functions changing the marker size, shape, color, etc.
(Figure 16). You can get really sophisticated here,
setting up rules or even plotting vectors.
While we could use QGIS to examine our altitude,
speed, etc., we are really interested in the
temperatures and colors we collected. Unfortunately,
seeing the values as these discrete points is not very
intuitive. We need to interpolate them onto a regularly
Gridding and Plotting
To grid our data, QGIS expects the data to be in a
format that can be processed by a set of tools called
OGR ( www.gdal.org). A shape file will work, and
QGIS can easily create one for us. Right-click the layer
we created in the layers panel (named DATAXXX) and
select “Save As...”. Using the browse button in the
save dialog, select somewhere for the file to live, and
give it a name. I used DATAXXX_SHP (Figure 17).
Click “OK and you’ll see the new layer in the layers
To grid our data, head up to the Raster menu and
locate the grid function (Raster -> Analysis -> Grid
(Interpolate)...). In the pop-up, select the shape file we just
saved as the input file. Check the “Z Field” box and select
the data value you want to grid up. It could be anything,
but I’ll use the object temperature in this example.
Name the output file; I used the convention
DATAXXX_FIELDNAME_GRIDDED. Click the “Algorithm”
checkbox and select “Inverse distance to a power” (Figure
18). This is the method QGIS will use to perform the
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Figure 17: We need to export the data as a shape file so that the
gridding tool can read and manipulate it.
Figure 18: The gridding and interpolation tool will try to
interpolate our point data to a regular grid so that shaded
images can be made. Again, there are a lot of settings in
here to play with and help tune the algorithm to produce the
best results for your data.
Figure 19: The default gray
color table is a bit boring, but
there are plenty to choose
from! The YlOrRd color table
gives the perception of
increasing temperature in an