Inverse-Distance Gridding

Inverse-Distance is one of the more common gridding methods. With this method, the value assigned to a grid node is a weighted average of either all of the data points or a number of directionally distributed neighbors. The value of each of the data points is weighted according to the inverse of its distance from the grid node, taken to a user-selected power. The greater the value of the exponent you specify, the more localized the gridding since distant points will have less influence on the value assigned to each grid node.

Advantages: The Inverse-Distance method produces a smooth and continuous grid and will not exaggerate its extrapolations beyond the given data points. The range of grid values will be smaller than the data point range: The highest grid value will be less than the maximum data point, and the lowest grid value will be greater than the minimum data point.

Disadvantages: Inverse-Distance can produce a "bulls-eye" effect in some data sets.

 

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