User guide

Choosing an Interpolation Technique
26 Vertical Mapper 3.7
2. How accurate is the data?
Some techniques assume that the value at every data point is an exact value and will honour it
when interpolating. Other techniques assume that the value is more representative of an area.
3. What does the distribution of the points look like?
Some interpolation techniques produce more reasonable surfaces when the distribution of points
is truly random. Other techniques work better with point data that is regularly distributed.
Data Type Possible Interpolation
Elevation Triangular Irregular Network (TIN), Natural Neighbour (NN)
Soil Chemistry Inverse Distance Weighting (IDW), Kriging
Demographic NN, IDW, Kriging
Drive Test NN
Point Value Accuracy Possible Interpolation Technique
Very Accurate NN, TIN, Rectangular
Not Very Accurate IDW, Kriging
Point Distribution Possible Interpolation Technique
Most interpolation techniques work well with randomly scattered
data points.
NN, TIN, IDW, Kriging
Highly clustered data presents problems for many interpolation
techniques.
NN, IDW, Kriging
TIN – for slightly clustered data points