Spatial Statistics and Spatio-Temporal Data (PDF)

In the spatial or spatio-temporal context, specifying the correct
covariance function is fundamental to obtain efficient predictions,
and to understand the underlying physical process of interest. This
book focuses on covariance and variogram functions, their role in
prediction, and appropriate choice of these functions in
applications. Both recent and more established methods are
illustrated to assess many common assumptions on these functions,
such as, isotropy, separability, symmetry, and intrinsic
correlation.

After an extensive introduction to spatial methodology, the book
details the effects of common covariance assumptions and addresses
methods to assess the appropriateness of such assumptions for
various data structures.

Key features:

* An extensive introduction to spatial methodology including a
survey of spatial covariance functions and their use in spatial
prediction (kriging) is given.

* Explores methodology for assessing the appropriateness of
assumptions on covariance functions in the spatial,
spatio-temporal, multivariate spatial, and point pattern
settings.

* Provides illustrations of all methods based on data and
simulation experiments to demonstrate all methodology and guide to
proper usage of all methods.

* Presents a brief survey of spatial and spatio-temporal models,
highlighting the Gaussian case and the binary data setting, along
with the different methodologies for estimation and model fitting
for these two data structures.

* Discusses models that allow for anisotropic and nonseparable
behaviour in covariance functions in the spatial, spatio-temporal
and multivariate settings.

* Gives an introduction to point pattern models, including
testing for randomness, and fitting regular and clustered point
patterns. The importance and assessment of isotropy of point
patterns is detailed.

Statisticians, researchers, and data analysts working with
spatial and space-time data will benefit from this book as well as
will graduate students with a background in basic statistics
following courses in engineering, quantitative ecology or
atmospheric science.