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Sparse Optimization Theory and Methods
by Yun-Bin ZhaoSeeking sparse solutions of underdetermined linear systems is required in many areas of engineering and science such as signal and image processing. The efficient sparse representation becomes central in various big or high-dimensional data processing, yielding fruitful theoretical and realistic results in these fields. The mathematical optimization plays a fundamentally important role in the development of these results and acts as the mainstream numerical algorithms for the sparsity-seeking problems arising from big-data processing, compressed sensing, statistical learning, computer vision, and so on. This has attracted the interest of many researchers at the interface of engineering, mathematics and computer science. Sparse Optimization Theory and Methods presents the state of the art in theory and algorithms for signal recovery under the sparsity assumption. The up-to-date uniqueness conditions for the sparsest solution of underdertemined linear systems are described. The results for sparse signal recovery under the matrix property called range space property (RSP) are introduced, which is a deep and mild condition for the sparse signal to be recovered by convex optimization methods. This framework is generalized to 1-bit compressed sensing, leading to a novel sign recovery theory in this area. Two efficient sparsity-seeking algorithms, reweighted l1-minimization in primal space and the algorithm based on complementary slackness property, are presented. The theoretical efficiency of these algorithms is rigorously analysed in this book. Under the RSP assumption, the author also provides a novel and unified stability analysis for several popular optimization methods for sparse signal recovery, including l1-mininization, Dantzig selector and LASSO. This book incorporates recent development and the author’s latest research in the field that have not appeared in other books.
Spatial Analysis
by Mark R. T. Dale Marie-Josée FortinNowadays, ecologists worldwide recognize the use of spatial analysis as essential. However, because of the fast-growing range of methods available, even an expert might occasionally find it challenging to choose the most appropriate one. Providing the ecological and statistical foundations needed to make the right decision, this second edition builds and expands upon the previous one by: - Encompassing the basic methods for spatial analysis, for both complete census and sample data - Investigating updated treatments of spatial autocorrelation and spatio-temporal analysis - Introducing detailed explanations of currently developing approaches, including spatial and spatio-temporal graph theory, scan statistics, fibre process analysis, and Hierarchical Bayesian analysis - Offering practical advice for specific circumstances, such as how to analyze forest Permanent Sample Plot data and how to proceed with transect data when portions of the data series are missing. Written for graduates, researchers and professionals, this book will be a valuable source of reference for years to come.
Spatial Analysis
by Marie-Josée Fortin Mark R. T. DaleThe spatial and temporal dimensions of ecological phenomena have always been inherent in the conceptual framework of ecology, but only recently have they been incorporated explicitly into ecological theory, sampling design, experimental design and models. Statistical techniques for spatial analysis of ecological data are burgeoning and many ecologists are unfamiliar with what is available and how the techniques should be used correctly. This book gives an overview of the wide range of spatial statistics available to analyse ecological data, and provides advice and guidance for graduate students and practising researchers who are either about to embark on spatial analysis in ecological studies or who have started but are unsure how to proceed. Only a basic understanding of statistics is assumed and many schematic illustrations are given to complement or replace mathematical technicalities, making the book accessible to ecologists wishing to enter this important and fast-growing field for the first time.
Spatial Analysis Along Networks
by Kokichi Sugihara Atsuyuki OkabeIn 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.
Spatial Analysis and Location Modeling in Urban and Regional Systems
by Jean-Claude ThillThe contributed volume collects cutting-edge research in Geographic Information Science & Technologies, Location Modeling, and Spatial Analysis of Urban and Regional Systems. The contributions emphasize methodological innovations or substantive breakthroughs on many facets of the socio-economic and environmental reality of urban and regional contexts.
Spatial Analysis for the Social Sciences
by David DarmofalMany theories in the social sciences predict spatial dependence or the similarity of behaviors at neighboring locations. Spatial Analysis for the Social Sciences demonstrates how researchers can diagnose and model this spatial dependence and draw more valid inferences as a result. The book is structured around the well-known Galton's problem and presents a step-by-step guide to the application of spatial analysis. The book examines a variety of spatial diagnostics and models through a series of applied examples drawn from the social sciences. These include spatial lag models that capture behavioral diffusion between actors, spatial error models that account for spatial dependence in errors, and models that incorporate spatial heterogeneity in the effects of covariates. Spatial Analysis for the Social Sciences also examines advanced spatial models for time-series cross-sectional data, categorical and limited dependent variables, count data, and survival data.
