Treemap

What is a treemap?

Treemaps allow users to grasp the relative size and structure of hierarchical data quickly. Instead of using traditional lists or tree diagrams, treemaps provide a space-filling approach, making them helpful in comparing categories and subcategories at a glance.

Structure of a treemap

A treemap consists of:

• Parent nodes: The top-level categories, represented as large rectangles.
• Child nodes: The subcategories or elements, nested inside their parent’s rectangle.
• Size encoding: The area of each rectangle corresponds to a numerical value (e.g., revenue, population, or file size).
• Color coding: Often used to represent another variable, such as growth rate, performance, or category type.

Example: In a corporate revenue treemap, each department is a parent node, and individual product sales are child nodes. The size of each rectangle represents the revenue contribution of each product.

Layout algorithms

Treemaps can be arranged using different algorithms, which determine how rectangles are proportionally divided:

• Squarified layout: Creates rectangles with aspect ratios close to a square for better readability.
• Slice-and-dice layout: Divides rectangles into uniform rows or columns, maintaining strict order.
• Strip layout: A variation of the slice-and-dice method but optimized for readability and balance.

Treemap examples and use cases

Treemaps are widely used in data visualization and analytics to efficiently represent complex hierarchical data in fields such as business, finance, and IT.

• Financial market analysis: Used to visualize stock market performance, where each rectangle represents a company, and size/color indicate market value or price change.
• File system analysis: Helps users understand disk space usage by showing which folders and files consume the most storage.
• Business and sales data: Provides insights into revenue distribution across different products or regions.
• E-commerce analytics: Shows product sales performance within different categories.
• Healthcare and genomics: Helps analyze hierarchical relationships in biological datasets (e.g., gene classifications).

Considerations for using treemaps in data visualization

While treemaps are powerful visualization tools, they come with certain limitations that machine learning teams should keep in mind to maximize the value of this data visualization practice. 

• Readability: If too many small rectangles are present, they can become difficult to interpret.
• Labeling challenges: Small sections may not have enough space for meaningful labels.
• Color interpretation: Poor color choices can lead to confusion, especially in datasets with multiple variables.
• Comparing non-adjacent rectangles: Since items are not always aligned, it can be difficult to make direct size comparisons.

Conclusion

Treemaps are an effective way to visualize hierarchical and proportional data using nested rectangles. 

They provide a compact and visually intuitive representation of datasets, making them ideal for applications in finance, business intelligence, IT storage, and analytics. 

While treemaps offer great insight into data distribution, careful consideration must be given to readability, color selection, and layout choices to maximize their clarity and effectiveness.