Sankey charts are a type of data visualization that helps users understand the flow and connections between different data points. These charts are named after Dr. Matthew Henry Phineas Riall Sankey, who first used them in the mid-19th century to show the energy consumption of the Newlands Works power station in Reid, Scotland. Since their inception, Sankey charts have evolved and are now used in a variety of fields and industries. In this article, we will delve into the power of Sankey charts and explore their creation process and diverse applications.
Creation Process of Sankey Charts
Creating a Sankey chart requires a certain level of data visualization expertise, but once you understand the process, it becomes an efficient tool for visualizing complex data. Below are the steps involved in the creation of Sankey charts:

1. Collect and Organize Data: Start by gathering the data you want to visualize. Sankey charts are best suited for data that has a quantitative or categorical nature and contains flow data. The data should be organized in a structured format, such as a spreadsheet or a database, and should contain the source and target data points, flow quantities, and any relevant labels.
2. Determine Node Positions: Nodes, which represent the source and target data points, should be positioned on the chart. One node can be the start of the flow, and the other can be the end. You can also add intermediary nodes to represent the flow’s direction, and they can be used to represent the different categories or stages in the flow. It’s crucial to have a clear understanding of the data and be able to map it to the appropriate positions on the chart.
3. Draw Edges with Widths: Edges represent the flow between the nodes and have widths that show the magnitude of the flow data. The widths can be adjusted to show the data’s quantity. The larger the flow quantity, the thicker the edges. You can also use colors or patterns to differentiate between different flows or data categories.
4. Adding Visual Enhancements: Now that you have created a basic Sankey chart, you can add visual enhancements, such as labels, annotations, or tooltips, to make the chart more readable and informative. You can also add graphs, graphs, charts, or images to provide more context or to make the chart more visually appealing.

Applications of Sankey Charts
Sankey charts can be used in a variety of fields and industries to represent complex data. Below are some common applications of Sankey charts:

1. Energy and Environmental: Sankey charts are typically used to show energy flows, such as electricity, gas, or water consumption. The flow represents the quantity, and the colors represent the type of source, such as renewable or non-renewable energy.
2. Business and Finance: Sankey charts can represent financial flows, such as investments, expenditures, and profits. They can help users understand the movement of money across different categories or divisions within an organization or across multiple organizations.
3. Science and Medical: Sankey charts can represent the flow of atoms, chemicals, or biological molecules in various reactions or processes. They are also used to represent the flow of blood in the human body or the movement of water in different ecosystems.
4. Social Sciences: Sankey charts can represent the flow of information across social networks or the movement of people between different regions or countries.

Conclusion
Sankey charts are an excellent tool for visualizing complex data and representing the flow and connections between different data points. By following the creation process and utilizing the diverse applications, you can effectively communicate your data’s message and gain insights that you might have overlooked otherwise. With the ability to provide informative, visually appealing, and easy-to-understand data, Sankey charts are an essential tool for anyone working with data.

SankeyMaster

SankeyMaster is your go-to tool for creating complex Sankey charts . Easily enter data and create Sankey charts that accurately reveal intricate data relationships.