10. Spatial Analysis: Raster Data

Duration: 8 minutes and 53 seconds

Country: South Africa

Language: English

License: CC - Attribution Non-commercial No Derivatives

Genre: Instructional

Producer: Chief Directorate: Spatial Planning & Information, Department of Land Affairs, Eastern Cape, South Africa

Views: 259 (17 embedded)

Posted by: giacomo on Mar 30, 2010

Understand interpolation as part of spatial analysis. Spatial interpolation is the process of using points with known values to estimate values at other unknown points. In this worksheet we describe interpolation in more detail and give some examples of how it can be useful.

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Hi my name is Sibongile.
Welcome to the tenth topic in our Gentle introduction to GIS series.
In this screencast, we will give you a basic introduction to the concept of ...
... spatial interpolation and show you a sample analysis tool.
Spatial Interpolation is the process of using points with known values ...
... to estimate other points with unknown values at other locations.
Spatial interpolation is useful if you need information for a large area ...
... as a surface map, but you only have a few points with information available.
Spatial interpolation tools are available as part of Geographical Information Systems.
As an example we will show you how interpolation can be used ...
... as a typical spatial analysis tool in the QGIS application.
Let's take a closer look!
Spatial interpolation in QGIS is provided by the Interpolation Plugin.
You can see the 'Interpolation' Icon in the menu here.
In this example we will interpolate an elevation surface from ...
... some spot heights which were collected with a GPS device.
A GPS device is a small computer that records position ...
... and height information as you move around.
First, we will load the vector layer 'elevation_p1000' ...
... containing some sample data.
To do this, click on the 'add vector layer' icon ...
... and choose the layer called 'elevation_p1000'.
Let's look at the attribute table for this layer.
To do this, click on the 'show table' icon.
As you remember from our topic on attribute data, ...
... the attributes of a vector layer describe the features.
In this case each point feature has a value attribute that ...
... describes how high the point is above sea level.
Now we can start the interpolation tool.
Click on the 'plugins' menu, ...
... then the 'Interpolation' submenu, ...
... then click the 'Interpolation' entry to open the dialog.
In the Interpolation dialog, select 'elevation_p1000' as the input vector layer.
Then select 'VALUE' as the 'Interpolation attribute' column.
Choose 'Inverse Distance Weighting' as the interpolation method.
Next, type 'one hundred' in the 'number of columns' field.
Then type '50' in the 'number of rows' field.
The interpolator will create a 'new Raster' layer that is one hundred pixels by 50 pixels.
Each pixel in the interpolated surface will have a resolution of ...
... about one hundred and seventy meters in the real world.
Finally, select a name. For example, 'elevationIDW' ...
... for the 'output file' and click 'Ok' to start the interpolation process.
This may take a few moments while the GIS analyses your data.
After the spatial interpolation is done, a new layer called Interpolation appears in the legend.
The layer is displayed as a grey area in the map view.
You now need to define a colour table ...
... for the 'new raster layer' in the 'layer properties dialog'.
Double click on the layer name in the legend to start the properties dialog.
In the 'Symbology' tab, select the value 'Pseudocolour' for the colour map ...
... and then click 'OK'.
You can now see the different interpolated values in the new map.
Finally, you can zoom closer into an interesting area of the map view ...
... and check some results of your interpolation analysis.
To do this, use the 'Identify feature' tool in the toolbar.
Make sure the Interpolation results layer is selected.
Then click on the map to see the estimated height values.
Each value represents an estimated height calculated ...
... by the Inverse Distance Weighting algorithm.
That brings us the end of this screencast.
This is the last screencast in this series.
We will be back with more soon!
See you next time!