Apply Conditional Styling Based on Data Values

The most effective map visualizations use conditional styling that transforms data attributes into visual properties, enabling viewers to understand information through colors, sizes, and symbols that communicate meaning at a glance.

If your maps display all features identically regardless of their attributes, using uniform colors and sizes that don't reflect underlying data, you're missing the communication power that data-driven styling provides. That's why analysts ask: can we apply conditional styling based on data values so map appearance communicates attribute information visually without requiring users to click and inspect every feature?

With Atlas, you can create data-driven styling that automatically adjusts feature appearance based on attribute values. No manual categorization, no custom styling code, no barriers to creating maps where appearance communicates data. Everything starts with your data and conditional rules that transform attributes into visual properties.

Here's how to set it up step by step.

Why Applying Conditional Styling Matters for Data Communication

Creating data-driven styling enables better information communication and more effective visual analysis across datasets with meaningful attribute variation.

So applying conditional styling isn't just visual refinement—it's essential technique that transforms how effectively maps communicate attribute information to viewers.

Step 1: Identify Styling Requirements and Data Attributes

Atlas makes it easy to plan conditional styling with clear communication goals:

Review available attributes understanding what data fields exist that could drive styling
Define communication priorities determining which attributes most need visual communication
Choose styling properties deciding whether color, size, symbols, or combinations best serve your goals
Plan visual hierarchy establishing which styled attributes should be most prominent
Consider audience interpretation ensuring styling choices will be intuitive for your viewers

Once identified, your styling requirements guide configuration that communicates data effectively.

Step 2: Configure Conditional Color Styling

Next, set up data-driven colors within your layer settings:

You can configure different color styling approaches:

Category colors assigning distinct colors to different categorical values
Graduated colors applying color gradients based on numeric value ranges
Color ramps using smooth color progressions for continuous numeric data
Custom color assignments specifying exact colors for specific values
Conditional rules setting colors based on logical conditions and formulas
Multi-attribute styling combining multiple data fields to determine color

Each approach communicates different types of data through color encoding.

Step 3: Apply Size-Based Conditional Styling

To communicate data through feature size:

Configure size scaling setting marker or symbol sizes based on numeric values
Define size ranges establishing minimum and maximum sizes for visual clarity
Scale proportionally sizing features relative to attribute magnitudes
Set size categories grouping values into discrete size classes
Combine with color using both size and color to communicate multiple attributes

Size styling adds another dimension of data communication to your visualization.

Step 4: Create Symbol-Based Conditional Styling

To communicate data through icon and symbol variation:

Assign icons by category using different symbols for different feature types
Configure conditional icons switching symbols based on data values
Combine icons with colors using both symbol shape and color for rich encoding
Design icon hierarchies establishing visual importance through symbol choices
Consider icon clarity ensuring symbols remain distinguishable at various zoom levels

Symbol styling enables communication of categorical data through recognizable visual shapes.

Step 5: Build Complex Conditional Rules

To create sophisticated data-driven styling:

Write conditional formulas creating rules that evaluate multiple attributes and conditions
Nest conditions building complex logic that handles various data scenarios
Set fallback styles defining default appearance when conditions aren't met
Test edge cases verifying styling handles unusual or missing values appropriately
Document rule logic recording why styling rules are configured as they are

Also read: Complete Guide to Map Visualization and Data Styling

Step 6: Validate and Refine Conditional Styling

Now that conditional styling is configured:

Review visual results checking that styling accurately represents your data
Test with different data verifying styling works correctly across your full data range
Gather viewer feedback learning whether styling communicates effectively to audiences
Refine color choices adjusting colors based on viewing conditions and feedback
Update styling as data changes maintaining accurate representation as data evolves

Your conditional styling becomes part of comprehensive visualization that communicates data through intentional visual design.

Also read: Customize Map Legends for Clear Data Communication

Use Cases

Applying conditional styling based on data values is useful for:

Sales analysts coloring customers by revenue tier or status to see value distribution geographically
Asset managers styling equipment by condition status to identify at-risk infrastructure visually
Real estate analysts sizing property markers by price or square footage
Demographic analysts coloring regions by population characteristics for thematic mapping
Project managers styling tasks by priority or status to see project geography

It's essential for anyone who wants maps to communicate data attributes through visual appearance rather than requiring feature inspection.

Tips

Match colors to meaning using intuitive color associations (red for issues, green for positive)
Limit categories avoiding too many colors or sizes that overwhelm viewers
Ensure contrast making styled differences visible and distinguishable
Add legends helping viewers understand what styling represents
Test accessibility verifying styling works for colorblind viewers

Applying conditional styling in Atlas enables data-driven visualization without custom coding.

No styling scripts needed. Just configure conditional rules and let data drive visual appearance automatically.

Data-Driven Styling with Atlas

Effective maps communicate through appearance. Conditional styling transforms data attributes into colors, sizes, and symbols that viewers understand instantly without inspecting individual features.

Atlas helps you turn data attributes into visual properties: one platform for conditional rules, color gradients, and data-driven appearance.

Transform Data Attributes into Visual Communication

You can:

Configure colors that change based on categorical or numeric attribute values
Apply size scaling that communicates magnitude through marker dimensions
Create conditional rules that handle complex styling logic automatically

Also read: Build Heatmaps to Show Density and Concentration Patterns

Build Visualizations That Tell Stories

Atlas lets you:

Combine multiple styling properties for rich data communication
Update styling automatically as data values change
Create professional maps where every visual choice serves communication purpose

That means no more uniform styling that hides data variation, and no more clicking features to understand what they represent.

Discover Better Communication Through Conditional Styling

Whether you're visualizing status, value, category, or any attribute data, Atlas helps you turn data fields into visual properties that communicate instantly.

It's conditional styling—designed for data-driven visualization and effective communication.

Style Your Data with the Right Tools

Data visualization is powerful, but styling configuration can be complex. Whether you're setting colors, scaling sizes, assigning symbols, or building rules—data-driven styling matters.

Atlas gives you both flexibility and simplicity.

In this article, we covered how to apply conditional styling based on data values, but that's just one of many ways Atlas helps you visualize.

From color configuration to size scaling, symbol assignment, and complex rules, Atlas makes data-driven styling accessible and effective. All from your browser. No styling code needed.

So whether you're styling your first data-driven map or building sophisticated conditional visualization, Atlas helps you move from "uniform appearance" to "data-driven styling" faster.