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Statistical Methods for Spatial Data Analysis - Texts in Statistical Science (Paperback)
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Statistical Methods for Spatial Data Analysis - Texts in Statistical Science (Paperback)
Series: Chapman & Hall/CRC Texts in Statistical Science
Expected to ship within 12 - 17 working days
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Understanding spatial statistics requires tools from applied and
mathematical statistics, linear model theory, regression, time
series, and stochastic processes. It also requires a mindset that
focuses on the unique characteristics of spatial data and the
development of specialized analytical tools designed explicitly for
spatial data analysis. Statistical Methods for Spatial Data
Analysis answers the demand for a text that incorporates all of
these factors by presenting a balanced exposition that explores
both the theoretical foundations of the field of spatial statistics
as well as practical methods for the analysis of spatial data. This
book is a comprehensive and illustrative treatment of basic
statistical theory and methods for spatial data analysis, employing
a model-based and frequentist approach that emphasizes the spatial
domain. It introduces essential tools and approaches including:
measures of autocorrelation and their role in data analysis; the
background and theoretical framework supporting random fields; the
analysis of mapped spatial point patterns; estimation and modeling
of the covariance function and semivariogram; a comprehensive
treatment of spatial analysis in the spectral domain; and spatial
prediction and kriging. The volume also delivers a thorough
analysis of spatial regression, providing a detailed development of
linear models with uncorrelated errors, linear models with
spatially-correlated errors and generalized linear mixed models for
spatial data. It succinctly discusses Bayesian hierarchical models
and concludes with reviews on simulating random fields,
non-stationary covariance, and spatio-temporal processes.
Additional material on the CRC Press website supplements the
content of this book. The site provides data sets used as examples
in the text, software code that can be used to implement many of
the principal methods described and illustrated, and updates to the
text itself.
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