Inverse Distance Weighted

The Inverse Distance Weighted method is the practical, easy-to-understand interpolator. When you use IDW, you are applying a "one size fits all" assumption to your sample points.

IDW works best for dense, evenly-spaced sample point sets. It does not consider any trends in the data, so, for example, if actual surface values change more in the north-south direction than they do in the east-west direction (because of slope, wind, or some other factor), the interpolated surface will average out this bias rather than preserve it.

IDW interpolation considers the values of the sample points and the distance separating them from the estimated cell. Sample points closer to the cell have a greater influence on the cell's estimated value than sample points that are further away.

Each of the five sample points in this example have a different value and distance from the estimated cell.

Inverse Distance Weighting cannot make estimates above the maximum or below the minimum sample values. For an elevation surface, this has the effect of flattening peaks and valleys (unless their high and low points are part of the sample). Because the estimated values are averages, the resulting surface will not pass through the sample points.

A surface created with IDW will not exceed the value range of the sample points or pass through those points.

You can adjust the relative influence of sample points. In other words, you can increase how much power the values of sample points have over the interpolation process. Increased power means that the output cell values become more localized and less averaged. Their influence, however, drops off rapidly with distance.

The solid line represents more power and the dashed line represents less power. The higher the power, the more localized an affect a sample point's value has on the resulting surface.

Lowering the power that sample point values have provides a more averaged output because sample points farther away become more and more influential until all of the sample points have the same influence.