Shows the usage of custom classes for a fine grained control about
the clustering behaviour.
The vectorlayer in this example contains random data with an
attribute "clazz" that can take the values 1, 2, 3 and 4. The
features with clazz = 4 are considered more important than the
others.
The radiobuttons on the right of the map control the
cluster strategy to be applied to the features.
-
No strategy
means that all features are
rendered, no clustering shall be applied
-
Simple cluster-strategy
applies the cluster
strategy with default options to the layer. You should notice
that many of the important features with clazz = 4 are getting
lost, since clustering happens regardless of feature attributes
-
Attributive cluster-strategy
uses a
customized cluster strategy. This strategy is configured to
cluster features of the same clazz only. You should be able to see all
red points (clazz = 4) even though the data is clustered. A
cluster now contains only features of the same clazz.
-
Rulebased cluster-strategy
uses another
customized cluster strategy. This strategy is configured to
cluster features that follow a certain rule only. In this case only
features with a clazz different from 4 are considered as
candidates for clustering. That means that usually you have fewer
clusters on the map, yet all with clazz = 4 are easily
distinguishable
Hover over the features to get a short infomation about the
feature or cluster of features.