Advanced Spatial Modeling With Stochastic Partial Differential Equations Using R And Inla

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Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA

Author : Elias T. Krainski
Publisher : CRC Press
ISBN 13 : 0429629850
Total Pages : 284 pages
Book Rating : 4.4/5 (296 download)

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Book Synopsis Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA by : Elias T. Krainski

Download or read book Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA written by Elias T. Krainski and published by CRC Press. This book was released on 2018-12-07 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modeling spatial and spatio-temporal continuous processes is an important and challenging problem in spatial statistics. Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA describes in detail the stochastic partial differential equations (SPDE) approach for modeling continuous spatial processes with a Matérn covariance, which has been implemented using the integrated nested Laplace approximation (INLA) in the R-INLA package. Key concepts about modeling spatial processes and the SPDE approach are explained with examples using simulated data and real applications. This book has been authored by leading experts in spatial statistics, including the main developers of the INLA and SPDE methodologies and the R-INLA package. It also includes a wide range of applications: * Spatial and spatio-temporal models for continuous outcomes * Analysis of spatial and spatio-temporal point patterns * Coregionalization spatial and spatio-temporal models * Measurement error spatial models * Modeling preferential sampling * Spatial and spatio-temporal models with physical barriers * Survival analysis with spatial effects * Dynamic space-time regression * Spatial and spatio-temporal models for extremes * Hurdle models with spatial effects * Penalized Complexity priors for spatial models All the examples in the book are fully reproducible. Further information about this book, as well as the R code and datasets used, is available from the book website at http://www.r-inla.org/spde-book. The tools described in this book will be useful to researchers in many fields such as biostatistics, spatial statistics, environmental sciences, epidemiology, ecology and others. Graduate and Ph.D. students will also find this book and associated files a valuable resource to learn INLA and the SPDE approach for spatial modeling.

Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA

Author : Elias T. Krainski
Publisher : CRC Press
ISBN 13 : 0429628218
Total Pages : 224 pages
Book Rating : 4.4/5 (296 download)

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Book Synopsis Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA by : Elias T. Krainski

Download or read book Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA written by Elias T. Krainski and published by CRC Press. This book was released on 2018-12-07 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modeling spatial and spatio-temporal continuous processes is an important and challenging problem in spatial statistics. Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA describes in detail the stochastic partial differential equations (SPDE) approach for modeling continuous spatial processes with a Matérn covariance, which has been implemented using the integrated nested Laplace approximation (INLA) in the R-INLA package. Key concepts about modeling spatial processes and the SPDE approach are explained with examples using simulated data and real applications. This book has been authored by leading experts in spatial statistics, including the main developers of the INLA and SPDE methodologies and the R-INLA package. It also includes a wide range of applications: * Spatial and spatio-temporal models for continuous outcomes * Analysis of spatial and spatio-temporal point patterns * Coregionalization spatial and spatio-temporal models * Measurement error spatial models * Modeling preferential sampling * Spatial and spatio-temporal models with physical barriers * Survival analysis with spatial effects * Dynamic space-time regression * Spatial and spatio-temporal models for extremes * Hurdle models with spatial effects * Penalized Complexity priors for spatial models All the examples in the book are fully reproducible. Further information about this book, as well as the R code and datasets used, is available from the book website at http://www.r-inla.org/spde-book. The tools described in this book will be useful to researchers in many fields such as biostatistics, spatial statistics, environmental sciences, epidemiology, ecology and others. Graduate and Ph.D. students will also find this book and associated files a valuable resource to learn INLA and the SPDE approach for spatial modeling.

Bayesian inference with INLA

Author : Virgilio Gomez-Rubio
Publisher : CRC Press
ISBN 13 : 1351707205
Total Pages : 330 pages
Book Rating : 4.3/5 (517 download)

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Book Synopsis Bayesian inference with INLA by : Virgilio Gomez-Rubio

Download or read book Bayesian inference with INLA written by Virgilio Gomez-Rubio and published by CRC Press. This book was released on 2020-02-20 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: The integrated nested Laplace approximation (INLA) is a recent computational method that can fit Bayesian models in a fraction of the time required by typical Markov chain Monte Carlo (MCMC) methods. INLA focuses on marginal inference on the model parameters of latent Gaussian Markov random fields models and exploits conditional independence properties in the model for computational speed. Bayesian Inference with INLA provides a description of INLA and its associated R package for model fitting. This book describes the underlying methodology as well as how to fit a wide range of models with R. Topics covered include generalized linear mixed-effects models, multilevel models, spatial and spatio-temporal models, smoothing methods, survival analysis, imputation of missing values, and mixture models. Advanced features of the INLA package and how to extend the number of priors and latent models available in the package are discussed. All examples in the book are fully reproducible and datasets and R code are available from the book website. This book will be helpful to researchers from different areas with some background in Bayesian inference that want to apply the INLA method in their work. The examples cover topics on biostatistics, econometrics, education, environmental science, epidemiology, public health, and the social sciences.

