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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.
Spatial Interaction Models
by Panos M. Pardalos Lina Mallozzi Egidio D’amatoFacility location theory develops the idea of locating one or more facilities by optimizing suitable criteria such as minimizing transportation cost, or capturing the largest market share. The contributions in this book focus an approach to facility location theory through game theoretical tools highlighting situations where a location decision is faced by several decision makers and leading to a game theoretical framework in non-cooperative and cooperative methods. Models and methods regarding the facility location via game theory are explored and applications are illustrated through economics, engineering, and physics. Mathematicians, engineers, economists and computer scientists working in theory, applications and computational aspects of facility location problems using game theory will find this book useful.
Spatial Linear Models for Environmental Data (Chapman & Hall/CRC Applied Environmental Statistics)
by Dale L. Zimmerman Jay M. Ver HoefMany applied researchers equate spatial statistics with prediction or mapping, but this book naturally extends linear models, which includes regression and ANOVA as pillars of applied statistics, to achieve a more comprehensive treatment of the analysis of spatially autocorrelated data. Spatial Linear Models for Environmental Data, aimed at students and professionals with a master’s level training in statistics, presents a unique, applied, and thorough treatment of spatial linear models within a statistics framework. Two subfields, one called geostatistics and the other called areal or lattice models, are extensively covered. Zimmerman and Ver Hoef present topics clearly, using many examples and simulation studies to illustrate ideas. By mimicking their examples and R code, readers will be able to fit spatial linear models to their data and draw proper scientific conclusions. Topics covered include: Exploratory methods for spatial data including outlier detection, (semi)variograms, Moran’s I, and Geary’s c. Ordinary and generalized least squares regression methods and their application to spatial data. Suitable parametric models for the mean and covariance structure of geostatistical and areal data. Model-fitting, including inference methods for explanatory variables and likelihood-based methods for covariance parameters. Practical use of spatial linear models including prediction (kriging), spatial sampling, and spatial design of experiments for solving real world problems. All concepts are introduced in a natural order and illustrated throughout the book using four datasets. All analyses, tables, and figures are completely reproducible using open-source R code provided at a GitHub site. Exercises are given at the end of each chapter, with full solutions provided on an instructor’s FTP site supplied by the publisher.
Spatial Mathematics: Theory and Practice through Mapping
by Joseph J. Kerski Sandra Lach ArlinghausIn terms of statistics, GIS offers many connections. With GIS, data are gathered, displayed, summarized, examined, and interpreted to discover patterns. Spatial Mathematics: Theory and Practice through Mapping uses GIS as a platform to teach mathematical concepts and skills through visualization of numbers. It examines theory and practice from disp
Spatial Microsimulation with R (Chapman & Hall/CRC The R Series)
by Robin Lovelace Morgane DumontGenerate and Analyze Multi-Level Data Spatial microsimulation involves the generation, analysis, and modeling of individual-level data allocated to geographical zones. Spatial Microsimulation with R is the first practical book to illustrate this approach in a modern statistical programming language. Get Insight into Complex BehaviorsThe book progresses from the principles underlying population synthesis toward more complex issues such as household allocation and using the results of spatial microsimulation for agent-based modeling. This equips you with the skills needed to apply the techniques to real-world situations. The book demonstrates methods for population synthesis by combining individual and geographically aggregated datasets using the recent R packages ipfp and mipfp. This approach represents the "best of both worlds" in terms of spatial resolution and person-level detail, overcoming issues of data confidentiality and reproducibility. Implement the Methods on Your Own DataFull of reproducible examples using code and data, the book is suitable for students and applied researchers in health, economics, transport, geography, and other fields that require individual-level data allocated to small geographic zones. By explaining how to use tools for modeling phenomena that vary over space, the book enhances your knowledge of complex systems and empowers you to provide evidence-based policy guidance.
Spatial Microsimulation: A Reference Guide for Users
by Kimberley Edwards Robert TantonThis book is a practical guide on how to design, create and validate a spatial microsimulation model. These models are becoming more popular as academics and policy makers recognise the value of place in research and policy making. Recent spatial microsimulation models have been used to analyse health and social disadvantage for small areas; and to look at the effect of policy change for small areas. This provides a powerful analysis tool for researchers and policy makers. This book covers preparing the data for spatial microsimulation; a number of methods for both static and dynamic spatial microsimulation models; validation of the models to ensure the outputs are reasonable; and the future of spatial microsimulation. The book will be an essential handbook for any researcher or policy maker looking to design and create a spatial microsimulation model. This book will also be useful to those policy makers who are commissioning a spatial microsimulation model, or looking to commission work using a spatial microsimulation model, as it provides information on the different methods in a non-technical way.
