The notion of capturing data movement and visualizing it in such a way that it tells a story, has always been a central theme in the field of data visualization. Among the myriad of tools and techniques available, Sankey charts have emerged as one of the most effective and visually intuitive ways to represent the flow of data between different entities. In this article, we delve into the creation and application of Sankey charts, exploring not only how to make them but also where and why we might use them.
Understanding Sankey Charts
Sankey diagrams are a type of flow diagram that display how amounts of resource, energy, cost, people, etc., move between entities. These entities could be machines, factories, countries, and even organisms like humans. The flow is represented by connecting nodes with parallel lines that scale in width based on the amount of flow. This simple yet powerful characteristic of scaling line width makes it particularly efficient for visualizing the distribution of resources across different categories or pathways.
How to Create a Sankey Chart
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Data Preparation: Start by organizing your data in a table format where each row represents a flow of data from one entity to another. The data should include columns for source and destination entities, as well as the amount of data flowing between them.
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Sankey Diagram Software: There are numerous software and online tools designed specifically for creating Sankey diagrams, such as Tableau, Gephi, and Python libraries like NetworkX or Plotly. Choose a tool that fits your data visualization needs.
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Plot Layout: Decide on the layout of your diagram. The sources will typically go at one axis, and the sinks (endpoints) will be at the other axis. The width of lines will represent the magnitude of data flow.
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Line Widths: The crucial step in creating a Sankey chart is determining the widths of the lines. The amount of data flowing between entities dictates the width, with larger flows having thicker lines and smaller flows having thinner lines. This is usually done by calculating the percentage of total flow for each step and translating that percentage to a width on a scale of total width possible.
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Coloring: You can enhance the clarity and appeal of your Sankey chart by using color coding to represent different categories of data or entities. Be strategic in your color choices to ensure that the information is not only represented but also legible to the viewer.
Applications of Sankey Charts
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Energy Flows: Sankey diagrams are used extensively in energy audits to illustrate the pathways and losses of energy in buildings or other systems. For example, in a household, the supply of electricity from a utility company might flow through heating systems, appliances, and lighting, showing where energy is lost as heat or consumed.
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Supply Chains: These charts are invaluable in visualizing the flow of materials and resources from suppliers, through manufacturing and distribution, to the end consumer or their final use. They help in identifying inefficiencies and potential cost savings.
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Economic Flows: Sankey diagrams are popular for representing economic data, such as the flow of goods and capital across countries in international trade. This includes everything from simple trade flow visualizations to more complex and theoretical trade models.
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Water Systems: Similar to energy and economic flows, water movement between different stages of treatment, distribution, and consumption in a water system can be represented clearly with a Sankey diagram. This is particularly useful for identifying leaks and inefficiencies in water distribution.
Conclusion
Sankey charts offer a unique approach to represent the flow of data in a visual and intuitive manner. They are not only effective in illustrating the distribution and pathways of data but also in providing insights into the underlying patterns and trends. Whether it’s in the realm of energy efficiency, economic modeling, or supply chain analysis, Sankey charts serve as a powerful tool in data visualization. Understanding how to create them and where to apply them opens up a new dimension of analytical capabilities, making complex datasets more accessible and understandable to a wide audience.
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