Hierarchical Modeling and Analysis for Spatial Data (Chapman & Hall/CRC Monographs on Statistics & Applied Probability). Questo articolo non è disponibile.Hierarchical Modeling and Analysis for Spatial Data (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)

Hierarchical Modeling and Analysis for Spatial Data, Third Edition is the 3rd edition of this popular and authoritative text on Bayesian modeling and inference for spatial and spatial-temporal data. The text presents a comprehensive presents a comprehensive and up-to-date treatment of hierarchical and multilevel modeling for spatial and spatio-temporal data within a Bayesian framework. Over the past decade since the second edition, spatial statistics has evolved significantly, driven by an explosion in data availability and advances in Bayesian computation. This edition reflects those changes, introducing new methods, expanded applications, and enhanced computational resources to support researchers and practitioners across disciplines, including environmental science, ecology, and public health.

Key features of the third edition:

• A dedicated chapter on state-of-the-art Bayesian modeling of large spatial and spatio-temporal datasets
• Two new chapters on spatial point pattern analysis, covering both foundational and Bayesian perspectives
• A new chapter on spatial data fusion, integrating diverse spatial data sources from different probabilistic mechanisms
• An accessible introduction to GPS mapping, geodesic distances, and mathematical cartography
• An expanded special topics chapter, including spatial challenges with finite population modeling and spatial directional data
• A thoroughly revised chapter on Bayesian inference, featuring an updated review of modern computational techniques
• A dedicated GitHub repository providing R programs and solutions to selected exercises, ensuring continued access to evolving software developments

With refreshed content throughout, this edition serves as an essential reference for statisticians, data scientists, and researchers working with spatial data. Graduate students and professionals seeking a deep understanding of Bayesian spatial modeling will find this volume an invaluable resource for both theory and practice.

OVERVIEW OF SPATIAL DATA PROBLEMS
Introduction to Spatial Data and Models
Fundamentals of Cartography
Exercises
BASICS OF POINT-REFERENCED DATA MODELS
Elements of Point-Referenced Modeling
Spatial Process Models
Exploratory Approaches for Point-Referenced Data
Classical Spatial Prediction
Computer Tutorials
Exercises
BASICS OF AREAL DATA MODELS
Exploratory Approaches for Areal Data
Brook's Lemma and Markov Random Fields
Conditionally Autoregressive (CAR) Models
Simultaneous Autoregressive (SAR) Models
Computer Tutorials
Exercises
BASICS OF BAYESIAN INFERENCE
Introduction to Hierarchical Modeling and Bayes Theorem
Bayesian Inference
Bayesian Computation
Computer Tutorials
Exercises
HIERARCHICAL MODELING FOR UNIVARIATE SPATIAL DATA
Stationary Spatial Process Models
Generalized Linear Spatial Process Modeling
Nonstationary Spatial Process Models
Areal Data Models
General Linear Areal Data Modeling
Exercises
SPATIAL MISALIGNMENT
Point-Level Modeling
Nested Block-Level Modeling
Nonnested Block-Level Modeling
Misaligned Regression Modeling
Exercises
MULTIVARIATE SPATIAL MODELING
Separable Models
Coregionalization Models
Other Constructive Approaches
Multivariate Models for Areal Data
Exercises
SPATIOTEMPORAL MODELING
General Modeling Formulation
Point-Level Modeling with Continuous Time
Nonseparable Spatio-Temporal Models
Dynamic Spatio-Temporal Models
Block-Level Modeling
Exercises
SPATIAL SURVIVAL MODELS
Parametric Models
Semiparametric Models
Spatio-Temporal Models
Multivariate Models
Spatial Cure Rate Models
Exercises
SPECIAL TOPICS IN SPATIAL PROCESS MODELING
Process Smoothness Revisited
Spatially Varying Coefficient Models
Spatial CDFs
APPENDICES
Matrix Theory and Spatial Computing Methods
Answers to Selected Exercises
REFERENCES
AUTHOR INDEX
SUBJECT INDEX

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