Aggregate features into clusters

Use clustering to dynamically aggregate point features that are geographically close to each other into single symbols to visually reveal useful patterns of information. You can also use it to avoid visually overlapping data.

If the data is in a point feature layer, you can aggregate the point features in the layer into clusters.

Feature clustering

A cluster is a symbol that represents two or more point features. Feature clustering aggregates point features into clusters. In most cases, a cluster's symbol also displays a value indicating the number of point features it represents. When the number of features a cluster includes increases, the size or shape of the cluster symbol proportionally increases as well.

When you aggregate point features into clusters, the layer redraws and groups point features within a specified distance of one another on the map into one cluster. The specified distance, which is called the clustering radius, is the approximate distance a point feature must be to another point feature to be aggregated into a cluster. If a point feature is not within a cluster's specified radius, it is not aggregated into a cluster.

An example of one large cluster, a few small clusters, and two unclustered features on a map
In this feature clustering example, the largest cluster contains 119 features. Two features on the left remain unclustered.

Clustering is used to simplify the symbology of a complex layer of cluttered points. Unique to feature clustering, the symbols have size, color, and text components, so they can visually display more than one variable from the data. Clustering can show patterns in the data that are difficult to visualize when a layer contains hundreds or thousands of points.

Examples of clustering include the following:

  • Cluster a layer of geocoded student addresses to view the areas where most students live without visualizing their specific address.
  • Categorize species into groups with unique values symbology clusters to view the most typically dominant trait in a national reserve.
  • Visualize the most common value (mode) in a dataset of traffic incidents in a city to see at what time of day accidents are most prevalent.

Aggregate point features into clusters

ArcGIS Pro provides two dynamic aggregation methods for point data: feature binning and feature clustering. Both methods achieve similar goals but are visually and behaviorally different.

Consider aggregating point features into clusters to see trends in the location and arrangement of features. With feature clustering, the clusters update dynamically depending on the map's scale and extent. Clusters support additional symbology types, such as unique values, unclassed colors, and proportional symbology.

Feature binning obscures much of the map while clustering allows other features or the basemap to remain partially visible. If a point feature is not clustered, it continues to be drawn as a singular point feature. With feature binning, a single point is always drawn as a bin. Feature binning is more faithful to the feature location than clustering. Clusters might dynamically change location depending on the centroid of their represented features, so the exact location of individual features in a cluster is not represented.

Using heat map symbology to show densely populated features is another way to visualize dense point information. Clustering features may better represent the data for sparsely distributed groups of points, and may be preferable for multiscale maps in which the level of detail frequently changes or requires insets.

Enable clustering on a feature layer

Feature clustering is available for use with any point feature layer in a map. Under Feature Layer, on the Appearance tab, in the Drawing group, click the Aggregation drop-down menu Aggregation and choose Clustering Clustering.

If feature binning is enabled on the layer, you can dynamically switch between feature clustering and feature binning from the Aggregation drop-down menu.


Clustering point features based on their z-values is not supported.

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