea that a small number of spatial processes underlie the wide variety of dynamic spatial models. Its central focus on three ‘building-blocks’ of dynamic spatial models – forces of attraction and segregation, individual mobile entities, and processes of spread – guides the reader to an understanding of the basis of many of the complicated models found in the research literature. The three building block models are presented in their simplest form and are progressively elaborated and related to real world process that can be represented using them. Introductory chapters cover essential background topics, particularly the relationships between pattern, process and spatiotemporal scale. Additional chapters consider how time and space can be represented in more complicated models, and methods for the analysis and evaluation of models. Finally, the three building block models are woven together in a more elaborate example to show how a complicated model can be assembled from relatively simple components. To aid understanding, more than 50 specific models described in the book are available online at patternandprocess.org for exploration in the freely available Netlogo platform. This book encourages readers to develop intuition for the abstract types of model that are likely to be appropriate for application in any specific context. Spatial Simulation: Exploring Pattern and Process will be of interest to undergraduate and graduate students taking courses in environmental, social, ecological and geographical disciplines. Researchers and professionals who require a non-specialist introduction will also find this book an invaluable guide to dynamic spatial simulation.
This book serves as a comprehensive source of asymptotic results for econometric models with deterministic exogenous regressors. Such regressors include linear (more generally, piece-wise polynomial) trends, seasonally oscillating functions, and slowly varying functions including logarithmic trends, as well as some specifications of spatial matrices in the theory of spatial models. The book begins with central limit theorems (CLTs) for weighted sums of short memory linear processes. This part contains the analysis of certain operators in Lp spaces and their employment in the derivation of CLTs. The applications of CLTs are to the asymptotic distribution of various estimators for several econometric models. Among the models discussed are static linear models with slowly varying regressors, spatial models, time series autoregressions, and two nonlinear models (binary logit model and nonlinear model whose linearization contains slowly varying regressors). The estimation procedures include ordinary and nonlinear least squares, maximum likelihood, and method of moments. Additional topical coverage includes an introduction to operators, probabilities, and linear models; Lp-approximable sequences of vectors; convergence of linear and quadratic forms; regressions with slowly varying regressors; spatial models; convergence; nonlinear models; and tools for vector autoregressions.
While numerous books have been written on earthquakes, earthquake resistance design, and seismic analysis and design of structures, none have been tailored for advanced students and practitioners, and those who would like to have most of the important aspects of seismic analysis in one place. With this book, readers will gain proficiencies in the following: fundamentals of seismology that all structural engineers must know; various forms of seismic inputs; different types of seismic analysis like, time and frequency domain analyses, spectral analysis of structures for random ground motion, response spectrum method of analysis; equivalent lateral load analysis as given in earthquake codes; inelastic response analysis and the concept of ductility; ground response analysis and seismic soil structure interaction; seismic reliability analysis of structures; and control of seismic response of structures. Provides comprehensive coverage, from seismology to seismic control Contains useful empirical equations often required in the seismic analysis of structures Outlines explicit steps for seismic analysis of MDOF systems with multi support excitations Works through solved problems to illustrate different concepts Makes use of MATLAB, SAP2000 and ABAQUAS in solving example problems of the book Provides numerous exercise problems to aid understanding of the subject As one of the first books to present such a comprehensive treatment of the topic, Seismic Analysis of Structures is ideal for postgraduates and researchers in Earthquake Engineering, Structural Dynamics, and Geotechnical Earthquake Engineering. Developed for classroom use, the book can also be used for advanced undergraduate students planning for a career or further study in the subject area. The book will also better equip structural engineering consultants and practicing engineers in the use of standard software for seismic analysis of buildings, bridges, dams, and towers. Lecture materials for instructors available at www.wiley.com/go/dattaseismic
Occupation Analysis in Practice is the essential book for all future and current occupational therapists. It offers a practical approach to the analysis of occupations in real world practice. The book frames occupation as the key component for analysis and builds upon previous work limited to analysis at the activity level. It examines the interests, goals, abilities and contexts of individuals, groups, institutions and communities, along with the demands of the occupation. It presents examples of occupation analysis in different practice context including working with children, health promotion, indigenous health, medico-legal practice; mental health and occupational rehabilitation. The book has four sections. Section 1 introduces theoretical perspectives of the concept of occupation analysis and how such analysis relates to particular models of Occupational Therapy practice and the generic World Health Organisation International Classification of Functioning, Disability and Health. Section 2 discusses analysis of particular components of occupation that support practice. These include culture, spirituality, home and community environments as well as self-care and leisure. Section 3 applies analysis of occupations to particular specialties encountered in practice. Section 4 considers the application of Occupation Analysis within professional reasoning and goal setting. FEATURES International team of contributors Examples of occupation analysis proforma Application to a wide range of practice areas. Glossary of key terms Incudes the International Classification of Functioning, Disability and Health.
