High density data

Point clusters visualize and summarize overlapping points in an intuitive, concise way. Use the swipe widget to compare an unclustered layer of power plants with a clustered version.

High density data

Large, dense datasets are difficult to visualize well. These datasets typically involve overlapping features, which makes it difficult or even impossible to see spatial patterns in raw data. The following topics demonstrate various ways you can visualize high density data in a more meaningful way.

Client-side techniques

The following topics describe how you can visualize high density data client-side. These are ideal for dense datasets where all features can be loaded to the browser.

Clustering

Learn how to visualize high density point data using clusters.

Heatmap

Learn how to visualize high density point data as a continuous heatmap surface.

Opacity

Learn how to visualize high density data using opacity.

Bloom

Learn how to visualize high density data using a bloom layer effect.

Server-side techniques

The following topics describe techniques ideal for representing very large layers that cannot be reliably loaded to the browser. These should also be considered for reducing the size of datasets that need to be viewed on mobile devices. Note that the techniques mentioned here may be used in combination with the techniques mentioned above.

Aggregation

Learn how to visualize high density data by aggregating points to polygons.

Thinning

Learn how to reduce the number of features in the view by thinning data from very large layers.

Visible scale range

Learn how to leverage visible scale ranges in layers to avoid downloading too many features at small scales.

API support

The following table describes the geometry and view types that are suited well for each visualization technique.

2D3DPointsLinesPolygonsMeshClient-sideServer-side
Clustering
Heatmap
Opacity
Bloom
Aggregation
Thinning11123
Visible scale range
Full supportPartial supportNo support
  • 1. Feature reduction selection not supported
  • 2. Only by feature reduction selection
  • 3. Only by scale-driven filter

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