# Gridding Method Comparison

It is recommended that you try each of the different gridding
methods, accepting the defaults, in much the same fashion as you have
seen here. This gives you a way to determine the best gridding method
to use with the same data set.

In this example, we will use the Demogrid.dat
file located in the Samples folder. Each image shows the contour and post
map combination created from the default settings for each of the various
gridding methods.

This is a comparison of the different gridding methods. For these examples,
the sample file, Demogrid.dat,
was used. All the defaults for the various methods were accepted. This
data set contains 47 data points, irregularly spaced over the extent of
the map. The data point locations are displayed as a post map layer.

The Results of Each Gridding Method with
Demogrid.dat:

## Smooth Appearance

Kriging,
Minimum Curvature,
Natural Neighbor,
and Radial
Basis Function all produced acceptable contour maps with smooth
appearance.

## Bulls Eye Pattern

Inverse
Distance to a Power and Modified Shepard's Method
both tended to generate "bull's eye" patterns.

## Triangular Facets

With Triangulation
with Linear Interpolation, there are too few data points to
generate an acceptable map, and this explains the triangular facets apparent
on the contour map.

## Blocky

Nearest
Neighbor shows as a "blocky" map because the data
set is not regularly spaced and therefore a poor candidate for this method.

## Tilted Plane

Polynomial
Regression shows the trend of the surface, represented as a
tilted plane.

## Discontinuities

Due to the small number of data in Demogrid.dat,
the Moving
Average method is not applicable. The results of using this
method with an inadequate data set are shown as discontinuities are created
as data are captured and discarded by the moving search neighborhoods.

## Median Distance

Data
Metrics can show many different types of information about
the data and about the gridding process, depending on which metric is
selected. This example shows the median distance between each grid node
and the original 47 data points.

## Smooth Local Variation

Local
Polynomial models smooth local variation in the data set.

See Also

Gridding
Methods

Choosing
Methods Based on the Number of XYZ Data Points

Creating
a Grid File from an XYZ Data File

Exact
and Smoothing Interpolators

General
Gridding Recommendation