4.0

credits

Average Course Rating

Introduces statistical techniques used to model, analyze, and interpret public health related spatial data. Casts analysis of spatially dependent data into a general framework based on regression methodology. Covers the geostatistical techniques of kriging and variogram analysis, point process methods for spatial event and case control data, and area-level analysis. Focuses on statistical modeling and topics relating to clustering and cluster detection of health related events. Provides an introduction to the public domain statistical software R, to be used for analysis. Reinforces skills and concepts related to the spatial science paradigm: Spatial Data, GIS, and Spatial Statistics.