##### AM-22 - Global measures of spatial association

- Describe the effect of the assumption of stationarity on global measures of spatial association
- Justify, compute, and test the significance of the join count statistic for a pattern of objects
- Compute the K function
- Explain how a statistic that is based on combining all the spatial data and returning a single summary value or two can be useful in understanding broad spatial trends
- Compute measures of overall dispersion and clustering of point datasets using nearest neighbor distance statistics
- Compute Moran’s I and Geary’s c for patterns of attribute data measured on interval/ratio scales
- Explain how the K function provides a scale-dependent measure of dispersion

## FC-37 - Spatial Autocorrelation

The scientific term spatial autocorrelation describes Tobler’s first law of geography: everything is related to everything else, but nearby things are more related than distant things. Spatial autocorrelation has a:

Positive spatial autocorrelation constitutes the focal point of its past and present; one expectation is that negative spatial autocorrelation will become a focal point of its future.