International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B2. Istanbul 2004 
  
  
useful when data are already evenly spaced, but need 
to be converted to a SURFER grid file. Alternatively, 
in cases where the data are close to being on a grid, 
with only a few missing values, this method is 
effective for filing in the holes in the data. 
Sometimes with nearly complete grids of data, there 
are areas of missing data that you want to exclude 
from the grid file. In this case, you can set the Search 
Ellipse to a certain value, so the areas of no data are 
assigned the blanking value in the grid file. By 
setting the search ellipse radii to values less than the 
distance between data values in your file, the 
blanking value is assigned at all grid nodes where 
data values do not exist. 
2.7 The Polynomial Regression Method 
Polynomial Regression is used to define large-scale 
trends and patterns in your data. Polynomial 
Regression is not really an interpolator because it 
does not attempt to predict unknown Z values. There 
are several options you can use to define the type of 
trend surface. 
2.8 The Radial Basis Function Interpolation 
Method 
Radial Basis Function interpolation is a diverse 
group of data interpolation methods. In terms of the 
ability to fit your data and produce a smooth surface, 
the Multiquadric method is considered by many to 
be the best. All of the Radial Basis Function methods 
are exact interpolators, so they attempt to honor your 
data. You can introduce a smoothing factor to all the 
methods in an attempt to produce a smoother 
surface. 
2.9 The Triangulation with Linear Interpolation 
Method 
The Triangulation with Linear Interpolation method 
in SURFER uses the optimal Delaunay triangulation. 
This algorithm creates triangles by drawing lines 
780 
between data points. The original points are 
connected in such a way that no triangle edges are 
intersected by other triangles. The result is a 
patchwork of triangular faces over the extent of the 
grid. This method is an exact interpolator. Each 
triangle defines a plane over the grid nodes lying 
within the triangle, with the tilt and elevation of the 
triangle determined by the three original data points 
defining the triangle. All grid nodes within a given 
triangle are defined by the triangular surface. 
Because the original data are used to define the 
triangles, the data are honored very closely. 
Triangulation with Linear Interpolation works best 
when your data are evenly distributed over the grid 
area. Data sets containing sparse areas result in 
distinct triangular facets on the map. 
2.10 The Moving Average Method 
The Moving Average method assigns values to grid 
nodes by averaging the data within the grid node's 
search ellipse. To use Moving Average, a search 
ellipse must be defined and the minimum number of 
data to use, specified. For each grid node, the 
neighboring data are identified by centering the 
search ellipse on the node. The output grid node 
value is set equal to the arithmetic average of the 
identified neighboring data. If there are fewer, than 
the specified minimum number of data within the 
neighborhood, the grid node is blanked. 
2.11 The Data Metrics Methods 
The collection of data metrics methods creates grids 
of information about the data on a node-by-node 
basis. The data metrics methods are not, in general, 
weighted average interpolators of the Z-values. For 
example, you can obtain information such as: 
a) The number of data points used to interpolate each 
grid node. 
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