Labels are a good way to communicate information such as the names of individual places, but they can’t be used for everything. For example, let’s say that someone wants to know what each landuse area is used for. Using labels, you’d get this:
This makes the map’s labeling difficult to read and even overwhelming if there are numerous different landuse areas on the map.
The goal for this lesson: To learn how to classify vector data effectively.
You’ll see something like this:
Now our landuse polygons are appropriately colored and are classified so that areas with the same land use are the same color. You may wish to remove the black border from the landuse layer:
You’ll see that the landuse polygon outlines have been removed, leaving just our new fill colours for each categorisation.
Notice that there is one category that’s empty:
This empty category is used to color any objects which do not have a landuse value defined or which have a NULL value. It is important to keep this empty category so that areas with a NULL value are still represented on the map. You may like to change the color to more obviously represent a blank or NULL value.
Remember to save your map now so that you don’t lose all your hard-earned changes!
If you’re only following the basic-level content, use the knowledge you gained above to classify the buildings layer. Set the categorisation against the building column and use the Spectral color ramp.
Note
Remember to zoom into an urban area to see the results.
There are four types of classification: nominal, ordinal, interval and ratio.
In nominal classification, the categories that objects are classified into are name-based; they have no order. For example: town names, district codes, etc.
In ordinal classification, the categories are arranged in a certain order. For example, world cities are given a rank depending on their importance for world trade, travel, culture, etc.
In interval classification, the numbers are on a scale with positive, negative and zero values. For example: height above/below sea level, temperature above/below freezing (0 degrees Celsius), etc.
In ratio classification, the numbers are on a scale with only positive and zero values. For example: temperature above absolute zero (0 degrees Kelvin), distance from a point, the average amount of traffic on a given street per month, etc.
In the example above, we used nominal classification to assign each farm to the town that it is administered by. Now we will use ratio classification to classify the farms by area.
We’re going to reclassify the layer, so existing classes will be lost if not saved.
We want to classify the landuse areas by size, but there’s a problem: they don’t have a size field, so we’ll have to make one.
The new field will be added (at the far right of the table; you may need to scroll horizontally to see it). However, at the moment it is not populated, it just has a lot of NULL values.
To solve this problem, we’ll need to calculate the areas.
You’ll get this dialog:
Now your AREA field is populated with values (you may need to click the column header to refresh the data). Save the edits and click Ok.
Note
These areas are in degrees. Later, we will compute them in square meters.
You’ll be using this to denote area, with small areas as Color 1 and large areas as Color 2.
In the example, the result looks like this:
Now you’ll have something like this:
Leave everything else as-is.
It’s often useful to combine multiple criteria for a classification, but unfortunately normal classification only takes one attribute into account. That’s where rule-based classification comes in handy.
These filters are exclusive, in that they collectively exclude some areas on the map (i.e. those which are smaller that 0.00005, are not residential and are not ‘Swellendam‘). This means that the excluded polygons take the style of the default (no filter) category.
We know that the excluded polygons on our map cannot be residential areas, so give the default category a suitable pale green color.
Your dialog should now look like this:
Your map will look something like this:
Now you have a map with Swellendam the most prominent residential area and other non-residential areas colored according to their size.
Symbology allows us to represent the attributes of a layer in an easy-to-read way. It allows us as well as the map reader to understand the significance of features, using any relevant attributes that we choose. Depending on the problems you face, you’ll apply different classification techniques to solve them.
Now we have a nice-looking map, but how are we going to get it out of QGIS and into a format we can print out, or make into an image or PDF? That’s the topic of the next lesson!