Lecture, three hours; discussion, one hour. Requisites: courses 200A, 200B, 202A, 202B. Introduction of various methods for exploring, modeling, and analyzing spatially referenced datasets, with emphasis on environmental/natural sciences and public health. Statistical theory and foundations for carrying out principled and scientifically rigorous inference on spatially referenced datasets and computational methods and algorithms for executing statistical modeling in practice. Practical examples and applications demonstrated using open-source statistical software environment R and datasets from diverse fields, such as public health, environmental health, natural sciences, and economics. Letter grading.