EAS 543 will introduce you to statistical methods that account not only for the attributes of spatial features but also their location, allowing you to assess and understand the possible relationships between spatial features and the potential effects on the attributes. With the inclusion of the spatial component, we could ask and answer questions such as: Are my observations spatially related? What is the importance of assessing spatial relationships between observations? How to include these spatial relationships as part of the analysis?

By the end of this course, you will acquire a new set of analytical skills focused on the statistical analysis of spatial data. You will learn the theoretical background and practical applications of five statistical methods: spatial point pattern analysis, spatial association (LISA), regionalization, spatial inference, and interpolation. You will also gain hands-on experience working with freeware software (R), processing and analyzing raw spatial and non-spatial data. Data manipulation encompasses activities such as acquiring, organizing, and documenting. Analysis activities include analytical procedures, interpretation, synthesis, and summarizing findings in formats such as maps, graphs, and tables.