Modelling Spatial and Spatial-Temporal Data: A Bayesian Approach

Modelling Spatial and Spatial-Temporal Data: A Bayesian Approach is a book for statisticians and quantitative social, economic, and public health students and researchers working with small-area spatial and spatial-temporal data. It covers fundamental issues, modelling cross-sectional spatial data, spatial econometric modelling, modelling spatial-temporal data, and applications. The book assumes a grounding in statistical theory and includes WinBUGS code and R code, with all datasets available online.

\n Format: Paperback / softback
\n Length: 640 pages
\n Publication date: 30 September 2021
\n Publisher: Taylor & Francis Ltd
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Modelling Spatial and Spatial-Temporal Data: A Bayesian Approach is a comprehensive guide designed for statisticians, quantitative social, economic, and public health students, and researchers working with small-area spatial and spatial-temporal data. It assumes a solid foundation in statistical theory, up to the standard linear regression model. The book offers a comparative analysis of hierarchical and spatial econometric modelling, serving as both a reference and a teaching text with exercises in each chapter. It provides a fully Bayesian, self-contained treatment of the underlying statistical theory, with dedicated chapters covering substantive applications.

The book includes comprehensive WinBUGS and R code, along with access to all datasets online.

Part I delves into fundamental considerations when modelling spatial and spatial-temporal data. It explores various issues such as the choice of spatial and temporal models, the specification of spatial autocorrelation structures, and the handling of missing data. Part II focuses on modelling cross-sectional spatial data, beginning with exploratory methods that aid in the modelling process. This section includes two theoretical chapters on Bayesian models and a chapter showcasing practical applications. Two additional chapters delve into spatial econometric modelling, presenting different models and discussing substantive applications.

Part III explores modelling spatial-temporal data, starting with time series models. It introduces exploratory techniques to detect different types of space-time interactions, followed by two chapters on space-time separable and inseparable models. An applications chapter showcases real-world examples, including policy evaluation, crime hotspot analysis, chronic disease surveillance, and testing for spatial spillovers in infectious disease spread. A final chapter provides insights into future directions and challenges in the field.

This comprehensive guide offers a valuable resource for researchers and practitioners seeking to employ Bayesian approaches to modelling spatial and spatial-temporal data, enabling them to analyze and interpret data more effectively and make informed decisions based on empirical evidence.

\n Weight: 1180g\n
Dimension: 178 x 253 x 41 (mm)\n
ISBN-13: 9781032175003\n \n