Spatial Analysis in Geology Using R (Chapman & Hall/CRC The R Series)
by Pedro M. NogueiraThe integration of geology with data science disciplines, such as spatial statistics, remote sensing, and geographic information systems (GIS), has given rise to a shift in many natural sciences schools, pushing the boundaries of knowledge and enabling new discoveries in geological processes and earth systems. Spatial analysis of geological data can be used to identify patterns and trends in data, to map spatial relationships, and to model spatial processes. R is a consolidated and yet growing statistical programming language with increasing value in spatial analysis often replacing, with advantage, GIS tools. By providing a comprehensive guide for geologists to harness the power of spatial analysis in R, Spatial Analysis in Geology Using R serves as a tool in addressing real-world problems, such as natural resource management, environmental conservation, and hazard prediction and mitigation.Features: Provides a practical and accessible overview of spatial analysis in geology using R Organised in three independent and complementary parts: Introduction to R, Spatial Analysis with R, and Spatial Statistics and Modelling Applied approach with many detailed examples and case studies using real geological data Presents a collection of R packages that are useful in many geological situations Does not assume any prior knowledge of R; all code are explained in detail Supplemented by a website with all data, code, and examples Spatial Analysis in Geology Using R will be useful to any geological researcher who has acquired basic spatial analysis skills, often using GIS, and is interested in deepening those skills through the use of R. It could be used as a reference by applied researchers and analysts in public, private, or third-sector industries. It could also be used to teach a course on the topic to graduate students or for self-study.
Spatial Analysis Methods of Road Traffic Collisions
by Becky P. Loo Tessa Kate AndersonExamine the Prevalence and Geography of Road CollisionsSpatial Analysis Methods of Road Traffic Collisions centers on the geographical nature of road crashes, and uses spatial methods to provide a greater understanding of the patterns and processes that cause them. Written by internationally known experts in the field of transport geography, the bo
Spatial Analysis with R: Statistics, Visualization, and Computational Methods
by Tonny J. OyanaIn the five years since the publication of the first edition of Spatial Analysis: Statistics, Visualization, and Computational Methods, many new developments have taken shape regarding the implementation of new tools and methods for spatial analysis with R. The use and growth of artificial intelligence, machine learning and deep learning algorithms with a spatial perspective, and the interdisciplinary use of spatial analysis are all covered in this second edition along with traditional statistical methods and algorithms to provide a concept-based problem-solving learning approach to mastering practical spatial analysis. Spatial Analysis with R: Statistics, Visualization, and Computational Methods, Second Edition provides a balance between concepts and practicums of spatial statistics with a comprehensive coverage of the most important approaches to understand spatial data, analyze spatial relationships and patterns, and predict spatial processes. New in the Second Edition: Includes new practical exercises and worked-out examples using R Presents a wide range of hands-on spatial analysis worktables and lab exercises All chapters are revised and include new illustrations of different concepts using data from environmental and social sciences Expanded material on spatiotemporal methods, visual analytics methods, data science, and computational methods Explains big data, data management, and data mining This second edition of an established textbook, with new datasets, insights, excellent illustrations, and numerous examples with R, is perfect for senior undergraduate and first-year graduate students in geography and the geosciences.
Spatial and Social Disparities
by Claudia Thomas John Stillwell Paul Norman Paula SurridgeInequality is one of the major problems of the contemporary world. Significant geographical disparities exist within nations of the developed world, as well as between these countries and those referred to as the 'South' in the Bruntland Report. Issues of equity and deprivation must be addressed in view of sustainable development. However, before policymakers can remove the obstacles to a fairer world, it is essential to understand the nature of inequality, both in terms of its spatial and socio-demographic characteristics. This second volume in the series contains population studies that examine the disparities evident across geographical space in the UK and between different individuals or groups. Topics include demographic and social change, deprivation, happiness, cultural consumption, ethnicity, gender, employment, health, religion, education and social values. These topics and the relationships between them are explored using secondary data from censuses, surveys or administrative records. In volume 1 the findings of research on fertility, living arrangements, care and mobility are examined. Volume 3 will focus on ethnicity and integration.
