Semivariogram Cloud allows you to examine the spatial autocorrelation between the measured sample points and look for outliers (extreme values). In spatial autocorrelation , it is assumed that things that are close to one another are more alike. The Semivariogram Cloud lets you examine this relationship. To do so, a semivariogram value, which is the difference squared between the values of each pair of locations, is plotted on the y-axis relative to the distance separating each pair on the x-axis.
Note:
1. Each dot in the Semivariogram Cloud represents a pair of locations. Since closer locations should be more alike, the close locations should have small semivariogram values. As the distance between the pairs of locations increases, the semivariogram values should also increase. However, a certain distance is reached where the cloud flattens out, indicating that the relationship between the pairs of locations beyond this distance is no longer correlated.
2. If it appears that some data locations that are close together have a higher value than you would expect, you should investigate these pairs of locations to see if there is the possibility that the data is inaccurate.
3. the closer the locations, the lower the semivariogram values.
4. extremely high values of variables can create high semivariance values with locations nearby as well as far away. Eg. much higher values of ozone concentration in Los Angeles than other areas would create high semivariance values regaredless of nearby or far away.