Interpolation is the method of using points with known values or sampling points at certain unknown points. For any geographic point data, such as temperature , rainfall, chemical concentrations, noise levels, etc., unknown values may be expected. Yet it has benefits and drawbacks, what are its main strengths and weaknesses?
I don't know if there are any strengths for using interpolation. You are substituting a guess for an actual value. I'd much rather know the actual value. There is also no one interpolation method. You can assume a linear, quadratic, cubic, etc. relationship between the x and y variables and it is often not that easy to know which one is the right one. Another thing affecting the accuracy of your interpolation is how many known pairs of the x:y variables are included in your analysis. Presumably, the more, the better. Another thing people often forget about is to never extrapolate outside the range of the known pairs of x:y variables.
- This method can calculate extreme changes in terrain such as: Cliffs, Fault Lines. These methods interpolate easily very dense evenly spaced points (flat areas with cliffs). It can increase or decrease the amount of sample points to influence cell values. It cannot estimate above maximum or below minimum values. It is not very good for peaks or mountainous areas.
Strength: high accuracy, you can read some articles for applications of splines (cubic) for rendering 3D surfaces.
Weakness: low performance: high computing cost and use of a lot of the computer resources.
Strength:
Can increase or decrease amount of sample points to influence cell values.
Weakness:
Not very good for peaks or mountainous areas
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