Hi list,
the Cluster strategy decides whether two features should be grouped in a
cluster and uses the relative distance of the features to do so. Nearby
features are clustered, and features apart from each other are left
untouched.
In a recent project we had the need to extend this behaviour, so that
the Cluster strategy should look at the attributes of the features too,
when deciding on clustering: only nearby features that shared an
attribute should be clustered, otherwise, they should be left alone.
Imagine a situation where you have features with an attribute "klasse"
that groups the features attributively. In the clustered map you only
want Clusters of the same "klasse".
In our case we could solve it using different layers (requested
according to their "klasse") and each had their own Cluster-strategy.
While we were refactoring that code a little bit we came up with the
idea of new Cluster-subtypes: the very simple
OpenLayers.Strategy.AttributeCluster and the more elaborate
OpenLayers.Strategy.RuleCluster:
OpenLayers.Strategy.AttributeCluster =
OpenLayers.Class(OpenLayers.Strategy.Cluster, {
/**
* the attribute to use for comparison
*/
attribute: null,
/**
* Method: shouldCluster
* Determine whether to include a feature in a given cluster.
*
* Parameters:
* cluster - {<OpenLayers.Feature.Vector>} A cluster.
* feature - {<OpenLayers.Feature.Vector>} A feature.
*
* Returns:
* {Boolean} The feature should be included in the cluster.
*/
shouldCluster: function(cluster, feature) {
var cc = cluster.geometry.getBounds().getCenterLonLat();
var fc = feature.geometry.getBounds().getCenterLonLat();
var distance = (
Math.sqrt(
Math.pow((cc.lon - fc.lon), 2) + Math.pow((cc.lat -
fc.lat), 2)
) / this.resolution
);
var cc_attrval = cluster.cluster[0].attributes[this.attribute];
var fc_attrval = feature.attributes[this.attribute];
return (distance <= this.distance && cc_attrval === fc_attrval);
},
CLASS_NAME: "OpenLayers.Strategy.AttributeCluster"
});
OpenLayers.Strategy.RuleCluster =
OpenLayers.Class(OpenLayers.Strategy.Cluster, {
/**
* the rule to use for comparison
*/
rule: null,
/**
* Method: shouldCluster
* Determine whether to include a feature in a given cluster.
*
* Parameters:
* cluster - {<OpenLayers.Feature.Vector>} A cluster.
* feature - {<OpenLayers.Feature.Vector>} A feature.
*
* Returns:
* {Boolean} The feature should be included in the cluster.
*/
shouldCluster: function(cluster, feature) {
var cc = cluster.geometry.getBounds().getCenterLonLat();
var fc = feature.geometry.getBounds().getCenterLonLat();
var distance = (
Math.sqrt(
Math.pow((cc.lon - fc.lon), 2) + Math.pow((cc.lat -
fc.lat), 2)
) / this.resolution
);
return (distance <= this.distance &&
this.rule.evaluate(cluster.cluster[0]) && this.rule.evaluate(feature));
},
CLASS_NAME: "OpenLayers.Strategy.RuleCluster"
});
Usage Examples:
// cluster only features that have 'klasse' < 3
new OpenLayers.Layer.Vector('Vektorlayer 1', {
strategies: [new OpenLayers.Strategy.Fixed(), new
OpenLayers.Strategy.RuleCluster({
rule: new OpenLayers.Rule({
// a rule contains an optional filter
filter: new OpenLayers.Filter.Comparison({
type: OpenLayers.Filter.Comparison.LESS_THAN,
property: "klasse",
value: 3
})
})
})],
protocol: new OpenLayers.Protocol.HTTP({
url: "../data/data_001.json",
format: new OpenLayers.Format.GeoJSON()
})
});
// cluster only features that are nearby and have the same "klasse"
new OpenLayers.Layer.Vector('Vektorlayer 2', {
strategies: [new OpenLayers.Strategy.Fixed(), new
OpenLayers.Strategy.AttributeCluster({
rule: new OpenLayers.Rule({
attribute: 'klasse'
})
})],
protocol: new OpenLayers.Protocol.HTTP({
url: "../data/data_002.json",
format: new OpenLayers.Format.GeoJSON()
})
});
We are unsure whether this might be of interest to anybody. If so, we
would be happy to provide patches for OpenLayers. This code currently
has no tests, and there is room for optimization (the comparison of
attributes is probably faster computed than the distance e.g.)
Please share your thoughts on this and tell us of alternatives we may
have missed, or drawbacks of the outlined approach.
Regards,
Marc and Thorsten
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