This course provides an introduction to spatial statistics for spatially referenced data. Spatial point patterns, geostatistical data, and area (regional/lattice) data are studied using the viewpoint that these are realizations from random processes. Major topics to be covered include spatial stochastic process, exploratory spatial data analysis, intensity function, K function, cluster statistics, spatial interpolation, spatial covariance functions, variograms, kriging, spatial autoregressive models, and geographically weighted regression. Computer exercises with R programming language (www.r-project.org) are designed to help students gain hands-on experience on the topics presented in lectures. Students are required to present and discuss assigned readings and develop an individual research project that applies spatial statistical methods in geographical problem solving. Prerequisites: Gog 408 or 508, or a similar introductory programming course on spatial data science using R and Python. Students should also have a basic understanding of statistics (equivalent to an introductory statistics course) and GIS (equivalent to an introductory GIS course). Please contact the instructor if you don't have these requirements, you may gain this background through self-learning.