Spatial and Spatio-temporal Bayesian Models with R - INLA

Author : Marta Blangiardo
Publisher : John Wiley & Sons
ISBN 13 : 1118326555
Total Pages : 321 pages
Book Rating : 4.1/5 (183 download)

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Book Synopsis Spatial and Spatio-temporal Bayesian Models with R - INLA by : Marta Blangiardo

Download or read book Spatial and Spatio-temporal Bayesian Models with R - INLA written by Marta Blangiardo and published by John Wiley & Sons. This book was released on 2015-06-02 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Geospatial Health Data

Author : Paula Moraga
Publisher : CRC Press
ISBN 13 : 1000732150
Total Pages : 217 pages
Book Rating : 4.0/5 (7 download)

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Book Synopsis Geospatial Health Data by : Paula Moraga

Download or read book Geospatial Health Data written by Paula Moraga and published by CRC Press. This book was released on 2019-11-26 with total page 217 pages. Available in PDF, EPUB and Kindle. Book excerpt: Geospatial health data are essential to inform public health and policy. These data can be used to quantify disease burden, understand geographic and temporal patterns, identify risk factors, and measure inequalities. Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny describes spatial and spatio-temporal statistical methods and visualization techniques to analyze georeferenced health data in R. The book covers the following topics: Manipulate and transform point, areal, and raster data, Bayesian hierarchical models for disease mapping using areal and geostatistical data, Fit and interpret spatial and spatio-temporal models with the Integrated Nested Laplace Approximations (INLA) and the Stochastic Partial Differential Equation (SPDE) approaches, Create interactive and static visualizations such as disease maps and time plots, Reproducible R Markdown reports, interactive dashboards, and Shiny web applications that facilitate the communication of insights to collaborators and policy makers. The book features fully reproducible examples of several disease and environmental applications using real-world data such as malaria in The Gambia, cancer in Scotland and USA, and air pollution in Spain. Examples in the book focus on health applications, but the approaches covered are also applicable to other fields that use georeferenced data including epidemiology, ecology, demography or criminology. The book provides clear descriptions of the R code for data importing, manipulation, modeling and visualization, as well as the interpretation of the results. This ensures contents are fully reproducible and accessible for students, researchers and practitioners.

Spatio-Temporal Statistics with R

Author : Christopher K. Wikle
Publisher : CRC Press
ISBN 13 : 0429649789
Total Pages : 380 pages
Book Rating : 4.4/5 (296 download)

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Book Synopsis Spatio-Temporal Statistics with R by : Christopher K. Wikle

Download or read book Spatio-Temporal Statistics with R written by Christopher K. Wikle and published by CRC Press. This book was released on 2019-02-18 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: The world is becoming increasingly complex, with larger quantities of data available to be analyzed. It so happens that much of these "big data" that are available are spatio-temporal in nature, meaning that they can be indexed by their spatial locations and time stamps. Spatio-Temporal Statistics with R provides an accessible introduction to statistical analysis of spatio-temporal data, with hands-on applications of the statistical methods using R Labs found at the end of each chapter. The book: Gives a step-by-step approach to analyzing spatio-temporal data, starting with visualization, then statistical modelling, with an emphasis on hierarchical statistical models and basis function expansions, and finishing with model evaluation Provides a gradual entry to the methodological aspects of spatio-temporal statistics Provides broad coverage of using R as well as "R Tips" throughout. Features detailed examples and applications in end-of-chapter Labs Features "Technical Notes" throughout to provide additional technical detail where relevant Supplemented by a website featuring the associated R package, data, reviews, errata, a discussion forum, and more The book fills a void in the literature and available software, providing a bridge for students and researchers alike who wish to learn the basics of spatio-temporal statistics. It is written in an informal style and functions as a down-to-earth introduction to the subject. Any reader familiar with calculus-based probability and statistics, and who is comfortable with basic matrix-algebra representations of statistical models, would find this book easy to follow. The goal is to give as many people as possible the tools and confidence to analyze spatio-temporal data.

