- Measures of Central Tendency aren't enough! We need to characterize variation.
- Variance provides error bars, characterizes distributions (e.g. the
Normal), and distinguish means (ANOVA).
- Complete Spatial Randomness is the default assumption in many spatial problems.
- We have methods for detecting departures from complete spatial randomness;
that is, spatial autocorrelation (e.g. Moran's I).
- In estimation problems, the variogram is a sensible method for determining
the effects of space, providing reasonable weights in an estimation
scheme as a function of distance and direction.
- Kriging is a good estimation scheme based on this spatial decomposition of
the variance, and allows us to estimate the values of a variable away from data
locations.
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