Introduction
Sankey charts have become a favorite for visualizing relationships between data sets. Used in many fields, including economics, science, environmental studies, social sciences, and more, these charts allow you to understand the flow or transfer of data between various categories, entities, or processes. A Sankey chart appears as a flow diagram where the width of each link represents the volume of data flow between different nodes. To make the most of these charts, we need to start by understanding how to effectively create and interpret them.
Creating Sankey Charts
Before starting to work with Sankey charts, it’s crucial to gather, organize, and sort data in the correct categories. Most data for charting flows is best organized into categories, with one being the ‘source’ category emitting flows, and the other being the ‘destination’ categories receiving the flows. This format enables readers to identify the major flows in your dataset.
1. Data Preparation
Before creating a Sankey chart, ensure your data is cleaned and sorted. This involves removing irrelevant data, correcting inconsistencies, and organizing your data in a way that it is sorted by categories corresponding to source and destination. Use an appropriate data structure, like JSON or CSV, depending on the charting tool you plan to use.
2. Choose a Charting Tool
With a properly prepared dataset, you can proceed to use software or online tools that support the creation of Sankey charts, such as Tableau, Microsoft Power BI, D3.js, or specialized software like Sigma.js, Sankeymatic, Chart.js, or Sankey.js. Each tool has its unique features, from simplicity to customizability to interactivity, that may suit your project needs.
3. Design and Layout
Once you’ve chosen your tool, design a layout for your Sankey chart. Decide where to position the nodes and how the flows will visually connect them. Adjust the size and shape of your nodes as needed. Most tools allow for customization of labels, colors, and other design elements to match your project requirements.
4. Build the Chart
Insert your source and destination categories into your chosen charting tool, inputting the flow data corresponding to the relationships established between the categories. Adjust the width of the edges based on the magnitude of the data flow. Tools generally have features that automatically adjust these widths according to the data volume, although manual adjustments may occasionally be necessary.
5. Review and Refine
After building the chart, ensure the layout and data representation accurately reflect the relationships and flow volumes present in your dataset. Adjust as necessary to optimize readability and clarity. Additionally, include meaningful labels to help readers understand the context and data flow more easily.
Interpreting Sankey Charts
Interpreting Sankey charts requires paying attention to the visual elements and understanding the underlying data represented. By following these guidelines, you can maximize the insights provided by your charts:
1. Analyze the Flow Patterns
Start by examining the patterns of the data flow. Observe the main and distinct pathways between nodes, and note any clustering or separation in the flows. This analysis can provide insights into areas that are heavily involved in transactions or flows, highlighting key drivers or mechanisms at work.
2. Understand Node Significance
Each node has its importance in the flow dynamics. Nodes that are connected to many other nodes with high flow volumes indicate substantial input or output in the dataset. Identifying these points can help pinpoint critical activities or entities in the data flow.
3. Note the Flow Volumes
Carefully note the widths of the edges between nodes, as they represent different volumes of flow. Broadly speaking, wider arrows signify higher volumes of data or resources being transferred, which can help gauge the intensity of connections or transactions.
4. Contextualize with Additional Data
To get a comprehensive understanding, it may be necessary to gather and contextualize additional data with the Sankey chart. Connecting the chart with other data tables, charts, or variables can provide deeper insights into specific dynamics and implications within the flow.
5. Identify Trends and Anomalies
Watch for any patterns or anomalies in the flow dynamics. A sudden increase or decrease in the flow widths can indicate significant events or potential issues in the system you are modeling. Additionally, observe if trends or cyclic patterns exist across different points in time, which might reveal growth, stability, or decline in the flow relationships.
Conclusion
Now that we’ve covered how to create and interpret Sankey charts, these powerful visual tools should be more accessible and useful for your data analysis needs. They provide a unique opportunity to visualize and understand the complex flow relationships within your data. Remember to always refine your data structure and be mindful of your chart’s layout to maximize the clarity and usability for your audience. Happy charting and data exploration!