Spatial Network Data
by Dev OliverThis brief explores two of the main challenges of spatial network data analysis: the many connected components in the spatial network and the many candidates that have to be processed. Within this book, these challenges are conceptualized, well-defined problems are explored, and critical techniques are discussed. The process of summarizing spatial network data entails finding a compact description or representation of observations or activities on large spatial or spatiotemporal networks. However, summarizing spatial network data can be computationally challenging for various reasons, depending on the domain. The content has applications for professionals, organizations, and researchers in transportation safety, public safety, public health, disaster response, and related fields.
Spatial Networking in the United Physical, Virtual, and Mental World (Studies in Systems, Decision and Control #533)
by Peter S. SapatyThe current book chooses graphs and networks as primary and global research objectives after reviewing different types and areas of networking and existing works on graph and network operations. The ideas of this book originate from the WAVE approach which allowed us, more than half a century ago, to implement citywide heterogeneous computer networks and solve distributed problems on them in flexible and mobile way. The invented management paradigm evolved into Spatial Grasp Technology resulted in European patent and nine previous books oriented on concrete applications in social and defense systems, security, crises management, collective robotics, space research, and others. Many obtained results were dealing with graph and network structures and problems which were extremely important in the researched areas. It aims at development of higher-level social infrastructures effectively integrating different types of networking under the same universal approach, also application of networking in new areas like organoids and brain research. This book is oriented toward system scientists, application programmers, industry managers, university students, philosophers, psychologists, and United Nations personnel too.
Spatial Networks: A Complete Introduction: From Graph Theory and Statistical Physics to Real-World Applications (Lecture Notes In Morphogenesis Ser.)
by Marc BarthelemyThis book provides a complete introduction into spatial networks. It offers the mathematical tools needed to characterize these structures and how they evolve in time and presents the most important models of spatial networks.The book puts a special emphasis on analyzing complex systems which are organized under the form of networks where nodes and edges are embedded in space. In these networks, space is relevant, and topology alone does not contain all the information. Characterizing and understanding the structure and the evolution of spatial networks is thus crucial for many different fields, ranging from urbanism to epidemiology.This subject is therefore at the crossroad of many fields and is of potential interest to a broad audience comprising physicists, mathematicians, engineers, geographers or urbanists. In this book, the author has expanded his previous book ("Morphogenesis of Spatial Networks") to serve as a textbook and reference on this topic for a wide range of students and professional researchers.
Spatial Point Patterns: Methodology and Applications with R (Chapman & Hall/CRC Interdisciplinary Statistics)
by Adrian Baddeley Ege Rubak Rolf TurnerModern Statistical Methodology and Software for Analyzing Spatial Point PatternsSpatial Point Patterns: Methodology and Applications with R shows scientific researchers and applied statisticians from a wide range of fields how to analyze their spatial point pattern data. Making the techniques accessible to non-mathematicians, the authors draw on th
Spatial Polarization Characteristics of Radar Antenna: Analysis, Measurement and Anti-jamming Application
by Hong Xie Jia Luo Huanyao Dai Xuesong Wang Shunping XiaoThis book presents novel research ideas and offers insights into radar system design, artificial intelligence and signal processing applications. Further, it proposes a new concept of antenna spatial polarization characteristics (SPC), suggesting that the antenna polarization is a function of the spatial direction and providing new ideas for radar signal processing (RSP) and anti-jamming. It also discusses the design of an advanced signal-processing algorithm, and proposes new polarimetric and anti-jamming methods using antenna inherent properties. The book helps readers discover the potential of radar information processing and improve its anti-interference and target identification ability. It is of interest to university researchers, radar engineers and graduate students in computer science and electronics who wish to learn the core principles, methods, algorithms, and applications of RSP.
