(2) density of data: some cells could not be fitted to the 
required accuracy of fit for lack of sufficient data. This 
implies a need for differential density for different types 
of terrain for a required accuracy of fit and fixed cell size 
or a variable cell size for a fixed density of data. 
(3) Percentage overlap in relation to terrain type and density 
of data. This will have to be investigated further in order 
to establish their relationships. 
CONCLUSION 
The method of least squares polynomial surface fitting with overlapping 
data has been shown to be a working alternative in functional digital 
elevation modelling. However the actual amount of data overlap in percent 
is a function of many factors the main ones being terrain type, data 
density and accuracy of fit. The author is still working on this to 
establish these parameters and their relationships. 
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