This sample demonstrates how to represent clusters as pie charts. This is done by calling the createRendererForClustering method in the pieChart smart mapping module.
This method returns a list of aggregate fields and a renderer. Both of these objects must be set on the FeatureReductionCluster object for the charts to properly render.
Configure clustering
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async function generateClusterConfig(layer){
// generates default labelingInfo
const { labelingInfo, clusterMinSize } = await clusterLabelCreator
.getLabelSchemes({ layer, view })
.then(labelSchemes => labelSchemes.primaryScheme );
const labelSymbol = labelingInfo[0].symbol;
labelSymbol.color = labelSymbol.haloColor.clone();
labelSymbol.haloColor = [255,255,255,0.3];
labelSymbol.font.size = 10;
const { renderer, fields } = await pieChartRendererCreator
.createRendererForClustering({
layer,
shape: "donut"
});
renderer.holePercentage = 0.66;
const fieldInfos = fields.map( field => {
return {
fieldName: field.name,
label: field.alias,
format: {
places: 0,
digitSeparator: true
}
}
});
// maps the field names for the popup chart
const fieldNames = fieldInfos.map(field => {
return field.fieldName
});
const popupTemplate = {
content: [{
type: "text",
text: "This cluster represents <b>{cluster_count}</b> features."
},
{
type: "media",
mediaInfos: [{
title: "311 Reports",
type: "pie-chart",
value: {
fields: fieldNames
}
}]
},
{
type: "fields"
}],
fieldInfos
};
return {
type: "cluster",
popupTemplate,
labelingInfo,
clusterMinSize,
fields,
renderer
};
}
Clustering is enabled via the featureReduction property of the FeatureLayer.
Set generated cluster configuration
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layer.when()
.then(generateClusterConfig)
.then((featureReduction) => {
layer.featureReduction = featureReduction;
Clustering is a method of aggregating points in a FeatureLayer, CSVLayer, GeoJSONLayer, WFSLayer, or OGCFeatureLayer by grouping them in clusters defined by screen space.
