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.
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.
Learn how to visualize high density point data using clusters.
Learn how to visualize high density point data as a continuous heatmap surface.
Learn how to visualize high density data using opacity.
Learn how to visualize high density data using a bloom layer effect.
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.
Learn how to visualize high density data by aggregating points to polygons.
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.
The following table describes the geometry and view types that are suited well for each visualization technique.
|Visible scale range|
- 1. Feature reduction selection not supported
- 2. Only by feature reduction selection
- 3. Only by scale-driven filter