Advanced spatial statistics
Graduate · Statistics
Syllabus focus
Standard syllabus · STEM / applied
Pricing
Graduate-level rates are set on consultation. See the pricing page for K–12 and undergraduate rates.
Topics typically covered
Standard syllabus
Spatial stochastic processes
- Gaussian random fields
- Variogram modeling and kriging
- Spatial prediction and uncertainty quantification
- Anisotropy and nonstationary processes (intro)
- Lattice data and CAR models
Point patterns and areal data
- Poisson and Cox point processes
- K-functions and spatial clustering tests
- Spatial autoregressive models for areal data
- Disease mapping and BYM models (introduction)
- Change of support problem
Computation
- Likelihood and Bayesian spatial computation
- INLA for spatial models (overview)
- Large spatial datasets: approximations
- Software: INLA, spBayes, Stan spatial examples
STEM / applied
Applied advanced spatial projects
- Environmental exposure mapping case studies
- Spatial epidemiology applications
- Integrating remote sensing with spatial models
- Validating spatial predictions with holdout designs
- Visualization of spatial uncertainty
- Reproducible spatial workflows with sf and terra
Additional applied practice
- Reviewing assumptions with domain experts
- Documenting analysis choices for reproducibility
- Sensitivity analyses for key modeling decisions
- Connecting results to the original research or business question
Notes
Graduate spatial statistics course covering point processes, geostatistics, and Bayesian spatial models in depth.