Census tracts styled with a dot density renderer to show level of educational attainment
Data-driven stylesData-driven styles answer questions about your data, such as Where? What? How much? When? or a combination of those questions. A layer's style is configured with its renderer.
A renderer defines how symbols are applied to each feature. In the case of a data-driven visualization, the symbol is always determined based on data (or an attribute value) returned from one of two data sources:
valueExpression property of a renderer or visual variable.The renderer will match the values returned from these sources to a predefined symbol used to represent each feature, or use the data value to override one of the symbol's properties, such as size or color.
Unique typesLearn how to visualize data by categories.
Class breaksLearn how to classify data by discrete numeric ranges.
Visual variablesLearn how to communicate potential relationships between two or more data attributes using multiple visual variables.
TimeLearn how to visualize dates as a timeline or an age relative to another date.
MultivariateLearn how to visualize two or more data attributes with the same renderer.
PredominanceLearn how to visualize the predominant value among a set of competing attributes (or subcategories).
Dot densityLearn how to visualize the density of subcategories of a count or population.
RelationshipLearn how to visualize the potential relationship between two numeric attributes using a blend of two color ramps.
Smart mappingLearn how to take advantage of Smart Mapping APIs to explore unfamiliar datasets.
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