Statistics for Spatio-Temporal Data (PDF)

Winner of the 2013 DeGroot Prize.

A state-of-the-art presentation of spatio-temporal processes,
bridging classic ideas with modern hierarchical statistical
modeling concepts and the latest computational methods

Noel Cressie and Christopher K. Wikle, are also winners
of the 2011 PROSE Award in the Mathematics category, for the
book "Statistics for Spatio-Temporal Data" (2011),
published by John Wiley and Sons. (The PROSE awards, for
Professional and Scholarly Excellence, are given by the Association
of American Publishers, the national trade association of the US
book publishing industry.)

Statistics for Spatio-Temporal Data has now been
reprinted with small corrections to the text and
the bibliography. The overall content and pagination of the
new printing remains the same; the difference comes in
the form of corrections to typographical errors, editing of
incomplete and missing references, and some updated spatio-temporal
interpretations.

From understanding environmental processes and climate trends to
developing new technologies for mapping public-health data and the
spread of invasive-species, there is a high demand for statistical
analyses of data that take spatial, temporal, and spatio-temporal
information into account. Statistics for Spatio-Temporal
Data presents a systematic approach to key quantitative
techniques that incorporate the latest advances in statistical
computing as well as hierarchical, particularly Bayesian,
statistical modeling, with an emphasis on dynamical spatio-temporal
models.

Cressie and Wikle supply a unique presentation that
incorporates ideas from the areas of time series and spatial
statistics as well as stochastic processes. Beginning with separate
treatments of temporal data and spatial data, the book combines
these concepts to discuss spatio-temporal statistical methods for
understanding complex processes.

Topics of coverage include:

* Exploratory methods for spatio-temporal data, including
visualization, spectral analysis, empirical orthogonal function
analysis, and LISAs

* Spatio-temporal covariance functions, spatio-temporal kriging,
and time series of spatial processes

* Development of hierarchical dynamical spatio-temporal models
(DSTMs), with discussion of linear and nonlinear DSTMs and
computational algorithms for their implementation

* Quantifying and exploring spatio-temporal variability in
scientific applications, including case studies based on real-world
environmental data

Throughout the book, interesting applications demonstrate the
relevance of the presented concepts. Vivid, full-color graphics
emphasize the visual nature of the topic, and a related FTP site
contains supplementary material. Statistics for Spatio-Temporal
Data is an excellent book for a graduate-level course on
spatio-temporal statistics. It is also a valuable reference for
researchers and practitioners in the fields of applied mathematics,
engineering, and the environmental and health sciences.