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Course Description
Geospatial data structures in R. Plotting and exploring data in R. Using R to manage data. Point process analysis using smoothed kernel density estimation and kriging. Variograms and semi-variograms. Spatial autocorrelation in areal data. Moran’s I and Geary’s G. Spatial autoregression.Course Outline
- Introduction to R
- Spatial Data Structures in R
- Using R as a GIS
- Point Process Analysis and Kriging
- Spatial Autocorrelation
Learner Outcomes
- Analyze the spread of diseases across time
- Build and analyze models to assess the health of populations across time and geographic regions
- Communicate results of spatial and spatio-temporal models applied to health data
Prerequisites
GEO5010 - Introduction to GIS or equivalent experience.Duration
30 Hours | 5 Days or 10 NightsLoading...
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*Academic Unit eligibility to be determined by college/university in which you are enrolled in a degree seeking program.
*Academic Unit eligibility to be determined by college/university in which you are enrolled in a degree seeking program.