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Data Visualization

Data Visualization defines the semantic contract for representing analytical meaning across charts, dashboards, and geospatial overlays.

It extends Design Tokens v2 with a small, explicit layer for data-specific semantics while preserving the same system architecture:

  • core tokens define raw values
  • semantic tokens define meaning
  • components and patterns consume semantic tokens only

This category exists to standardize recurring analytical meaning without coupling the system to chart types, rendering libraries, or map providers.

Purpose​

The purpose of this category is to make analytical meaning durable, reusable, and governable.

It defines semantic roles for:

  • categorical identity
  • ordered magnitude
  • midpoint comparison
  • analytical references
  • contextual emphasis
  • data status
  • non-color analytical encodings
  • minimal geospatial overlay semantics

The goal is not to define charts.

The goal is to define the stable meaning that charts, dashboards, and overlays express.

Scope​

Data Visualization governs:

  • semantic color roles for analytical data
  • non-color encodings that reinforce analytical meaning
  • minimal geospatial semantics for thematic overlays

Data Visualization does not govern:

  • chart type selection
  • chart-specific configuration
  • visualization libraries or rendering APIs
  • multi-view composition
  • tooltip behavior
  • labeling strategies
  • statistical transformations
  • basemap design systems
  • map projection, tiling, or generalization logic

These concerns belong to the pattern and implementation layers.

Principles​

Semantic-first​

Tokens express analytical meaning, not stylistic preference.

Task-first​

The system models analytical roles, not chart types.

Small and explicit​

Only stable and recurring concepts are encoded.

Accessible by design​

Meaning must not depend on color alone.

Overlay-first for geospatial​

Geospatial support focuses on thematic overlays, not full cartographic systems.

Clear boundaries​

Tokens govern meaning.
Patterns and implementations govern composition and behavior.

Architecture​

Data Visualization follows the same architectural model as the rest of the system:

core.foundation → semantic.foundation
core.dataviz    → semantic.dataviz
patterns/specs  → consume semantic only

Foundation reuse​

Data Visualization reuses the existing foundation wherever the problem is already solved, including:

  • typography
  • spacing
  • sizing
  • borders
  • radii
  • elevation
  • z-index
  • motion
  • global opacity

Core extension​

Data Visualization introduces new core tokens only where the problem is unique to analytical visualization:

  • analytical color palettes
  • non-color encoding primitives

This keeps the category small and prevents parallel foundations.

Semantic Surfaces​

Colors​

dataviz.color.* defines the semantic roles of color in analytical contexts.

It governs:

  • series identity
  • sequential scales
  • diverging scales
  • analytical references
  • analytical states
  • data status

This is the primary semantic API of the category.

Encodings​

dataviz.encoding.* defines non-color channels used to reinforce meaning.

It governs:

  • shape
  • pattern
  • stroke style
  • opacity semantics

Encodings exist to improve accessibility, robustness, and perceptual clarity.

Geospatial overlays​

Geospatial overlays are part of Data Visualization.

They do not introduce a parallel color or encoding system.

Instead:

  • overlays use dataviz.color.* for analytical color meaning
  • overlays use dataviz.encoding.* for non-color reinforcement
  • geospatial semantics define only the relationship between the overlay and spatial context

The geospatial contract is therefore contextual, not a separate visual language.

What This Category Does Not Do​

This category intentionally does not attempt to:

  • model every visualization technique
  • encode chart-specific semantics
  • replace chart or map libraries
  • define dashboard composition rules
  • define complete cartographic systems

Its job is to provide a stable semantic layer for recurring analytical meaning.

Consumption Model​

Components and patterns must consume semantic dataviz tokens only.

They must never:

  • consume core.dataviz directly
  • create chart-type tokens in product code
  • introduce local analytical vocabularies that bypass the contract

If a need cannot be expressed by existing semantics, it must be handled through:

  1. existing tokens
  2. pattern-level composition
  3. deliberate governance of new semantics

Relationship to the Core Color System​

Data Visualization colors are distinct from UI semantic colors.

  • UI colors express interface meaning
  • Data Visualization colors express analytical meaning

These systems must remain separate.

A negative action color is not the same thing as a negative analytical scale value. A highlighted chart series is not the same thing as a highlighted button state.

Relationship to Patterns​

Patterns are responsible for applying Data Visualization semantics to real visualizations.

They decide:

  • which chart type to use
  • which encoding strategy is appropriate
  • whether scales are shared or independent
  • whether meaning is reinforced through labels, legends, or annotations
  • how maps behave across zoom levels

Data Visualization tokens provide the semantic building blocks. Patterns turn those blocks into complete visualization behavior.

Validation​

Family-specific checks such as sequential order, diverging balance, bounded pattern sets, or detailed geospatial legibility belong in their own documents.

Errors (validation must fail when)​

  • Data Visualization introduces chart-type, component, library, provider, or map-style semantics in the semantic token surface

  • analytical opacity does not stay separate from foundation opacity:

    • dataviz.encoding.opacity.* resolves outside core.dataviz.opacity.*
    • foundation opacity tokens are used to encode analytical meaning

Warning (validation should warn when)​

  • dataviz.geo.state.focus and dataviz.geo.state.selection resolve to the same effective value

Summary​

Data Visualization is a minimal semantic extension for analytical meaning.

It exists to make charts, dashboards, and geospatial overlays more:

  • consistent
  • accessible
  • scalable
  • and semantically durable

It does this by defining a small public API for analytical meaning while keeping chart behavior, composition, and rendering outside the token layer.