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MODERN SPATIOTEMPORAL GEOSTATISTICS

George Christakos
University of North Carolina, Chapel Hill, USA

This is the first book on the subject of spatiotemporal geostatistics.  It is
widely recognized that the techniques of traditional geostatistics and
classical statistics-based imaging, which have been used for several decades,
have reached their limit, and the time has come for some alternative
approaches to be given a chance.  This book, therefore, is an introduction to
the fundamentals of modern spatiotemporal geostatistics and space/time imaging
of life support-related systems (physical, ecological, agricultural,
epidemiological, medical, etc.), which is a group of concepts and methods that
are the products of the advancement of the epistemic status of stochastic data
analysis.  The latter is considered from a novel perspective, promoting the
view that a deeper understanding of a theory of knowledge is an important
prerequisite for the development of improved mathematical models of scientific
mapping across space and time.  Thus, a stochastic analysis based on the
characterization of physical connection offers a more meaningful description
of the spatiotemporal framework suggested by the laws of nature than an
analysis based merely on explicitly statistical reasoning.  The main focus of
the book is the Bayesian Maximum Entropy (BME) approach for studying
spatiotemporal distributions of life support fields (contaminant variables,
exposure processes, gene frequency distributions, etc.).  As part of the
modern geostatistics paradigm, the BME approach provides a fundamental insight
into the mapping problem in which the knowledge of a natural field, not the
field itself, is the direct object of study.  The thread running throughout
the book is that the modern geostatistics approach to space/time problems is
that of natural scientists who are more interested in a stochastic analysis
concerned with both the ontological level (building models for physical and
biological systems) and the epistemic level (using what we know about the
natural systems and integrating and modeling knowledge from a variety of
scientific disciplines), rather than in the purely inductive account of
science (based merely on a linear relationship between data and hypotheses and
theory-free techniques).  The BME formulation incorporates a wide variety of
general knowledge bases (physical laws, biological principles, scientific
theories, phenomenological relationships, multiple-point space/time
statistics) as well as site-specific information (hard and soft data,
uncertain observations, fuzzy relations, field measurements, probabilistic
logics, etc.).  A powerful class of generalized spatiotemporal random fields
is discussed.  Several well-known models, like space/time fractals and
wavelets, are shown to be special cases of the generalized random field model.
 Modern spatiotemporal geostatistics offers the basis for the development of
advanced functions for temporal geographical information systems (GIS) for use
in life support sciences.

2000   304 pp.; 131 line illus.
0-19-513895-3  $60.00/$48.00

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