Dynamic Time Series Models using R-INLA

Author : Nalini Ravishanker
Publisher : CRC Press
ISBN 13 : 1000622606
Total Pages : 297 pages
Book Rating : 4.0/5 (6 download)

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Book Synopsis Dynamic Time Series Models using R-INLA by : Nalini Ravishanker

Download or read book Dynamic Time Series Models using R-INLA written by Nalini Ravishanker and published by CRC Press. This book was released on 2022-08-10 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dynamic Time Series Models using R-INLA: An Applied Perspective is the outcome of a joint effort to systematically describe the use of R-INLA for analysing time series and showcasing the code and description by several examples. This book introduces the underpinnings of R-INLA and the tools needed for modelling different types of time series using an approximate Bayesian framework. The book is an ideal reference for statisticians and scientists who work with time series data. It provides an excellent resource for teaching a course on Bayesian analysis using state space models for time series. Key Features: Introduction and overview of R-INLA for time series analysis. Gaussian and non-Gaussian state space models for time series. State space models for time series with exogenous predictors. Hierarchical models for a potentially large set of time series. Dynamic modelling of stochastic volatility and spatio-temporal dependence.

Gaussian Markov Random Fields

Author : Havard Rue
Publisher : CRC Press
ISBN 13 : 0203492021
Total Pages : 280 pages
Book Rating : 4.2/5 (34 download)

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Book Synopsis Gaussian Markov Random Fields by : Havard Rue

Download or read book Gaussian Markov Random Fields written by Havard Rue and published by CRC Press. This book was released on 2005-02-18 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gaussian Markov Random Field (GMRF) models are most widely used in spatial statistics - a very active area of research in which few up-to-date reference works are available. This is the first book on the subject that provides a unified framework of GMRFs with particular emphasis on the computational aspects. This book includes extensive case-studie

Spatial Statistics for Data Science

Author : Paula Moraga
Publisher : CRC Press
ISBN 13 : 1003832334
Total Pages : 332 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Spatial Statistics for Data Science by : Paula Moraga

Download or read book Spatial Statistics for Data Science written by Paula Moraga and published by CRC Press. This book was released on 2023-12-08 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spatial 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

Recent Developments in Statistics and Data Science

Author : Regina Bispo
Publisher : Springer Nature
ISBN 13 : 3031127668
Total Pages : 364 pages
Book Rating : 4.0/5 (311 download)

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Book Synopsis Recent Developments in Statistics and Data Science by : Regina Bispo

Download or read book Recent Developments in Statistics and Data Science written by Regina Bispo and published by Springer Nature. This book was released on 2022-11-28 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents a collection of twenty-five peer-reviewed articles carefully selected from the contributions presented at the XXV Congress of the Portuguese Statistical Society (2021). Containing state-of-the-art developments in theoretical and applied statistics, the book will be accessible to readers with a background in mathematics and statistics, but will also be of interest to researchers from other scientific disciplines (e.g., biology, economics, medicine), who will find a broad range of relevant applications.

Statistical Modeling Using Bayesian Latent Gaussian Models

Author : Birgir Hrafnkelsson
Publisher : Springer Nature
ISBN 13 : 3031397916
Total Pages : 256 pages
Book Rating : 4.0/5 (313 download)

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Book Synopsis Statistical Modeling Using Bayesian Latent Gaussian Models by : Birgir Hrafnkelsson

Download or read book Statistical Modeling Using Bayesian Latent Gaussian Models written by Birgir Hrafnkelsson and published by Springer Nature. This book was released on 2023-12-10 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the statistical modeling of geophysical and environmental data using Bayesian latent Gaussian models. The structure of these models is described in a thorough introductory chapter, which explains how to construct prior densities for the model parameters, how to infer the parameters using Bayesian computation, and how to use the models to make predictions. The remaining six chapters focus on the application of Bayesian latent Gaussian models to real examples in glaciology, hydrology, engineering seismology, seismology, meteorology and climatology. These examples include: spatial predictions of surface mass balance; the estimation of Antarctica’s contribution to sea-level rise; the estimation of rating curves for the projection of water level to discharge; ground motion models for strong motion; spatial modeling of earthquake magnitudes; weather forecasting based on numerical model forecasts; and extreme value analysis of precipitation on a high-dimensional grid. The book is aimed at graduate students and experts in statistics, geophysics, environmental sciences, engineering, and related fields.

