# Interpolation

The interpolation technique is a mathematical method to create new data points from an existing data set. This technique uses the information from the variables in the data set to predict values of the same variables in other locations within the sampling area (Figure 1).

There are many different types of interpolation techniques, and they can go from simply assigning the value of the nearest existing data point to the new data point, to complex models that take into account all the dataset everytime that they need to predict one new data point.

As an example, in Figure 1 the blue circles are the sampled data points. Using the interpolation technique, a new data point (blue square) can be estimated. The size of the blue circles represents the influence of the original data points on the new data point generated. The closer the original data point is (blue circle) to the newly generated one(blue square), the more influence it will have on the new data point. Note that all previous data points will have some influence on that new data point, no matter how far away they are.

**Figure 1.** Example of how interpolation works.