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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 Nights
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