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Size of Input Data Set
The size of your input data set should be considered when selecting a gridding method.
For example, some gridding methods interpret small data sets more effectively than others
do.
In general:
- Ten or fewer points are not enough to define more than a general trend in your data.
- Triangulation with Linear Interpolation is not effective with few points. As with most
data sets, Kriging and Radial Basis Function methods will produce the best representation
of your data in this situation.
- If you want only to define the trend of the data, you can
use Polynomial Regression. With 10 or fewer points, gridding is extremely fast, so you
might want to try the different methods to determine the most effective method for your
data.
- With small data sets (<250 observations), Kriging with a linear variogram,
or Radial Basis Function using a multiquadratic function produce good representations of
most data sets.
- With moderate-sized data sets (from 250 to 1000 observations), Triangulation with Linear
Interpolation is fast and creates a good representation of your data. Although Kriging or
Radial Basis Function generate the grids more slowly, they also produce good data
representations.
- For large data sets (>1000 observations), both Minimum Curvature and Triangulation
with Linear Interpolation are quite fast, and both produce good representations. As with
most other data sets, Kriging or Radial Basis Function probably produce the best maps but
are quite a bit slower.
- Using Kriging or Radial Basis Function with large data sets does not result in
significantly different gridding times. For example, if your data file contains 3,000 or
30,000 data points, the gridding time is not significantly different. Either data set
might take a considerable amount of time to grid, but they take approximately the same
amount of time.
TIP: The software
comes with extensive information on each of the gridding techniques, as well
as information on the more advanced topics such as variogram modeling
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