Spatial and Spatio-temporal Bayesian Models with R - INLA
by Marta Blangiardo Michela CamelettiSpatial 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
Spatial and Spatio-Temporal Geostatistical Modeling and Kriging (Wiley Series in Probability and Statistics #998)
by José-María Montero Gema Fernández-Avilés Jorge MateuStatistical Methods for Spatial and Spatio-Temporal Data Analysis provides a complete range of spatio-temporal covariance functions and discusses ways of constructing them. This book is a unified approach to modeling spatial and spatio-temporal data together with significant developments in statistical methodology with applications in R. This book includes: Methods for selecting valid covariance functions from the empirical counterparts that overcome the existing limitations of the traditional methods. The most innovative developments in the different steps of the kriging process. An up-to-date account of strategies for dealing with data evolving in space and time. An accompanying website featuring R code and examples
Spatial Behavior in Haredi Jewish Communities in Great Britain (The Urban Book Series)
by Shlomit Flint AsheryThis book focuses on the strict orthodox Jewish (Haredi) community, which comprises many sects whose communal identity plays a central role in everyday life and spatial organization. The research reveals and analyses powerful mechanisms of residential segregation acting at the apartment-, building- and near-neighbourhood levels. Identifying the main engines of spontaneous and organised neighbourhood change and evaluating the difficulties of liberalism dealing with non-autonomous individuals in the housing market sheds light on similar processes occurring in other city centres with diverse population groups. Highlighting the impact of various organisational levels on the spatial structure of the urban enclave, the book focuses on the internal dynamics of ethno-religious enclaves that emerge from three levels of action: (1) individuals' relationships with their own and other groups; (2) the community leadership's powers within the group and in respect of other groups; and (3) government directives and tools (e.g planning). The study examines how different levels of communal organisation are reflected in the residential patterns of four British communities: the Litvish communities of Golders Green and Gateshead, and the Hassidic communities of Stamford Hill and Canvey Island.
Spatial Complexity: Theory, Mathematical Methods and Applications
by Fivos PapadimitriouThis book delivers stimulating input for a broad range of researchers, from geographers and ecologists to psychologists interested in spatial perception and physicists researching in complex systems. How can one decide whether one surface or spatial object is more complex than another? What does it require to measure the spatial complexity of small maps, and why does this matter for nature, science and technology? Drawing from algorithmics, geometry, topology, probability and informatics, and with examples from everyday life, the reader is invited to cross the borders into the bewildering realm of spatial complexity, as it emerges from the study of geographic maps, landscapes, surfaces, knots, 3D and 4D objects. The mathematical and cartographic experiments described in this book lead to hypotheses and enigmas with ramifications in aesthetics and epistemology.
Spatial Context: An Introduction to Fundamental Computer Algorithms for Spatial Analysis (ISPRS Book Series #12)
by Christopher GoldMany disciplines are concerned with manipulating geometric (or spatial) objects in the computer – such as geology, cartography, computer aided design (CAD), etc. – and each of these have developed their own data structures and techniques, often independently. Nevertheless, in many cases the object types and the spatial queries are similar, and this book attempts to find a common theme.
Spatial Data Analysis: Models, Methods and Techniques
by Jinfeng Wang Manfred M. FischerThe availability of spatial databases and widespread use of geographic information systems has stimulated increasing interest in the analysis and modelling of spatial data. Spatial data analysis focuses on detecting patterns, and on exploring and modelling relationships between them in order to understand the processes responsible for their emergence. In this way, the role of space is emphasised , and our understanding of the working and representation of space, spatial patterns, and processes is enhanced. In applied research, the recognition of the spatial dimension often yields different and more meaningful results and helps to avoid erroneous conclusions. This book aims to provide an introduction into spatial data analysis to graduates interested in applied statistical research. The text has been structured from a data-driven rather than a theory-based perspective, and focuses on those models, methods and techniques which are both accessible and of practical use for graduate students. Exploratory techniques as well as more formal model-based approaches are presented, and both area data and origin-destination flow data are considered.
