Robert P. Haining, Guangquan Li

Modelling Spatial and Spatial-Temporal Data: A Bayesian Approach (eBook, PDF)

A Bayesian Approach

• Format: PDF

Produktbeschreibung

Analyzes spatial and spatial-temporal data, focuses on key datasets, data analysis using WinBUGS, R, GeoDa. Examines spatial and spatial-temporal data modeling in social, economic sciences. Looks at modeling decisions made in the course of research, undertakes data analysis, interpret results.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 400
• Erscheinungstermin: 27. Januar 2020
• Englisch
• ISBN-13: 9781482237436
• Artikelnr.: 58571471

Autorenporträt

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 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.

Inhaltsangabe

Introduction. Thinking spatially, thinking statistically in the social and
economic sciences. The nature of spatial data and the implications for
statistical analysis. Exploratory analysis of spatial and spatial-temporal
data. Bayesian regression modeling with spatial data. Introduction to the
Bayesian approach to regression modeling with spatial data. Topics in
spatial modeling. Further topics in spatial modeling. Bayesian regression
modeling with spatial-temporal data. Generic issues in spatial-temporal
modeling. Topics in spatial-temporal modeling. Appendices.

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Introduction. Thinking spatially, thinking statistically in the social and economic sciences. The nature of spatial data and the implications for statistical analysis. Exploratory analysis of spatial and spatial-temporal data. Bayesian regression modeling with spatial data. Introduction to the Bayesian approach to regression modeling with spatial data. Topics in spatial modeling. Further topics in spatial modeling. Bayesian regression modeling with spatial-temporal data. Generic issues in spatial-temporal modeling. Topics in spatial-temporal modeling. Appendices.

Rezensionen

"Knowledge on statistical theory and regression concepts are essential to read, comprehend, appreciate, and use the rich contents of this fascinating book. This well-written book is a good source for the Bayesian concepts and methods to practice the spatial-temporal analysis using R and WinBugs codes . . . I recommend this book to economics, health, statistics and computing professionals and researchers."
-Ramalingam Shanmugam, Texas State University

"Overall, this book stands out among other spatial statistics books because of its ability to help readers develop practical modeling skills. Specifically, R code snippets are provided when specific R packages or functions are needed to handle geospatial data sets. The impressive number of case studies provide real-world guidance on how to adapt the same modeling strategies, with the accompanyingWinBUGS code, to other data sets. ... In summary, this book is an excellent resource for graduate students, statisticians, and quantitative researchers who are interested in analyzing areal spatial data. The inclusion of both spatial hierarchical models and econometrics models is particularly unique. Finally, the book's organization, contents, and writing style also encourage self-learning."
-Howard H. Chang in Biometrics, March 2022