View source for Intracluster Correlation Coefficient

==Cluster design planning criteria==
In the preceding chapter we have seen that there are various criteria that should be taken into account when designing cluster plots for a forest inventory (or any other sampling study). As always cost and practical criteria need to be balanced with statistical performance. As with plot design planning in general, the overall objective is to design the plots such, that for a given cost the highest possible within-plot variability is achieved for the most highly prioritized target variables.
The following basic “factors” need to be defined / optimized in cluster design planning in large area forest inventories, where clusters consist of a number of m sub-plots:

*number of sub-plots;
*type of sub-plot;
*size of sub-plot;
*geometrical arrangement of sub-plots;
*distance between sub-plots.

The distance between sub-plots is either empirically defined, or one tries to derive the [[spatial autocorrelation]] function and defines the inter-plot distance then in such a manner that inter-plot correlation is small. The larger the distance between neighboring sub-plots, the smaller is usually the correlation. However, very large distances are obviously difficult in the field in many cases because of the long (and essentially inefficient) walking time so that a certain level of correlation is accepted.
In many cases in large area forest inventories, the cluster design is not only optimized towards statistical performance, but it is intended that one cluster can be worked on and finished within one day. It is usually too inefficient to come back a second time to a cluster plot, because in large area inventories, distances are usually large.


[[Category:Plot design]]