Spatial Data Science: With Applications in R (Chapman & Hall/CRC The R Series)
by Edzer Pebesma Roger BivandSpatial Data Science introduces fundamental aspects of spatial data that every data scientist should know before they start working with spatial data. These aspects include how geometries are represented, coordinate reference systems (projections, datums), the fact that the Earth is round and its consequences for analysis, and how attributes of geometries can relate to geometries. In the second part of the book, these concepts are illustrated with data science examples using the R language. In the third part, statistical modelling approaches are demonstrated using real world data examples. After reading this book, the reader will be well equipped to avoid a number of major spatial data analysis errors. The book gives a detailed explanation of the core spatial software packages for R: sf for simple feature access, and stars for raster and vector data cubes – array data with spatial and temporal dimensions. It also shows how geometrical operations change when going from a flat space to the surface of a sphere, which is what sf and stars use when coordinates are not projected (degrees longitude/latitude). Separate chapters detail a variety of plotting approaches for spatial maps using R, and different ways of handling very large vector or raster (imagery) datasets, locally, in databases, or in the cloud. The data used and all code examples are freely available online from https://r-spatial.org/book/. The solutions to the exercises can be found here: https://edzer.github.io/sdsr_exercises/.
Spatial Dynamics and Pattern Formation in Biological Populations
by Ranjit Kumar Upadhyay Satteluri R. IyengarThe book provides an introduction to deterministic (and some stochastic) modeling of spatiotemporal phenomena in ecology, epidemiology, and neural systems. A survey of the classical models in the fields with up to date applications is given. The book begins with detailed description of how spatial dynamics/diffusive processes influence the dynamics of biological populations. These processes play a key role in understanding the outbreak and spread of pandemics which help us in designing the control strategies from the public health perspective. A brief discussion on the functional mechanism of the brain (single neuron models and network level) with classical models of neuronal dynamics in space and time is given. Relevant phenomena and existing modeling approaches in ecology, epidemiology and neuroscience are introduced, which provide examples of pattern formation in these models. The analysis of patterns enables us to study the dynamics of macroscopic and microscopic behaviour of underlying systems and travelling wave type patterns observed in dispersive systems. Moving on to virus dynamics, authors present a detailed analysis of different types models of infectious diseases including two models for influenza, five models for Ebola virus and seven models for Zika virus with diffusion and time delay. A Chapter is devoted for the study of Brain Dynamics (Neural systems in space and time). Significant advances made in modeling the reaction-diffusion systems are presented and spatiotemporal patterning in the systems is reviewed. Development of appropriate mathematical models and detailed analysis (such as linear stability, weakly nonlinear analysis, bifurcation analysis, control theory, numerical simulation) are presented. Key Features Covers the fundamental concepts and mathematical skills required to analyse reaction-diffusion models for biological populations. Concepts are introduced in such a way that readers with a basic knowledge of differential equations and numerical methods can understand the analysis. The results are also illustrated with figures. Focuses on mathematical modeling and numerical simulations using basic conceptual and classic models of population dynamics, Virus and Brain dynamics. Covers wide range of models using spatial and non-spatial approaches. Covers single, two and multispecies reaction-diffusion models from ecology and models from bio-chemistry. Models are analysed for stability of equilibrium points, Turing instability, Hopf bifurcation and pattern formations. Uses Mathematica for problem solving and MATLAB for pattern formations. Contains solved Examples and Problems in Exercises. The Book is suitable for advanced undergraduate, graduate and research students. For those who are working in the above areas, it provides information from most of the recent works. The text presents all the fundamental concepts and mathematical skills needed to build models and perform analyses.
Spatial Econometric Interaction Modelling
by Roberto Patuelli Giuseppe ArbiaThis contributed volume applies spatial and space-time econometric methods to spatial interaction modeling. The first part of the book addresses general cutting-edge methodological questions in spatial econometric interaction modeling, which concern aspects such as coefficient interpretation, constrained estimation, and scale effects. The second part deals with technical solutions to particular estimation issues, such as intraregional flows, Bayesian PPML and VAR estimation. The final part presents a number of empirical applications, ranging from interregional tourism competition and domestic trade to space-time migration modeling and residential relocation.
