MATH 3410 - Applied Spatial Statistics

3 Credit(s) | $3.75 Fee

An introduction to analyzing patterns, modeling relationships, and interpolation and prediction for spatial data structures. Topics covered include spatial data exploration and visualization, spatial autocorrelation with Moran’s I, hot-spot analysis, point process analysis, geographically weighted regression, spatial autoregressive models, nearest neighbor distances, semivariograms, and Kriging. Students will use a statistical programming language (e.g. R or Python) throughout the course. (Fall - Odd Years) [Graded Letter]

Prerequisite(s): (MATH 1040 or MATH 1190 or MATH 3700) and (MATH 1050 or MATH 1100 or MATH 1210) - Prerequisite Min. Grade: C