Spatial statistics
Undergraduate · Statistics
Syllabus focus
Standard syllabus · STEM / applied
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$60.00 · 60 min · Undergraduate · Online ($60/hr)
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Topics typically covered
Standard syllabus
Spatial data concepts
- Point, areal, and geostatistical data types
- Coordinate systems and map projections
- Spatial autocorrelation: Moran's I and variograms
- Tobler's law and stationarity assumptions
- Spatial sampling designs
Geostatistics
- Empirical and theoretical variograms
- Ordinary kriging
- Kriging variance and cross-validation
- Spatial interpolation vs regression
- Anisotropy and nested structures (intro)
Spatial regression
- Spatial lag and spatial error models (introduction)
- Point process models overview
- Disease mapping and smoothed rate estimation
- Software: sp, sf, gstat in R (or GeoPandas)
STEM / applied
Applied spatial projects
- Linking census, environmental, and health GIS layers
- Hot spot analysis for crime or pollution data
- Remote sensing data in spatial models (intro)
- Visualization with choropleths and heat maps
- Uncertainty maps for spatial predictions
- Case studies in ecology and urban planning
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
Undergraduate spatial courses introduce kriging and spatial regression at an accessible level. Applied sections use GIS-linked datasets.