Moran’s I (both global and local) is a measure of spatial autocorrelation. One form of its equation looks like this: Note the residual and spread terms. As such, Moran’s I can be sensitive to outliers. The following example demonstrates this point: You can map the simulated data as follows: The p-values from the Monte Carlo […]
Archive for category: Spatial analysis
The following example assumes a stationary point process. This example does NOT assume a stationary process.
Data files used with the following script were downloaded from: ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/. The following figure shows the standardized regression coefficients from a Universal Kriging analysis of the Precip_total_coef_all_z.csv file (the values are in fact correlation coefficients). The time period spans 1975 – 2009.
Data for this exercise can be downloaded from Maine_pop. The resulting plots are shown side-by-side To display all the color palettes available from Brewer type:
!! Data for this exercise can be downloaded from here !!
The following example was created in R and was inspired from O’Sullivan and Unwin’s Geographic Information Analysis (1st edition) Data for this exercise can be downloaded here.