Alan E. Gelfand is The James B Duke Professor Emeritus of Statistical Science at Duke University. He also enjoys a secondary appointment as Professor of Environmental Science and Policy in the Nicholas School. Author of more than 330 papers and 6 books, Gelfand is internationally known for his contributions to applied statistics, Bayesian computation and Bayesian inference. For the past thirty years, Gelfand's primary research focus has been in the area of statistical modeling for spatial and space-time data. He has advanced methodology, using the Bayesian paradigm, to associate fully model-based inference with spatial and space-time data. His chief areas of application include spatio-temporal environmental and ecological processes.

Sudipto Banerjee is Professor of Biostatistics and Senior Associate Dean for Academic Programs in the Fielding School of Public Health at the University of California, Los Angeles (UCLA). He holds joint appointments as a Professor in the UCLA Department of Statistics and Data Science and as an Affiliate faculty in the UCLA Institute of Environment and Sustainability. Banerjee has authored over 200 research articles, 2 textbooks, 2 committee reports for the National Research Council of the National Academies, and an edited handbook on spatial epidemiology. Banerjee is well-known for his research expertise and methodological advancements in Bayesian hierarchical modeling and inference for spatial-temporal data; theoretical and computational developments for Gaussian processes; environmental processes and their impact on public health; spatial epidemiology; stochastic process models; statistical learning from physical and mechanistic systems; survey sampling and survival analysis.

1 Overview of spatial data problems. 2 Basics of point-referenced data models. 3 Some theory for point-referenced data models. 4 Basics of areal data models. 5 Basics of Bayesian inference. 6 Hierarchical modeling for univariate spatial data. 7 Spatial misalignment. 8 Basics of Point Pattern Data Modeling. 9 Bayesian Analysis of Point Pattern Models. 10 Multivariate spatial modeling for point-referenced data. 11 Models for multivariate areal data.12 Spatiotemporal modeling.13 Modeling large spatial and spatiotemporal datasets. 14 Spatial gradients and wombling. 15 Spatial survival models. 16 Spatial data fusion (and preferential sampling). 17 Special topics in spatial process modeling.