The Search for Spatial Associations
Summary
Module Objectives
Module Outline
Module Details
Module Evaluation
Before you begin: try our
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Outline
The outline of the module is as follows:
- Readings
- Motivation: effective sample size
- One interpretation of spatial autocorrelation
- The univariate case
- The bivariate case
- Decision-making in the presence of spatial autocorrelation: Spatial statistical theory simulation and matrix results
- Variance inflation/deflation factors
- Confidence intervals for means
- The significance of correlation coefficients
- Graphical depictions
- The relationship between the SAR autoregressive parameter value and Moran Coefficient
- The relationship between the univariate effective sample size and the SAR autoregressive parameter estimate
- The relationship between the bivariate effective sample size and a pair of SAR autoregressive parameter estimates
- Some useful equations
- Predicting the SAR autoregressive parameter value from a Moran Coefficient
- Predicting the univariate effective sample size from an SAR autoregressive parameter estimate
- Predicting the bivariate effective sample size from a pair of SAR autoregressive parameter estimates
- Applications: Haining's Glasgow disease incidence data
- Summary
The Module
Dr. Griffith's materials
Now that you're done:
Page by Andy Long.
Comments appreciated.
aelon@sph.umich.edu