This is an excellent source for public health professionals so far as the needed state-of-the-art concepts and methods that are needed to analyse and interpret geostatistical data. Basic knowledge of mathematical statistics is necessary to read through this well-written book. For the sake of readers with less statistics background, the authors have presented in the appendix probability distributions, covariates, random effects, likelihood function, estimation, testing, prediction, classical versus Bayesian inference, direct simulation, Markov chain Monte Carlo, and R coding. Focus has been made on disease mapping, environmental epidemiology, generalized linear models, variogram, and R-codes. The references are thorough and up-to-date. The examples are real-life oriented and interesting.
There are ten chapters covering a range of topics including motivation for mapping, empirical versus mechanistic models, regression modelling, generalized linear including logistic regression, spatial correlation, Gaussian process, spatial correlation, exponential versus Matern versus spherical family of distributions, variogram, likelihood-based inference, Bayesian and predictive inferences, model validation, binomial versus Poisson versus negative binomial sampling, Laplace approximation, point versus spatial discrete process, adaptive versus non-adaptive designs, preferential sampling, zero inflation, spatial-temporal analyses, visualizations, combining data from multiple surveys, and incomplete data among others. Some unique features of this well-written book are the illustrations and they include river blindness in Liberia, heavy metal monitoring in Galicia, malnutrition in Ghana, rolling malaria in Malawi, ozone concentration in Eastern United States, prevalence and intensity of infection among others.
This book is quite suitable to be a textbook for a graduate level course in global public health or geo-statistics. Researchers and doctoral graduate students seeking thesis topic ought to read this book. I enjoyed reading this book. I recommend this book to statistics and computing professionals.