STATISTICAL METHODS FOR SPATIAL DATA

Prerequisites & course information

Requisites: ( STAT 333 or 340 ) and ( MATH 320 , 340 , 341 , 345 , or 375 ), graduate/professional standing, or declared in Statistics VISP Course Designation: Core GenEd - Natural Science & Wellness Level - Advanced L&S Credit - L&S Liberal Arts and Science Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Fall 2025 Learning Outcomes: 1. Undertake exploratory analysis of spatial and spatio-temporal data with nonparametric estimators like empirical variograms. Audience: Both Grad & Undergrad 2. Fit parametric and semi-parametric mean and covariance models to spatial and spatio-temporal data for the purposes of prediction and uncertainty quantification. Audience: Both Grad & Undergrad 3. Understand important differences between interpolation and extrapolation and how to choose and design models more effectively for each application. Audience: Both Grad & Undergrad 4. Use the R programming language to load, manipulate, and effectively work with spatial data in popular file formats like CSV, NetCDF, HDF5. Audience: Undergraduate 5. Use several popular software libraries and modeling paradigms for spatial and spatio-temporal problems like estimation and prediction. Audience: Both Grad & Undergrad 6. Understand problems of identifiability and consistency in popular modeling paradigms. Audience: Graduate Learning Outcomes: 1. Undertake exploratory analysis of spatial and spatio-temporal data with nonparametric estimators like empirical variograms. Audience: Both Grad & Undergrad 2. Fit parametric and semi-parametric mean and covariance models to spatial and spatio-temporal data for the purposes of prediction and uncertainty quantification. Audience: Both Grad & Undergrad 3. Understand important differences between interpolation and extrapolation and how to choose and design models more effectively for each application. Audience: Both Grad & Undergrad 4. Use the R programming language to load, manipulate, and effectively work with spatial data in popular file formats like CSV, NetCDF, HDF5. Audience: Undergraduate 5. Use several popular software libraries and modeling paradigms for spatial and spatio-temporal problems like estimation and prediction. Audience: Both Grad & Undergrad 6. Understand problems of identifiability and consistency in popular modeling paradigms. Audience: Graduate