Hierarchical Modeling and Analysis for Spatial Data
3rd Edition
Published on 23. September 2025
699 pages
978-1-040-41525-2 (ISBN)
Description
Hierarchical Modeling and Analysis for Spatial Data, Third Edition is the latest edition of this popular and authoritative text on Bayesian modeling and inference for spatial and spatial-temporal data. The text 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.
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.
Reviews / Votes
"This new edition of a well-known and highly regarded work by Banerjee, Gelfand and Carlin cements the book's reputation as a comprehensive and authoritative account of statistical theory and methods for the analysis of spatially referenced data. It updates and expands the material in earlier editions, whilst a linked GitHub repository provides code and data for many of the methods described in the book itself. In addition to detailed coverage of the three core sub-areas of spatial data structure (point-referenced, areal and point pattern), the book includes a number of more recent methodological developments covering multivariate, spatio-temporal and other, more specialised topics. The authors work within the Bayesian inferential framework, but this need not put off non-Bayesian readers, as almost all of the material can be re-cast in a purely likelihood-based framework incorporating random effects in the model specification."~Peter Diggle, Lancaster University, UK
"This book has already become a classic, that deserves a position in the library of anybody interested in spatial statistics. The previous editions were received with plenty of enthusiasm by the scientific community and so should this edition. The book became a franchise that keeps flourishing roughly once at every decade. The authors are top top-tier researchers in the field and the amount and variety of material that they cover at each edition astonishes. If the growth from the 2nd to the 1st edition was about 20%, the growth from 3rd to 2nd editions was about 25%, to a staggering 699 pages document full of content. If this trend is retained we can hope for a 30% larger 4th edition, hopefully in less than a decade from now. If I could suggest one topic for that edition it would be exact inference for point processes. The book will probably become an encyclopedia by then. It is already a landmark and primary source of information in the area of (Bayesian) spatial statistics."
~Dani Gamerman, Emeritus Professor, Universidade Federal do Rio de Janeiro, Brazil
"I was thrilled to see this third edition of Hierarchical Modeling and Analysis for Spatial Data. The previous two editions have set the standard for a modern book on spatial statistics, and the authors have done a masterful job of updating this classic. I am often asked for my recommendation for a reference on spatial statistics, or for the best textbook to use in a graduate-level course on the subject. My first reaction is Banerjee et al.! Although I have some other favorites for certain topics, this book has the breadth of coverage and is written at the right level for those with some graduate training in statistics, with enough code to implement the methods on one's own data. The book keeps getting bigger and better! The third edition has added two excellent chapters on spatial point processes, as well as some other important material for modern spatial analysis (large datasets, extremes, spatial quantile regression, and spatial functional models, to name a few). The authors have also sensibly moved the code examples to a GitHub repository. Given the pace at which software changes in this area, it was a great decision to decouple the code from the text, allowing more frequent code updates. All-in-all, this is a masterful revision, and I definitely recommend that owners of the 2nd edition upgrade as soon as possible. I congratulate the authors on their hard work with this fantastic revision. Going forward, when asked for a recommendation of a book/text for spatial statistics, I will continue to say, without hesitation - Banerjee et al. (third edition)!"
~Chris Wikle, University of Missouri
"I have kept Hierarchical Modeling and Analysis for Spatial Data by Banerjee, Carlin, and Gelfand on my bookshelf since its earlier edition. It has been an invaluable resource for both my undergraduate and graduate teaching, as well as a reliable reference in my research whenever I need clarification on specific topics. I have even provided each of my students with a copy of this book. Compared to the second edition, the third edition introduces several new chapters, including Bayesian analysis of point pattern models and spatial data fusion. In addition, each chapter has been updated to reflect cutting-edge developments in the field, particularly advances in handling large spatial and spatio-temporal datasets. Overall, the addition of approximately 20% new material makes the third edition a more comprehensive and authoritative resource on the theory, methods, applications, and computation of spatial statistics. The inclusion of software and data examples through the accompanying GitHub repository further enhances its value, making it an excellent textbook as well as a resource for self-study."
~Bo Li, Washington University in St Louis, USA
More details
Series
Edition
3rd edition
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
Illustrations
79 Tables, black and white; 188 Line drawings, color; 188 Illustrations, color
File size
92,68 MB
ISBN-13
978-1-040-41525-2 (9781040415252)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Sudipto Banerjee
Hierarchical Modeling and Analysis for Spatial Data
Book
approx. 09/2026
CRC Press
Unfortunately, price unknown
Not yet published
Sudipto Banerjee | Alan E. Gelfand | Bradley P. Carlin
Hierarchical Modeling and Analysis for Spatial Data
Book
09/2025
3rd Edition
Chapman & Hall/CRC
€175.00
Shipment within 10-20 days
Persons
Alan E. Gelfand is The James B Duke Professor Emeritus of Statistical Science at Duke University. He also enjoys a secondary appointment as Professor of Environmental Science and Policy in the Nicholas School. Author of more than 330 papers and 6 books, Gelfand is internationally known for his contributions to applied statistics, Bayesian computation and Bayesian inference. For the past thirty years, Gelfand's primary research focus has been in the area of statistical modeling for spatial and space-time data. He has advanced methodology, using the Bayesian paradigm, to associate fully model-based inference with spatial and space-time data. His chief areas of application include spatio-temporal environmental and ecological processes.
Sudipto Banerjee is Professor of Biostatistics and Senior Associate Dean for Academic Programs in the Fielding School of Public Health at the University of California, Los Angeles (UCLA). He holds joint appointments as a Professor in the UCLA Department of Statistics and Data Science and as an Affiliate faculty in the UCLA Institute of Environment and Sustainability. Banerjee has authored over 200 research articles, 2 textbooks, 2 committee reports for the National Research Council of the National Academies, and an edited handbook on spatial epidemiology. Banerjee is well-known for his research expertise and methodological advancements in Bayesian hierarchical modeling and inference for spatial-temporal data; theoretical and computational developments for Gaussian processes; environmental processes and their impact on public health; spatial epidemiology; stochastic process models; statistical learning from physical and mechanistic systems; survey sampling and survival analysis.
Sudipto Banerjee is Professor of Biostatistics and Senior Associate Dean for Academic Programs in the Fielding School of Public Health at the University of California, Los Angeles (UCLA). He holds joint appointments as a Professor in the UCLA Department of Statistics and Data Science and as an Affiliate faculty in the UCLA Institute of Environment and Sustainability. Banerjee has authored over 200 research articles, 2 textbooks, 2 committee reports for the National Research Council of the National Academies, and an edited handbook on spatial epidemiology. Banerjee is well-known for his research expertise and methodological advancements in Bayesian hierarchical modeling and inference for spatial-temporal data; theoretical and computational developments for Gaussian processes; environmental processes and their impact on public health; spatial epidemiology; stochastic process models; statistical learning from physical and mechanistic systems; survey sampling and survival analysis.
Content
1 Overview of spatial data problems. 2 Basics of point-referenced data models. 3 Some theory for point-referenced data models. 4 Basics of areal data models. 5 Basics of Bayesian inference. 6 Hierarchical modeling for univariate spatial data. 7 Spatial misalignment. 8 Basics of Point Pattern Data Modeling. 9 Bayesian Analysis of Point Pattern Models. 10 Multivariate spatial modeling for point-referenced data. 11 Models for multivariate areal data.12 Spatiotemporal modeling.13 Modeling large spatial and spatiotemporal datasets. 14 Spatial gradients and wombling. 15 Spatial survival models. 16 Spatial data fusion (and preferential sampling). 17 Special topics in spatial process modeling.
