Data can be summarized within irregular polygon boundaries. Aggregating points to a polygon layer allows you to represent the data in a performant way.Ģ. Some point datasets are so large they cannot reasonably be loaded to the browser and visualized with good performance. The point dataset is too large to cluster client-side. There are a couple of scenarios where this technique can be more effective than others:ġ. This is typically done by summarizing points within polygons where each polygon visualizes the number of points contained in the polygon. Brighter areas indicate areas with more frequent earthquakes.Īggregation allows you to summarize (or aggregate) large datasets with many features to layers with fewer features. This map uses bloom to show the prevalence of earthquakes in certain areas of the world. These topics are grouped by client-side and server-side approaches.Įach page provides a brief definition of the technique, describes why and when you should use it, and steps through two or more live examples that demonstrate how to practically use the technique. The High density data visualization guide outlines seven approaches for visualizing large amounts of data. These improvements prompted the need to highlight appropriate and effective ways to make sense of large, dense datasets. Thanks to major performance improvements over the last few years, the JS API can now render hundreds of thousands (even millions) of features with fast performance. These datasets typically involve overlapping features, which make it difficult or even impossible to see spatial patterns in raw data. Large, dense datasets are difficult to visualize well. You should consult these pages when working with large datasets containing many overlapping features. "High density data" is a new sub-chapter in the Visualization guide of the ArcGIS API for JavaScript.
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