Overview of Spatial Data Problems. Basics of Point-Referenced Data Models. Basics of Areal Data Models. Basics of Bayesian Inference. Spatial Misalignment. Hierarchical Models for Point-Process Data. Hierarchical Modeling for Univariate Spatial Data. Modeling Large Spatial and Spatial-Temporal Data Sets. Multivariate Spatial Modeling. Special Topics. Appendices. References. Author Index. Subject Index.
"The second edition of Hierarchical Modeling and Analysis for
Spatial Data is a nice, rich, and excellent book, which deserves to
be read by students and researchers, especially those working in
the area of geosciences, environmental sciences, public health,
ecology, and epidemiology. This second edition builds on the
strengths of the first edition and includes significant new
chapters that make the book rather comprehensive. … About 20
different applications are presented along the text (some of them
are treated in several chapters). They nicely illustrate the use of
the methods that are exposed in the book. These applications are
based on data in ecology (…), epidemiology and public health (…),
environmental sciences (…), and economics … To conclude, the second
edition of Hierarchical Modeling and Analysis for Spatial Data
provides an excellent treatment of methods and applications in
spatial statistics. It takes into consideration 10 years of changes
(with respect to the first edition), including the changes induced
by the increasing complexity and volume of data and the increasing
complexity of questions that one aims to address with modeling and
inference approaches. In Chapter 1, the authors claim that they
aimed "to present a practical, self-contained treatment of
hierarchical modeling, and data analysis for complex spatial (and
spatiotemporal) datasets". They succeeded."
—Samuel Soubeyrand, INRA, France, in Mathematical Geosciences,
January 2017"If you want a thorough taste of the spatial statistics
field, Hierarchical Modeling and Analysis for Spatial Data is
definitely a book for you. It is accessible and comprehensive, and
it fully explores how useful spatial statistics can be without
sacrificing the theory it is grounded in. This is a great book for
graduate students and professors who want to understand the
theoretical underpinnings of the field as well as practitioners who
want a toolkit for tackling spatial problems. … the authors provide
an easy-to-use online resource with all of the book’s code and
datasets. Indeed, these examples are so comprehensive that readers
could learn a lot by simply going through them. The accessible
theoretical material paired with these detailed examples make
Hierarchical Modeling and Analysis for Spatial Data an especially
substantial and worthwhile investment. … the authors do not hold
back on their references. … Some could view this book as a spatial
statistics biography from the 1950s onward … If a graduate student
or professor wants a full taste of the spatial statistics
literature—where it has been, where it is, and where it still needs
to go—this is probably one of the best books they could pick up. …
We find this book to give a much more applied perspective with
better computational tools, and thus believe it to be more
accessible to a wider audience [than Cressie]. We recommend this
book to anyone who seriously wants to start being involved in
spatial statistics."
—Journal of the American Statistical Association, December
2015"This is a very welcome second edition of a nice and very
successful book written by three experts in the field … I have no
doubts that this updated text will continue being a compulsory
reference for those graduate students and researchers interested in
understanding and applying any of the three areas of spatial
statistics … printed in color and this helps to see better some of
the graphical representations … excellent book that I highly
recommend for anyone interested in the fascinating field of space
and space–time modeling. This is definitely one of those second
edition books that is worthwhile having. Many thanks to the authors
for their effort."
—Biometrics, March 2015 Praise for the First Edition:"This book was
a pleasure to review. Most of the emphasis is on insight and
intuition with relatively little on traditional multivariate
techniques. I also found some of the explanations delightful …
while they did not convert me to Bayesianism, [the authors] made me
reconsider some of my assumptions. They later state 'Our book is
intended as a research monograph, presenting the state of the art'
and my impression is that they have succeeded … In many sections
the formulae are augmented by showing R or S code, making it easy
to actually apply the mathematics. In summary, this is a nice
book."
