MATH 3410 - Applied Spatial Statistics

3 Credit(s) | $3.75 Fee

An introduction to analyzing spatial 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