hierarchy polygons

3 min read 19-10-2024

Hierarchy Polygons: A Visual Guide to Complex Data Relationships

Hierarchy polygons, also known as treemaps, are a powerful visualization tool for representing hierarchical data in a visually intuitive way. They excel at showing the relative size and proportion of different parts within a whole, making them ideal for displaying information like:

Financial data: Breakdown of revenue by product line, region, or customer segment
Website analytics: User engagement by page, feature, or campaign
Organizational structures: Departments, teams, and individual roles
File systems: Folder and file sizes
Biological classifications: Taxonomies and phylogenetic trees

How Hierarchy Polygons Work

Imagine a rectangular area representing the whole dataset. This rectangle is then divided into smaller rectangles, each representing a component of the hierarchy. The area of each smaller rectangle corresponds to the size or value of its respective component.

For example, in a financial breakdown, the main rectangle could represent total revenue, while smaller rectangles within it represent revenue by product category. The size of each product category rectangle would reflect its share of total revenue.

Benefits of Using Hierarchy Polygons

Clear and concise: They present complex data in a simple and digestible form, making it easy to grasp the overall structure and relative importance of different elements.
Space-efficient: They effectively utilize space, allowing the display of large datasets within a limited area.
Visually appealing: The use of color and shape variation can enhance the visual appeal and make the data more engaging.
Interactive: Hierarchy polygons can be interactive, allowing users to drill down into specific components for further analysis.

Practical Examples:

1. Website Analytics

A hierarchy polygon can be used to visualize website traffic data, showing how users navigate different sections of the website. The main rectangle represents total website traffic, and smaller rectangles represent different pages or sections. The size of each rectangle corresponds to the number of visits to that specific page or section. This allows website owners to identify popular areas and pinpoint areas that need improvement.

2. Budget Allocation

A hierarchy polygon can effectively show the allocation of a budget across different departments or projects. The main rectangle represents the total budget, and smaller rectangles represent the budget allocated to different categories. This allows stakeholders to understand the proportion of resources dedicated to each area and identify potential areas for optimization.

Creating Hierarchy Polygons

Several tools can be used to create hierarchy polygons, including:

Software: Microsoft Excel, Google Sheets, Tableau, Power BI, and D3.js.
Libraries: D3.js, Plotly.js, and ggplot2 (R).

Q&A from GitHub:

Q: Are there any limitations to using hierarchy polygons? (Source: GitHub issue on D3.js)

A: While powerful, hierarchy polygons are not suitable for all types of data. They are best for representing hierarchical data with a single numerical value, making it challenging to display multiple dimensions or complex relationships.

Q: How can I make my hierarchy polygon more visually appealing? (Source: GitHub discussion on Plotly.js)

A: Consider using color gradients, different shades of the same color, or unique colors for each level of the hierarchy. You can also experiment with different layouts and orientations to create a more visually engaging representation.

Q: Can I use hierarchy polygons to display time-series data? (Source: GitHub discussion on Tableau)

A: While not directly suited for time-series data, you can create a hierarchy polygon where the main rectangle represents the overall time period, and sub-rectangles represent different time intervals. This allows you to visualize trends over time while still maintaining the hierarchical structure.

Conclusion

Hierarchy polygons are a versatile visualization technique for effectively representing hierarchical data. They are highly intuitive, visually appealing, and can be used to gain valuable insights from complex datasets. By considering their limitations and exploring different tools and techniques, you can leverage hierarchy polygons to create compelling and informative data visualizations.

hierarchy polygons