Statistical Methods for Spatial Data
Statistical Methods for Spatial Data covers detecting and modeling spatial patterns, including variograms, covariance functions, linear predictions, and Gaussian process fitting at scale.
Summary
Recent recorded grades — Fall 2015: 3.66 GPA, 91.2% A/AB (n=34 letter grades); Spring 2024: 3.31 GPA, 66.7% A/AB (n=27 letter grades); Fall 2025: 3.33 GPA, 61.1% A/AB (n=18 letter grades).
What you’ll be able to do
Undertake exploratory analysis of spatial and spatio-temporal data with nonparametric estimators like empirical variograms.
Fit parametric and semi-parametric mean and covariance models to spatial and spatio-temporal data for the purposes of prediction and uncertainty quantification.
Understand important differences between interpolation and extrapolation and how to choose and design models more effectively for each application.
Use the R programming language to load, manipulate, and effectively work with spatial data in popular file formats like CSV, NetCDF, HDF5.
Use several popular software libraries and modeling paradigms for spatial and spatio-temporal problems like estimation and prediction.
Understand problems of identifiability and consistency in popular modeling paradigms.
Source: UW Guide
Grade history
↗All recorded terms · compare terms & instructors
Prerequisites
Course map(STAT 333 or 340) and (MATH 320, 340, 341, 345, or 375), graduate/professional standing, or declared in Statistics VISP
This is a best-effort interpretation; check the catalog requirements above.
Professors
Spring 2027No instructors recorded for this selection.
Historical instructors & teaching patterns
Recorded history may be incomplete and does not establish a future schedule.
Calendar & sections
Spring 2027Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
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Times are Central. Select a meeting for details; export includes recorded dates for the selected sections.
Meeting source records
Student experience
the class
Recent recorded grades — Fall 2015: 3.66 GPA, 91.2% A/AB (n=34 letter grades); Spring 2024: 3.31 GPA, 66.7% A/AB (n=27 letter grades); Fall 2025: 3.33 GPA, 61.1% A/AB (n=18 letter grades).
difficulty & workload
No workload feedback recorded.
Topics
- Variograms, covariance functions, and linear predictions
- Spectral domain models and spectral densities
- Spatial point processes
- Gaussian process model fitting at scale
Skills
- Exploratory analysis of spatial data using nonparametric estimators
- Fitting parametric and semi-parametric mean and covariance models
- Manipulating spatial data in R across various file formats
- Applying software libraries for spatial estimation and prediction
Grades
Latest available · Fall 2025— not enough history to project Spring 2027.
Grade distribution · % of letter grades
Grades over time
Through Fall 2025
More grade details Grade mix, volume & source data
Not enough comparable courses for Spring 2027 in UW–Madison.
Sources & history
Catalog & offerings
Descriptions, prerequisites, and recorded course offerings.
Catalog observation history
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Raw records
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Student reviews
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Grade history
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesRecorded 2026-10-14
Model outputs & technical records
LLM outputs across runs
Full model traces
Recorded model configuration, reasoning, and tool conversations.
Model & dataset provenance
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"model": "Qwen/Qwen3.8-27B",
"model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
"task_version": "17",
"output_id": "e65505cfea42d1110962552e401acdfb30eea5c743d846a9764ba9d060eac92e",
"requirements_status": "needs_review",
"dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
"observed_at": "2026-10-14 17:50:09.728220+00:00"
}