Interpolation Methods

GIS 5935 Special Topics in GIS 
Lab 5

Student Learning Outcomes:

• Carry out different surface interpolation techniques in GIS, including Theissen, IDW, and Spline
• Critically interpret the results from surface interpolation techniques
• Compare and contrast different surface interpolation techniques

Theissen:

This interpolation method is also called nearest neighbor interpolation. One of the benefits of the thesisian interpolation method is its simplicity. It is relatively simple to compute and results in a simple output where the Z value for each and every polygon is the same. one of the drawbacks is that it produces an output that is not smooth. values change abruptly from one polygon to another. This method also provides an exact interpolator. All of these characteristics mean that continuous data may not be represented accurately And that the density of the input sample points Has a large impact on the accuracy of the interpolation.

IDW (Inverse Distance Weighted):

Inverse Distance Weighted uses the sample values and the distance to the nearest known point to estimate the value at the sample values. The closer to the known point a value is, the more weight it is given in the calculation (Bolstad pg 519).  This method normally produces a smooth surface with no gaps and usually results in an exact interpolator (surface passes directly through sample values).

Spline:

The spline interpolation method uses sample points as a guide to creating a smooth surface. A mathematical function is used to create a surface that passes through the sample points with the least amount of curvature. Like IDWs, splines also give exact interpolations and produce a smooth surface with no gaps. In this lab, we manually altered the data in an endeavor to make the interpolation more accurate. I ran a regularized spline with the original unedited data and a tension spline with the modified data (pictured below).