Spatial Econometrics
by J. Paul ElhorstThis book provides an overview of three generations of spatial econometric models: models based on cross-sectional data, static models based on spatial panels and dynamic spatial panel data models. The book not only presents different model specifications and their corresponding estimators, but also critically discusses the purposes for which these models can be used and how their results should be interpreted.
Spatial Economics Volume I: Theory
by Stefano ColomboSpace is a crucial variable in any economic activity. Spatial Economics is the branch of economics that explicitly aims to incorporate the space dimension in the analysis of economic phenomena. From its beginning in the last century, Spatial Economics has contributed to the understanding of the economy by developing plenty of theoretical models as well as econometric techniques having the “space” as a core dimension of the analysis. This edited volume addresses the complex issue of Spatial Economics from a theoretical point of view. This volume is part of a more complex project including another edited volume (Spatial Economics Volume II: Applications) collecting original papers which address Spatial Economics from an applied perspective.
Spatial Economics Volume II: Applications
by Stefano ColomboSpace is a crucial variable in any economic activity. Spatial Economics is the branch of economics that explicitly aims to incorporate the space dimension in the analysis of economic phenomena. From its beginning in the last century, Spatial Economics has contributed to the understanding of the economy by developing plenty of theoretical models as well as econometric techniques having the “space” as a core dimension of the analysis.This edited volume addresses the complex issue of Spatial Economics from an applied point of view. This volume is part of a more complex project including another edited volume (Spatial Economics Volume I: Theory) collecting original papers which address Spatial Economics from a theoretical perspective.
Spatial Encounters and Togetherness in the Metropolis: The Metrobuses of Istanbul (Identities and Modernities in Europe)
by Özlem CihanThis book analyzes Istanbul's bus rapid transit, the metrobus, as an encountering space to unfold the perception and practice of togetherness. Based on field research with regular metrobus passengers, the book presents a layered analysis between everyday life, everyday mobility, and togetherness to emphasize the metropolitan impact on the socio-spatial experience and subjectification. By articulating Lefebvrian social space in a metropolitan context, the book discusses that Istanbul's spatially and temporally framed everydayness leads inhabitants to the need for bus rapid transit. On the other hand, the need for the metrobus produces transit modes of experience in regulars' socio-spatial relation and subjectification. As a result, encountering and being with the unfamiliar and diverse others undertake the framed typologies of the first two layers and produce a dissolving essence in the idea and practice of togetherness in Istanbul.
Spatial Fleming-Viot Models with Selection and Mutation
by Donald A. Dawson Andreas GrevenThis book constructs a rigorous framework for analysing selected phenomena in evolutionary theory of populations arising due to the combined effects of migration, selection and mutation in a spatial stochastic population model, namely the evolution towards fitter and fitter types through punctuated equilibria. The discussion is based on a number of new methods, in particular multiple scale analysis, nonlinear Markov processes and their entrance laws, atomic measure-valued evolutions and new forms of duality (for state-dependent mutation and multitype selection) which are used to prove ergodic theorems in this context and are applicable for many other questions and renormalization analysis for a variety of phenomena (stasis, punctuated equilibrium, failure of naive branching approximations, biodiversity) which occur due to the combination of rare mutation, mutation, resampling, migration and selection and make it necessary to mathematically bridge the gap (in the limit) between time and space scales.
Spatial Inequalities
by Allan G. Hill Justin Stoler John R. WeeksThis book provides a fresh analysis of the demography, health and well-being of a major African city. It brings a range of disciplinary approaches to bear on the pressing topics of urban poverty, urban health inequalities and urban growth. The approach is primarily spatial and includes the integration of environmental information from satellites and other geospatial sources with social science and health survey data. The authors Ghanaians and outsiders, have worked to understand the urban dynamics in this burgeoning West African metropolis, with an emphasis on urban disparities in health and living standards. Few cities in the global South have been examined from so many different perspectives. Our analysis employs a wide range of GIScience methods, including analysis of remotely sensed imagery and spatial statistical analysis, applied to a wide range of data, including census, survey and health clinic data, all of which are supplemented by field work, including systematic social observation, focus groups, and key informant interviews. This book aims to explain and highlight the mix of methods, and the important findings that have been emerging from this research, with the goal of providing guidance and inspiration for others doing similar work in cities of other developing nations.