Providing a graduate level introduction to various aspects of stochastic geometry, spatial statistics and random fields, this volume places a special emphasis on fundamental classes of models and algorithms as well as on their applications, for example in materials science, biol…
Providing a graduate level introduction to various aspects of stochastic geometry, spatial statistics and random fields, this volume places a special emphasis on fundamental classes of models and algorithms as well as on their applications, for example in materials science, biol…
A severe thunderstorm morphs into a tornado that cuts a swath of destruction through Oklahoma. How do we study the storm's mutation into a deadly twister? Avian flu cases are reported in China. How do we characterize the spread of the flu, potentially preventing an epidemic? T…
From understanding environmental processes and climate trends to developing new technologies for mapping public-health data and the spread of invasive species, there is a high demand for statistical analyses of data that take spatial, temporal, and spatio-temporal information in…
The number and variety of statistical techniques for spatial analysis of ecological data are burgeoning and many ecologists are unfamiliar with what is available and how the techniques should be used. This book provides an overview of the wide range of spatial statistics availab…
This book presents several different statistical approaches to the environmental sciences, including Bayesian and nonparametric methodologies. The book begins with an introduction to types of data, evaluation of data, modeling and estimation, random variation, and sampling--all …
Although numerous statistical methods for analyzing spatial point patterns have been available for several decades, they haven't been extensively applied in an ecological context. Addressing this gap, this book shows how the techniques of point-pattern analysis are useful for ta…
This study predicts the presence or absence of terrorism in Europe at a previously unexplored spatial scale. Dependent variables consisted of satellite imagery and socio-environmental data. Five machine learning models were evaluated over a binary classification problem, the pre…
This work consists of three parts. For the first portion we consider a generalized linear model with possible correlated random effects. We use a Bayesian hierarchical generalized linear mixed model to estimate success rates at a post-stratified level. The model includes an auto…
While previous literature has emphasized the importance of energy and public infrastructure services for economic development, questions surrounding the implications of unequal spatial distribution in access to these resources remain, particularly in the developing country conte…
Cancer epidemiology has a long history of applying geographic thinking to address long-standing place-based disparities. This dissertation adds new knowledge to geospatial approaches to social determinants of cancer outcomes. It establishes a framework consisting of three dimens…
