Bubble Map
Chart overview
Bubble maps combine geographic mapping with proportional symbols to display quantitative data across locations.
Key points
- Each bubble is positioned at specific coordinates and sized according to the value it represents.
- This visualization is excellent for showing regional comparisons, identifying geographic clusters, and communicating location-based metrics like sales by city or population by region.
Example Visualization
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Generate publication-ready bubble maps with AI in seconds. No coding required – just describe your data and let AI do the work.
View example prompt
"Create a bubble map of the United States displaying 'Regional Sales Performance' by major metropolitan area. Generate data for 15+ cities including New York, Los Angeles, Chicago, Houston, Phoenix, etc. Size bubbles proportionally to 'Annual Sales Volume' ($500K to $50M range) using sqrt scaling. Color bubbles by 'Year-over-Year Growth' using a diverging colorscale (red for negative, green for positive). Show city names and include a legend for both size and color."
How to create this chart in 30 seconds
Upload Data
Drag & drop your Excel or CSV file. Plotivy securely processes it in your browser.
AI Generation
Our AI analyzes your data and generates the Bubble Map code automatically.
Customize & Export
Tweak the design with natural language, then export as high-res PNG, SVG or PDF.
Python Code Example
Console Output
Total cities: 15 Total sales: $330M Average growth: 3.8%
Common Use Cases
- 1Visualizing sales performance by location
- 2Showing population distribution across cities
- 3Mapping earthquake magnitudes
- 4Displaying store locations with revenue
Pro Tips
Use sqrt scaling for bubble sizes to prevent large values from dominating
Include both size and color legends for multi-variable encoding
Set appropriate map boundaries for your region
Add city labels for major bubbles to improve readability
Scientific Chart Selection Cheat Sheet
Not sure whether to use a Violin Plot, Box Plot, or Ridge Plot? Download our single-page reference mapping the most-used scientific chart types, exactly when to use them, and the core Matplotlib/Seaborn functions.