Spatial Predictive Modelling with R
by Jin LiSpatial predictive modeling (SPM) is an emerging discipline in applied sciences, playing a key role in the generation of spatial predictions in various disciplines. SPM refers to preparing relevant data, developing optimal predictive models based on point data, and then generating spatial predictions. This book aims to systematically introduce the entire process of SPM as a discipline. The process contains data acquisition, spatial predictive methods and variable selection, parameter optimization, accuracy assessment, and the generation and visualization of spatial predictions, where spatial predictive methods are from geostatistics, modern statistics, and machine learning. The key features of this book are: •Systematically introducing major components of SPM process.•Novel hybrid methods (228 hybrids plus numerous variants) of modern statistical methods or machine learning methods with mathematical and/or univariate geostatistical methods.•Novel predictive accuracy-based variable selection techniques for spatial predictive methods.•Predictive accuracy-based parameter/model optimization.•Reproducible examples for SPM of various data types in R. This book provides guidelines, recommendations, and reproducible examples for developing optimal predictive models by considering various components and associated factors for quality-improved spatial predictions. It provides valuable tools for researchers, modelers, and university students not only in SPM field but also in other predictive modeling fields. Dr Li has produced over 100 various publications in spatial predictive modelling, statistical computing, ecological and environmental modelling, and ecology, developed a number of hybrid methods for SPM, and published four R packages for variable selections as well as SPM.
Spatial Reasoning Puzzles That Make Kids Think!: Grades 6-8
by Jeffrey J. WankoSpatial Reasoning Puzzles That Make Kids Think! engages even the most reluctant math learner. In this fun and challenging book, students must conquer four types of logical and spatial reasoning puzzles (Slitherlink, Hashiwokakero, Masyu, and Yajilin). The rules for each type of puzzle are very different, but easy to understand. The challenge is for students to apply their critical thinking skills to new situations and develop new strategies for solving each puzzle. Teacher support is provided for solving the puzzles and also for helping students to create puzzles of their own. Students will be begging for more of these unique spatial reasoning puzzles!Grades 6-8
Spatial Reasoning: A Mathematics Unit for High-Ability Learners in Grades 2-4
by Dana T. JohnsonSpark the visual learning of students in grades 2-4 with Spatial Reasoning, a mathematics unit for high-ability learners. Gifted students demonstrate an advanced aptitude for spatial reasoning at early ages, and they require more complex lessons than what the standard curriculum provides. This field-tested unit approaches spatial reasoning through one-dimensional (1-D), two-dimensional (2-D), and three-dimensional (3-D) tasks that will engage students.The lessons in this unit are differentiated for gifted learners, and they are supported by hands-on extension activities that extend spatial concepts beyond the classroom. The skills learned throughout this unit will lay the foundation of spatial reasoning that will prepare students for middle school and beyond.Spatial Reasoning was developed by the Center for Gifted Education at The College of William and Mary.Grades 2-4
Spatial Regression Models for the Social Sciences (Advanced Quantitative Techniques in the Social Sciences #14)
by Jun Zhu Guangqing ChiSpatial Regression Models for the Social Sciences shows researchers and students how to work with spatial data without the need for advanced mathematical statistics. Focusing on the methods that are commonly used by social scientists, Guangqing Chi and Jun Zhu explain what each method is and when and how to apply it by connecting it to social science research topics. Throughout the book they use the same social science example to demonstrate applications of each method and what the results can tell us.
Spatial Regression Models for the Social Sciences (Advanced Quantitative Techniques in the Social Sciences #14)
by Jun Zhu Guangqing ChiSpatial Regression Models for the Social Sciences shows researchers and students how to work with spatial data without the need for advanced mathematical statistics. Focusing on the methods that are commonly used by social scientists, Guangqing Chi and Jun Zhu explain what each method is and when and how to apply it by connecting it to social science research topics. Throughout the book they use the same social science example to demonstrate applications of each method and what the results can tell us.