Stochastic Differential Equations for Science and Engineering

Author : Uffe Høgsbro Thygesen
Publisher : CRC Press
ISBN 13 : 1000884996
Total Pages : 381 pages
Book Rating : 4.0/5 (8 download)

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Book Synopsis Stochastic Differential Equations for Science and Engineering by : Uffe Høgsbro Thygesen

Download or read book Stochastic Differential Equations for Science and Engineering written by Uffe Høgsbro Thygesen and published by CRC Press. This book was released on 2023-06-15 with total page 381 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stochastic Differential Equations for Science and Engineering is aimed at students at the M.Sc. and PhD level. The book describes the mathematical construction of stochastic differential equations with a level of detail suitable to the audience, while also discussing applications to estimation, stability analysis, and control. The book includes numerous examples and challenging exercises. Computational aspects are central to the approach taken in the book, so the text is accompanied by a repository on GitHub containing a toolbox in R which implements algorithms described in the book, code that regenerates all figures, and solutions to exercises. Features: Contains numerous exercises, examples, and applications Suitable for science and engineering students at Master’s or PhD level Thorough treatment of the mathematical theory combined with an accessible treatment of motivating examples GitHub repository available at: https://github.com/Uffe-H-Thygesen/SDEbook and https://github.com/Uffe-H-Thygesen/SDEtools

Regression Modelling wih Spatial and Spatial-Temporal Data

Author : Robert P. Haining
Publisher : CRC Press
ISBN 13 : 0429529104
Total Pages : 527 pages
Book Rating : 4.4/5 (295 download)

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Book Synopsis Regression Modelling wih Spatial and Spatial-Temporal Data by : Robert P. Haining

Download or read book Regression Modelling wih Spatial and Spatial-Temporal Data written by Robert P. Haining and published by CRC Press. This book was released on 2020-01-27 with total page 527 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modelling Spatial and Spatial-Temporal Data: A Bayesian Approach is aimed at statisticians and quantitative social, economic and public health students and researchers who work with spatial and spatial-temporal data. It assumes a grounding in statistical theory up to the standard linear regression model. The book compares both hierarchical and spatial econometric modelling, providing both a reference and a teaching text with exercises in each chapter. The book provides a fully Bayesian, self-contained, treatment of the underlying statistical theory, with chapters dedicated to substantive applications. The book includes WinBUGS code and R code and all datasets are available online. Part I covers fundamental issues arising when modelling spatial and spatial-temporal data. Part II focuses on modelling cross-sectional spatial data and begins by describing exploratory methods that help guide the modelling process. There are then two theoretical chapters on Bayesian models and a chapter of applications. Two chapters follow on spatial econometric modelling, one describing different models, the other substantive applications. Part III discusses modelling spatial-temporal data, first introducing models for time series data. Exploratory methods for detecting different types of space-time interaction are presented followed by two chapters on the theory of space-time separable (without space-time interaction) and inseparable (with space-time interaction) models. An applications chapter includes: the evaluation of a policy intervention; analysing the temporal dynamics of crime hotspots; chronic disease surveillance; and testing for evidence of spatial spillovers in the spread of an infectious disease. A final chapter suggests some future directions and challenges.

The R Book

Author : Michael J. Crawley
Publisher : John Wiley & Sons
ISBN 13 : 9780470515068
Total Pages : 953 pages
Book Rating : 4.5/5 (15 download)

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Book Synopsis The R Book by : Michael J. Crawley

Download or read book The R Book written by Michael J. Crawley and published by John Wiley & Sons. This book was released on 2007-06-13 with total page 953 pages. Available in PDF, EPUB and Kindle. Book excerpt: The high-level language of R is recognized as one of the mostpowerful and flexible statistical software environments, and israpidly becoming the standard setting for quantitative analysis,statistics and graphics. R provides free access to unrivalledcoverage and cutting-edge applications, enabling the user to applynumerous statistical methods ranging from simple regression to timeseries or multivariate analysis. Building on the success of the author’s bestsellingStatistics: An Introduction using R, The R Book ispacked with worked examples, providing an all inclusive guide to R,ideal for novice and more accomplished users alike. The bookassumes no background in statistics or computing and introduces theadvantages of the R environment, detailing its applications in awide range of disciplines. Provides the first comprehensive reference manual for the Rlanguage, including practical guidance and full coverage of thegraphics facilities. Introduces all the statistical models covered by R, beginningwith simple classical tests such as chi-square and t-test. Proceeds to examine more advance methods, from regression andanalysis of variance, through to generalized linear models,generalized mixed models, time series, spatial statistics,multivariate statistics and much more. The R Book is aimed at undergraduates, postgraduates andprofessionals in science, engineering and medicine. It is alsoideal for students and professionals in statistics, economics,geography and the social sciences.

