Choosing the right interpolation algorithm

Choosing the right interpolation algorithm
SPRING-help >> Interpolation >>

The following table is intended to help you choose the right interpolation algorithm. The recommendations mainly relate to the computing effort and the resulting computing time.

 

 

Number of measuring points

 

Remark

few (<5000)

medium

many (>5000)

Gaussian interpolation

x

X

x

smooth isolines and accurate, recommended for most cases

Area Interpolation

(x)

x

x

good for digitised isolines

Distance weighting

0

(x)

x

Tendency for "staircase effect"

Kriging

x

(x)

-

smooth isolines with suitable

parameters,Parameter selection complex

Nearest Neighbour

x

x

0

Smooth isolines

 

Where: - : poor / 0: less suitable / (x): suitable / x: well suitable

 

The current implementation of Gaussian interpolation is suitable for any amount of data. This interpolation algorithm should therefore be favoured, as it provides the most accurate results and very smooth contours.

If the amount of data is very large and the measurement data is very unevenly distributed, area interpolation with an integrated Gaussian algorithm is the best choice.

 

Batch File Interpolation