Spatial Regression Models: Spatial Regression Models (Quantitative Applications in the Social Sciences #155)
by Kristian Skrede Gleditsch Michael D. WardSpatial Regression Models illustrates the use of spatial analysis in the social sciences within a regression framework and is accessible to readers with no prior background in spatial analysis. The text covers different modeling-related topics for continuous dependent variables, including mapping data on spatial units, creating data from maps, analyzing exploratory spatial data, working with regression models that have spatially dependent regressors, and estimating regression models with spatially correlated error structures. Using social science examples based on real data, the authors illustrate the concepts discussed, and show how to obtain and interpret relevant results. The examples are presented along with the relevant code to replicate all the analysis using the R package for statistical computing. Users can download both the data and computer code to work through all the examples found in the text. New to the Second Edition is a chapter on mapping as data exploration and its role in the research process, updates to all chapters based on substantive and methodological work, as well as software updates, and information on estimation of time-series, cross-sectional spatial models.
Spatial Regression Models: Spatial Regression Models (Quantitative Applications in the Social Sciences #155)
by Kristian Skrede Gleditsch Michael D. WardSpatial Regression Models illustrates the use of spatial analysis in the social sciences within a regression framework and is accessible to readers with no prior background in spatial analysis. The text covers different modeling-related topics for continuous dependent variables, including mapping data on spatial units, creating data from maps, analyzing exploratory spatial data, working with regression models that have spatially dependent regressors, and estimating regression models with spatially correlated error structures. Using social science examples based on real data, the authors illustrate the concepts discussed, and show how to obtain and interpret relevant results. The examples are presented along with the relevant code to replicate all the analysis using the R package for statistical computing. Users can download both the data and computer code to work through all the examples found in the text. New to the Second Edition is a chapter on mapping as data exploration and its role in the research process, updates to all chapters based on substantive and methodological work, as well as software updates, and information on estimation of time-series, cross-sectional spatial models.
Spatial Relationships Between Two Georeferenced Variables: With Applications in R
by Ronny Vallejos Felipe Osorio Moreno BevilacquaThis book offers essential, systematic information on the assessment of the spatial association between two processes from a statistical standpoint. Divided into eight chapters, the book begins with preliminary concepts, mainly concerning spatial statistics. The following seven chapters focus on the methodologies needed to assess the correlation between two or more processes; from theory introduced 35 years ago, to techniques that have only recently been published. Furthermore, each chapter contains a section on R computations to explore how the methodology works with real data. References and a list of exercises are included at the end of each chapter. The assessment of the correlation between two spatial processes has been tackled from several different perspectives in a variety of applications fields. In particular, the problem of testing for the existence of spatial association between two georeferenced variables is relevant for posterior modeling and inference. One evident application in this context is the quantification of the spatial correlation between two images (processes defined on a rectangular grid in a two-dimensional space). From a statistical perspective, this problem can be handled via hypothesis testing, or by using extensions of the correlation coefficient. In an image-processing framework, these extensions can also be used to define similarity indices between images.
Spatial Sampling with R (Chapman & Hall/CRC The R Series)
by Dick J. BrusScientific research often starts with data collection. However, many researchers pay insufficient attention to this first step in their research. The author, researcher at Wageningen University and Research, often had to conclude that the data collected by fellow researchers were suboptimal, or in some cases even unsuitable for their aim. One reason is that sampling is frequently overlooked in statistics courses. Another reason is the lack of practical textbooks on sampling. Numerous books have been published on the statistical analysis and modelling of data using R, but to date no book has been published in this series on how these data can best be collected. This book fills this gap. Spatial Sampling with R presents an overview of sampling designs for spatial sample survey and monitoring. It shows how to implement the sampling designs and how to estimate (sub)population- and space-time parameters in R. Key features Describes classical, basic sampling designs for spatial survey, as well as recently developed, advanced sampling designs and estimators Presents probability sampling designs for estimating parameters for a (sub)population, as well as non-probability sampling designs for mapping Gives comprehensive overview of model-assisted estimators Covers Bayesian approach to sampling design Illustrates sampling designs with surveys of soil organic carbon, above-ground biomass, air temperature, opium poppy Explains integration of wall-to-wall data sets (e.g. remote sensing images) and sample data Data and R code available on github Exercises added making the book suitable as a textbook for students The target group of this book are researchers and practitioners of sample surveys, as well as students in environmental, ecological, agricultural science or any other science in which knowledge about a population of interest is collected through spatial sampling. This book helps to implement proper sampling designs, tailored to their problems at hand, so that valuable data are collected that can be used to answer the research questions.
