Sankey charts, named after Major Sankey, an engineer who first used them to visualize water flow in Victorian England, have evolved into powerful tools for displaying data flows across different categories. These charts, often referred to as Sankey diagrams or Sankey flow charts, are particularly useful for illustrating complex processes involving quantities and their transfers between different steps. They are a versatile method of data visualization that helps analysts and decision-makers understand the movement of resources, energy, or information through a system. This article will explore how to create a Sankey chart and its versatile applications from environmental and economic analyses to social media trends and beyond.

Understanding Sankey Charts

Sankey diagrams are composed of nodes (the “bars” that indicate where data flows originate and terminus) and arcs (the arrows that show how the data flows from one step to another). They typically display the amount or some form of data flowing from one stage to another and can include information on the value of the data being transferred. The width of the arrows is directly proportional to the amount of data flowing, making it easier to identify the flow direction and magnitude by looking at the Sankey chart.

Creating a Sankey Chart

Creating a Sankey chart primarily involves organizing your data in a table format with columns clearly defined. Here’s a basic step-by-step guide to creating a Sankey chart using Python and the “seaborn” and “matplotlib” libraries:

Step 1: Data Preparation

• Start by organizing your data into a table that contains four columns: Source, Target, Value, and Color.
• Ensure that the Source and Target columns list the nodes from which or to which data transfers occur.
• The Value column represents the flow quantity.

Step 2: Import Libraries

• Import the required libraries, including matplotlib, seaborn, and numpy, for plotting and data handling.

Step 3: Load Data

• Load your prepared data into the Python script.

Step 4: Define and Arrange Node Positions

• Define the coordinates of the nodes in a matrix where the first two columns indicate the start and end positions of each step, respectively.

Step 5: Plot Baseline

• Define the x, y positions of each node and plot the baseline for each step (this step involves coding the positions manually based on your data).

Step 6: Plot Arrows

• Use the matplotlib library to plot the arrows based on the positions of the nodes and the data flow quantities.

Step 7: Style the Plot

• Customize the appearance of the chart by adjusting colors, line widths, and add labels.

Applications of Sankey Charts

Sankey charts are invaluable in a variety of fields:

• Environmental Analysis: They can show energy flow through systems or the carbon footprint of a product.
• Economic Analysis: Illustrate the flow of money through different sectors of an economy or the cost breakdown of a product.
• Social Media Monitoring: Analyze the distribution of follower counts across multiple social media platforms for a brand or individual.
• Information Flow: Show data transfer between different data centers or systems.
• Transportation: Visualize traffic flow through different parts of a city or the distribution of goods between suppliers and retailers.

Conclusion

Sankey charts offer a compelling way to visualize complex data flows. By understanding how to create these charts and appreciating their diverse applications, professionals across various sectors can harness the power of these visual tools to extract insights and communicate findings effectively. Whether for academic research, business strategy, or public policy, Sankey charts have the ability to transform abstract data into comprehensible and compelling visual stories.

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