Pitney Bowes MapInfo Vertical Mapper User Manual
Page 28
Choosing an Interpolation Technique
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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