Spatial Socio-econometric Modeling: A Low-Code Toolkit for Spatial Data Science and Interactive Visualizations Using R (Springer Texts in Social Sciences)
by Manuel S. González CanchéWith the primary goal of expanding access to spatial data science tools, this book offers dozens of minimal or low-code functions and tutorials designed to ease the implementation of fully reproducible Spatial Socio-Econometric Modeling (SSEM) analyses. Designed as a University of Pennsylvania Ph.D. level course for sociologists, political scientists, urban planners, criminologists, and data scientists, this textbook equips social scientists with all concepts, explanations, and functions required to strengthen their data storytelling. It specifically provides social scientists with a comprehensive set of open-access minimal code tools to:•Identify and access place-based longitudinal and cross-sectional data sources and formats•Conduct advanced data management, including crosswalks, joining, and matching•Fully connect social network analyses with geospatial statistics•Formulate research questions designed to account for place-based factors in model specification and assess their relevance compared to individual- or unit-level indicators•Estimate distance measures across units that follow road network paths •Create sophisticated and interactive HTML data visualizations cross-sectionally or longitudinally, to strengthen research storytelling capabilities•Follow best practices for presenting spatial analyses, findings, and implications•Master theories on neighborhood effects, equality of opportunity, and geography of (dis)advantage that undergird SSEM applications and methods•Assess multicollinearity issues via machine learning that may affect coefficients' estimates and guide the identification of relevant predictors•Strategize how to address feedback loops by using SSEM as an identification framework that can be merged with standard quasi-experimental techniques like propensity score models, instrumental variables, and difference in differences•Expand the SSEM analyses to connections that emerge via social interactions, such as co-authorship and advice networks, or any form of relational dataThe applied nature of the book along with the cost-free, multi-operative R software makes the usability and applicability of this textbook worldwide.
Spatial Statistical Methods for Geography
by Peter A. RogersonThis accessible new textbook offers a straightforward introduction to doing spatial statistics. Grounded in real world examples, it shows you how to extend traditional statistical methods for use with spatial data. The book assumes basic mathematical and statistics knowledge but also provides a handy refresher guide, so that you can develop your understanding and progress confidently. It also: · Equips you with the tools to both interpret and apply spatial statistical methods · Engages with the unique considerations that apply when working with geographic data · Helps you build your knowledge of key spatial statistical techniques, such as methods of geographic cluster detection.
Spatial Statistical Methods for Geography
by Peter A. RogersonThis accessible new textbook offers a straightforward introduction to doing spatial statistics. Grounded in real world examples, it shows you how to extend traditional statistical methods for use with spatial data. The book assumes basic mathematical and statistics knowledge but also provides a handy refresher guide, so that you can develop your understanding and progress confidently. It also: · Equips you with the tools to both interpret and apply spatial statistical methods · Engages with the unique considerations that apply when working with geographic data · Helps you build your knowledge of key spatial statistical techniques, such as methods of geographic cluster detection.
Spatial Statistics and Spatio-Temporal Data
by Michael ShermanIn 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.
Spatial Statistics for Data Science: Theory and Practice with R (Chapman & Hall/CRC Data Science Series)
by Paula MoragaSpatial data is crucial to improve decision-making in a wide range of fields including environment, health, ecology, urban planning, economy, and society. Spatial Statistics for Data Science: Theory and Practice with R describes statistical methods, modeling approaches, and visualization techniques to analyze spatial data using R. The book provides a comprehensive overview of the varying types of spatial data, and detailed explanations of the theoretical concepts of spatial statistics, alongside fully reproducible examples which demonstrate how to simulate, describe, and analyze spatial data in various applications. Combining theory and practice, the book includes real-world data science examples such as disease risk mapping, air pollution prediction, species distribution modeling, crime mapping, and real state analyses. The book utilizes publicly available data and offers clear explanations of the R code for importing, manipulating, analyzing, and visualizing data, as well as the interpretation of the results. This ensures contents are easily accessible and fully reproducible for students, researchers, and practitioners. Key Features: Describes R packages for retrieval, manipulation, and visualization of spatial data Offers a comprehensive overview of spatial statistical methods including spatial autocorrelation, clustering, spatial interpolation, model-based geostatistics, and spatial point processes Provides detailed explanations on how to fit and interpret Bayesian spatial models using the integrated nested Laplace approximation (INLA) and stochastic partial differential equation (SPDE) approaches