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Modelling Spatial and Spatial-Temporal Data - A Bayesian Approach (Paperback)
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Modelling Spatial and Spatial-Temporal Data - A Bayesian Approach (Paperback)
Series: Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences
Expected to ship within 12 - 17 working days
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Modelling Spatial and Spatial-Temporal Data: A Bayesian Approach is
aimed at statisticians and quantitative social, economic and public
health students and researchers who work with small-area spatial
and spatial-temporal data. It assumes a grounding in statistical
theory up to the standard linear regression model. The book
compares both hierarchical and spatial econometric modelling,
providing both a reference and a teaching text with exercises in
each chapter. The book provides a fully Bayesian, self-contained,
treatment of the underlying statistical theory, with chapters
dedicated to substantive applications. The book includes WinBUGS
code and R code and all datasets are available online. Part I
covers fundamental issues arising when modelling spatial and
spatial-temporal data. Part II focuses on modelling cross-sectional
spatial data and begins by describing exploratory methods that help
guide the modelling process. There are then two theoretical
chapters on Bayesian models and a chapter of applications. Two
chapters follow on spatial econometric modelling, one describing
different models, the other substantive applications. Part III
discusses modelling spatial-temporal data, first introducing models
for time series data. Exploratory methods for detecting different
types of space-time interaction are presented, followed by two
chapters on the theory of space-time separable (without space-time
interaction) and inseparable (with space-time interaction) models.
An applications chapter includes: the evaluation of a policy
intervention; analysing the temporal dynamics of crime hotspots;
chronic disease surveillance; and testing for evidence of spatial
spillovers in the spread of an infectious disease. A final chapter
suggests some future directions and challenges. Robert Haining is
Emeritus Professor in Human Geography, University of Cambridge,
England. He is the author of Spatial Data Analysis in the Social
and Environmental Sciences (1990) and Spatial Data Analysis: Theory
and Practice (2003). He is a Fellow of the RGS-IBG and of the
Academy of Social Sciences. Guangquan Li is Senior Lecturer in
Statistics in the Department of Mathematics, Physics and Electrical
Engineering, Northumbria University, Newcastle, England. His
research includes the development and application of Bayesian
methods in the social and health sciences. He is a Fellow of the
Royal Statistical Society.
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