Modelling Spatial and Spatial-Temporal Data: A Bayesian Approach

Author : Robert P. Haining
Publisher : CRC Press
ISBN 13 : 1482237431
Total Pages : 641 pages
Book Rating : 4.4/5 (822 download)

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Book Synopsis Modelling Spatial and Spatial-Temporal Data: A Bayesian Approach by : Robert P. Haining

Download or read book Modelling Spatial and Spatial-Temporal Data: A Bayesian Approach written by Robert P. Haining and published by CRC Press. This book was released on 2020-01-27 with total page 641 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modelling Spatial and Spatial-Temporal Data: A Bayesian Approach is aimed at statisticians and quantitative social, economic and public health students and researchers who work with spatial and spatial-temporal data. It assumes a grounding in statistical theory up to the standard linear regression model. The book compares both hierarchical and spatial econometric modelling, providing both a reference and a teaching text with exercises in each chapter. The book provides a fully Bayesian, self-contained, treatment of the underlying statistical theory, with chapters dedicated to substantive applications. The book includes WinBUGS code and R code and all datasets are available online. Part I covers fundamental issues arising when modelling spatial and spatial-temporal data. Part II focuses on modelling cross-sectional spatial data and begins by describing exploratory methods that help guide the modelling process. There are then two theoretical chapters on Bayesian models and a chapter of applications. Two chapters follow on spatial econometric modelling, one describing different models, the other substantive applications. Part III discusses modelling spatial-temporal data, first introducing models for time series data. Exploratory methods for detecting different types of space-time interaction are presented followed by two chapters on the theory of space-time separable (without space-time interaction) and inseparable (with space-time interaction) models. An applications chapter includes: the evaluation of a policy intervention; analysing the temporal dynamics of crime hotspots; chronic disease surveillance; and testing for evidence of spatial spillovers in the spread of an infectious disease. A final chapter suggests some future directions and challenges.

Emerging Swine Infectious Diseases

Author : Tao Lin
Publisher : Frontiers Media SA
ISBN 13 : 2832542379
Total Pages : 95 pages
Book Rating : 4.8/5 (325 download)

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Book Synopsis Emerging Swine Infectious Diseases by : Tao Lin

Download or read book Emerging Swine Infectious Diseases written by Tao Lin and published by Frontiers Media SA. This book was released on 2024-01-10 with total page 95 pages. Available in PDF, EPUB and Kindle. Book excerpt: As major threats to the global swine industry, swine infectious diseases caused significant economic losses and potential public health issues. During the past three decades, many swine infectious diseases emerged in the field, such as porcine reproductive and respiratory syndrome virus (PRRSV) and its novel isolates with distinct pathogenicity, high pathogenic variants of porcine epidemic diarrhea virus (PEDV) and pseudorabies virus (PRV) and influenza viruses, which lead to tremendous economic losses worldwide. A few novel pathogens identified recently, such as Senecavirus A, atypical porcine pestivirus (APPV), PCV-3, PCV4, PDCoV, SADS-CoV, influenza D, constitute a new challenge. Although significant effects have been made to study these emerging infectious diseases, there are still gaps in many aspects, such as the pathogenesis mechanisms, structure and function of viral proteins, protective immunity, and viral evolution. For disease control, new approaches for prevention and treatment of infectious diseases are urgently required. To fill these gaps of knowledge, we encourage scientists to publish their experimental and theoretical results in the below aspects: - Genomic structure and function - Virus-host interaction - Viral and/or host dependency for virulence and transmissibility of the disease - Antiviral countermeasures - Vaccine design - Disease control programs

Theory of Spatial Statistics

Author : M.N.M. van Lieshout
Publisher : CRC Press
ISBN 13 : 0429627033
Total Pages : 162 pages
Book Rating : 4.4/5 (296 download)

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Book Synopsis Theory of Spatial Statistics by : M.N.M. van Lieshout

Download or read book Theory of Spatial Statistics written by M.N.M. van Lieshout and published by CRC Press. This book was released on 2019-03-19 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: Theory of Spatial Statistics: A Concise Introduction presents the most important models used in spatial statistics, including random fields and point processes, from a rigorous mathematical point of view and shows how to carry out statistical inference. It contains full proofs, real-life examples and theoretical exercises. Solutions to the latter are available in an appendix. Assuming maturity in probability and statistics, these concise lecture notes are self-contained and cover enough material for a semester course. They may also serve as a reference book for researchers. Features * Presents the mathematical foundations of spatial statistics. * Contains worked examples from mining, disease mapping, forestry, soil and environmental science, and criminology. * Gives pointers to the literature to facilitate further study. * Provides example code in R to encourage the student to experiment. * Offers exercises and their solutions to test and deepen understanding. The book is suitable for postgraduate and advanced undergraduate students in mathematics and statistics.