Data Visualization: Understanding Sankey Diagrams – A Comprehensive Step-by-Step Guide
Sankey diagrams are considered highly effective for visualizing relationships between entities where data flows are concerned. They were invented by William Sankey and first utilized at the end of the 19th century. Since then, the use of Sankey diagrams has significantly expanded due to their unique ability to depict not only the magnitude of data but also the direction and flow between nodes. This article will take you through the basics of Sankey diagrams and guide you on how to create and effectively interpret them.
### What Are Sankey Diagrams?
Sankey diagrams, named after William Sankey, an English engineer and inventor, are a type of flow diagram in which the width of the arrows is proportional to the flow quantity in a given data set. They are used in a multitude of fields including engineering, economics, science, and data analytics to represent the flow of a quantity through a system.
### Components of a Sankey Diagram
1. **Nodes**: Representing discrete entities in the flow, such as sources, sinks, or intermediate points.
2. **Arrows (or Bands)**: Show the flow between nodes. The width of the arrows visually denotes the magnitude of the flow quantity, making it easier to understand where the most considerable data is moving.
3. **Arcs**: Can be used to connect the nodes and to maintain the flow’s continuity between them.
### Creating Sankey Diagrams
#### 1. Determine Flow Quantities
The first step in creating a Sankey diagram involves collecting the quantities that will form the flow data between nodes. These quantities should be quantifiable measurements representing the direction, volume, or amount of flow.
#### 2. Decide on Nodes
Next, identify the entities involved. Nodes typically consist of ‘sources’ or ‘primary’ entities at the start of the data flow in the diagram.
#### 3. Select a Visualization Tool
Choose a suitable tool for creating Sankey diagrams. Tools such as Python libraries (Matplotlib, Seaborn, Plotly, or Pandas), JavaScript frameworks (D3.js), or specialized software can be utilized to generate these diagrams.
#### 4. Design the Layout
Decide on how to layout your nodes and flow. The layout can affect how easy it is to interpret the flow from one entity to another. There are many strategies to ensure clarity and aesthetics, such as using force-directed graphs to optimize node placement.
#### 5. Plot the Data
Once you’ve prepared your data and chosen your tool, plot the diagram. Adjust the sizes of the bands between nodes according to the quantities of flow, ensuring consistency in your visualization.
#### 6. Enhance Readability
Add labels to nodes and flow paths to make the diagram more informative. Annotations can aid in interpreting the diagram by explaining the function or meaning behind the flow between nodes.
### Interpreting Sankey Diagrams
#### 1. Analyze Flow Amounts
The width of the arrows (or bands) is crucial in interpreting the magnitude of the flow. Thicker arrows represent larger quantities, while narrower ones signify smaller data flows.
#### 2. Identify Major Flows
Major flows can be seen in certain paths between nodes which are relatively wider than the rest. This helps delineate the most significant data transit within the system represented by the Sankey diagram.
#### 3. Understand the System Dynamics
A Sankey diagram provides a visual framework to understand the underlying system’s dynamics. By examining the diagram, one can decipher how data is routed, where bottlenecks may lie, and how the flow distribution changes over time.
#### 4. Spot Node Relationships
The diagram also highlights strong or weak relationships between nodes. Nodes attached to high-traffic paths demonstrate significant interaction, while those with few connections might be less significant players in the flow process.
Mastering the creation, understanding, and interpretation of Sankey diagrams not only adds to your data visualization toolkit but also enhances your ability to tackle complex data representation tasks in fields like economics, transportation, and more. By leveraging these diagrams, professionals can make informed decisions based on intuitive and clear visual representations of data flows, thereby improving their overall analytical and strategic capabilities.
