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. There are four parts with seventeen chapters covering the concepts and tools of challenging versus opportunities in spatial-temporal data, introduction to ‘spatial thinking’, nature of spatial-temporal attribute, relationship on the map with weight matrix, Bayesian approach to regression modelling, exploratory analysis of spatial data, hierarchical models, spatial dependence, several applications, spatial econometric models and applications, space-time separable models, space-time inseparable models, applications in modelling spatial-temporal data, future agendas among others. Every chapter begins with introduction, ends with conclusion and exercises which ease the comprehension of the contents of the book. The references in the end are well selected for the readers.
Noteworthy features in this book are aims of the book, structures of the book, heterogeneity, data sparsity, data versus parameter uncertainty, list of data sets used in the book, why it matters to map data among others. Interestingly, there are more maps (at times without a title) than the equations with descriptions in the statistics books.
I enjoyed reading this book. I recommend this book to economics, health, statistics and computing professionals and researchers.