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# 17.30. Interpolation and contouring

Module contributed by Paolo Cavallini - Faunalia

Megjegyzés

This chapter shows how to use different backends to calculate different interpolations.

## 17.30.1. Interpolation

The project shows a gradient in rainfall, from south to north. Let’s use different methods for interpolation, all based on vector `points.shp`, parameter `RAIN`:

Figyelem

Set cell size to 500 for all analyses.

• GRASS ► v.surf.rst

• SAGA ► Multilevel B-Spline Interpolation

• SAGA ► Inverse Distance Weighted [Inverse distance to a power; Power: 4; Search radius: Global; Search range: all points]

• GDAL ► Grid (Inverse Distance to a power) [Power:4]

• GDAL ► Grid (Moving average) [Radius1&2: 50000]

Then measure variation among methods and correlate it with distance to points:

• GRASS ► r.series [Unselect Propagate NULLs, Aggregate operation: stddev]

• GRASS ► v.to.rast.value on `points.shp`

• GDAL ► Proximity

• GRASS ► r.covar to show the correlation matrix; check the significance of the correlation e.g. with http://vassarstats.net/rsig.html.

Thus, areas far from points will have less accurate interpolation.

## 17.30.2. Contour

Various methods to draw contour lines [always step= 10] on the stddev raster:

• GRASS ► r.contour.step

• GDAL ► Contour

• SAGA ► Contour lines from grid [NB: in some older SAGA versions, output shp is not valid, known bug]