Clear, up-to-date coverage of methods for analyzing geographical information in a GIS context Geographic Information Analysis, Second Edition is fully updated to keep pace with the most recent developments of spatial analysis in a geographic information systems (GIS) environment. Still focusing on the universal aspects of this science, this revised edition includes new coverage on geovisualization and mapping as well as recent developments using local statistics. Building on the fundamentals, this book explores such key concepts as spatial processes, point patterns, and autocorrelation in area data, as well as in continuous fields. Also addressed are methods for combining maps and performing computationally intensive analysis. New chapters tackle mapping, geovisualization, and local statistics, including the Moran Scatterplot and Geographically Weighted Regression (GWR). An appendix provides a primer on linear algebra using matrices. Complete with chapter objectives, summaries, «thought exercises,» explanatory diagrams, and a chapter-by-chapter bibliography, Geographic Information Analysis is a practical book for students, as well as a valuable resource for researchers and professionals in the industry.
Transnational spatial relations offer a key point from which to study the geographies of contemporary globalization. This book assesses the possible cross-fertilization between two of the most notable analytical frameworks – the world city network framework and the global commodity chain framework. Transnational spatial relations have become a key analytical lens through which to study the geographies of contemporary globalization Brings together contributions of key researchers from different backgrounds and different parts of the world Offers a set of original approaches to the study of the networked geography of globalization
An overview of the methods used for risk analysis in a variety of industrial sectors, with a particular focus on the consideration of human aspects, this book provides a definition of all the fundamental notions associated with risks and risk management, as well as clearly placing the discipline of risk analysis within the broader context of risk management processes. The author begins by presenting a certain number of basic concepts, followed by the general principle of risk analysis. He then moves on to examine the ISO31000 standard, which provides a specification for the implementation of a risk management approach. The ability to represent the information we use is crucial, so the representation of knowledge, covering both information concerning the risk occurrence mechanism and details of the system under scrutiny, is also considered. The different analysis methods are then presented, firstly for the identification of risks, then for their analysis in terms of cause and effect, and finally for the implementation of safety measures. Concrete examples are given throughout the book and the methodology and method can be applied to various fields (industry, health, organization, technical systems). Contents Part 1. General Concepts and Principles 1. Introduction. 2. Basic Notions. 3. Principles of Risk Analysis Methods. 4. The Risk Management Process (ISO31000). Part 2. Knowledge Representation 5. Modeling Risk. 6. Measuring the Importance of a Risk. 7. Modeling of Systems for Risk Analysis. Part 3. Risk Analysis Method 8. Preliminary Hazard Analysis. 9. Failure Mode and Effects Analysis. 10. Deviation Analysis Using the HAZOP Method. 11. The Systemic and Organized Risk Analysis Method. 12. Fault Tree Analysis. 13. Event Tree and Bow-Tie Diagram Analysis. 14. Human Reliability Analysis. 15. Barrier Analysis and Layer of Protection Analysis. Part 4. Appendices Appendix 1. Occupational Hazard Checklists. Appendix 2. Causal Tree Analysis. Appendix 3. A Few Reminders on the Theory of Probability. Appendix 4. Useful Notions in Reliability Theory. Appendix 5. Data Sources for Reliability. Appendix 6. A Few Approaches for System Modelling. Appendix 7. CaseStudy: Chemical Process. Appendix 8. XRisk Software. About the Authors Jean-Marie Flaus is Professor at Joseph Fourier University in Grenoble, France.
Spatial and Spatio-Temporal Bayesian Models with R-INLA provides a much needed, practically oriented & innovative presentation of the combination of Bayesian methodology and spatial statistics. The authors combine an introduction to Bayesian theory and methodology with a focus on the spatial and spatio-temporal models used within the Bayesian framework and a series of practical examples which allow the reader to link the statistical theory presented to real data problems. The numerous examples from the fields of epidemiology, biostatistics and social science all are coded in the R package R-INLA, which has proven to be a valid alternative to the commonly used Markov Chain Monte Carlo simulations
The study contributes in analytical description of spatial diffusion of fertility, in particular, influenced by labour movements of people between places of residence and work. It is assumed that the labour market has externality on the marriage market due to commuting, which, in turn, affects fertility. A model of spatial diffusion of fertility is based on assumption of global and local spillover effects. The global spillover effect, as shifts in fertility norms, is motivated by increasing variance of social interactions of an individual, when places of work and residence are different. One local spillover effect is in response to flows of earnings across space. Another mechanism is related to expected changes in probabilities to find a partner affected by differences in day and night population. The analytical model, in which the effects on fertility of the cited spillovers are decomposed, is constructed in the paper on the base of a model of the demand for children, spatial stock-flow model of a market, and a matching model with a sex imbalance or spatial mismatch as the probability of matching. Three sex imbalances, namely of night-, day-time population and an adjusted to sex imbalance of commuters to residents are empirically tested. Empirical evidence on municipal Swedish data for the period 1994–2008 does not provide any strong evidence of spatial diffusion of fertility. However, there are externalities of labour mobility on fertility due to changes of gender structure of population.