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.
| 2D | 3D | Points | Lines | Polygons | Mesh | Client-side | Server-side | |
|---|---|---|---|---|---|---|---|---|
| Clustering | ||||||||
| Binning | ||||||||
| Heatmap | ||||||||
| Opacity | ||||||||
| Bloom | ||||||||
| Aggregation | ||||||||
| Thinning | 1 | 1 | 1 | 2 | 3 | |||
| Visible scale range |
- 1. Feature reduction selection not supported
- 2. Only by feature reduction selection
- 3. Only by scale-driven filter