—ISI Short Book Reviews "The book contains a wealth of material not
available elsewhere in a unified manner. Each chapter contains
worked out examples using some well-known software packages and has
exercises with related computer code and data on a supporting web
page. The book is up to date in its coverage … an important
addition to the literature on spatial data analysis."
—Zentralblatt MATH 1053
"The second edition of Hierarchical Modeling and Analysis for
Spatial Data is a nice, rich, and excellent book, which deserves to
be read by students and researchers, especially those working in
the area of geosciences, environmental sciences, public health,
ecology, and epidemiology. This second edition builds on the
strengths of the first edition and includes significant new
chapters that make the book rather comprehensive. … About 20
different applications are presented along the text (some of them
are treated in several chapters). They nicely illustrate the use of
the methods that are exposed in the book. These applications are
based on data in ecology (…), epidemiology and public health (…),
environmental sciences (…), and economics … To conclude, the second
edition of Hierarchical Modeling and Analysis for Spatial Data
provides an excellent treatment of methods and applications in
spatial statistics. It takes into consideration 10 years of changes
(with respect to the first edition), including the changes induced
by the increasing complexity and volume of data and the increasing
complexity of questions that one aims to address with modeling and
inference approaches. In Chapter 1, the authors claim that they
aimed "to present a practical, self-contained treatment of
hierarchical modeling, and data analysis for complex spatial (and
spatiotemporal) datasets". They succeeded."
—Samuel Soubeyrand, INRA, France, in Mathematical Geosciences,
January 2017"If you want a thorough taste of the spatial statistics
field, Hierarchical Modeling and Analysis for Spatial Data is
definitely a book for you. It is accessible and comprehensive, and
it fully explores how useful spatial statistics can be without
sacrificing the theory it is grounded in. This is a great book for
graduate students and professors who want to understand the
theoretical underpinnings of the field as well as practitioners who
want a toolkit for tackling spatial problems. … the authors provide
an easy-to-use online resource with all of the book’s code and
datasets. Indeed, these examples are so comprehensive that readers
could learn a lot by simply going through them. The accessible
theoretical material paired with these detailed examples make
Hierarchical Modeling and Analysis for Spatial Data an especially
substantial and worthwhile investment. … the authors do not hold
back on their references. … Some could view this book as a spatial
statistics biography from the 1950s onward … If a graduate student
or professor wants a full taste of the spatial statistics
literature—where it has been, where it is, and where it still needs
to go—this is probably one of the best books they could pick up. …
We find this book to give a much more applied perspective with
better computational tools, and thus believe it to be more
accessible to a wider audience [than Cressie]. We recommend this
book to anyone who seriously wants to start being involved in
spatial statistics."
—Journal of the American Statistical Association, December
2015"This is a very welcome second edition of a nice and very
successful book written by three experts in the field … I have no
doubts that this updated text will continue being a compulsory
reference for those graduate students and researchers interested in
understanding and applying any of the three areas of spatial
statistics … printed in color and this helps to see better some of
the graphical representations … excellent book that I highly
recommend for anyone interested in the fascinating field of space
and space–time modeling. This is definitely one of those second
edition books that is worthwhile having. Many thanks to the authors
for their effort."
—Biometrics, March 2015Praise for the First Edition:"This book was
a pleasure to review. Most of the emphasis is on insight and
intuition with relatively little on traditional multivariate
techniques. I also found some of the explanations delightful …
while they did not convert me to Bayesianism, [the authors] made me
reconsider some of my assumptions. They later state 'Our book is
intended as a research monograph, presenting the state of the art'
and my impression is that they have succeeded … In many sections
the formulae are augmented by showing R or S code, making it easy
to actually apply the mathematics. In summary, this is a nice
book."
—ISI Short Book Reviews "The book contains a wealth of material not
available elsewhere in a unified manner. Each chapter contains
worked out examples using some well-known software packages and has
exercises with related computer code and data on a supporting web
page. The book is up to date in its coverage … an important
addition to the literature on spatial data analysis."
—Zentralblatt MATH 1053
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