Lecture, two hours; laboratory, two hours. Requisite: course 401. Concepts and techniques fundamental to spatial statistics and analysis and visualization of data with geographic dimension. Introduction to statistical computing and tabular data processing and analysis techniques. Students learn to apply common spatial analysis methods in practical context. Emphasis on essential concepts in spatial statistics, including spatial relationships, spatial autocorrelation analysis, cluster analysis, spatial regression analysis, point-pattern analysis, and space-time modeling. Letter grading.