Unraveling the Complexity with Sankey Charts: A Comprehensive Guide to Data Flow Visualization
In the world of data visualization, charts and graphs play a crucial role in making complex information manageable and comprehensible. One such tool, which has gained immense popularity for depicting complex workflows and data cycles, is the Sankey diagram. Also referred to as a flow chart, flux diagram, or material balance diagram, the Sankey chart represents a physical quantity, such as energy or traffic, flowing from one source to another.
Today, we’re going to dive deep into what Sankey charts are, how they work, their unique features, and how to create them using various tools and software, including Tableau, Microsoft Power BI, and Python libraries like Plotly and Bokeh. We’re also going to learn when and where to use these powerful visualization tools to gain insights into business processes, material handling, and information flows.
**What Are Sankey Charts?**
**Definition & Functions**
A Sankey chart is a type of flow diagram which displays quantities as the areas of arrows, or ‘streets’—this allows viewers to easily see which areas are receiving the most and least traffic. They were named after Captain Matthew Henry Phineas Riall Sankey, who used them in the late 19th century to illustrate the energy efficiency of steam engines. They have become widely applicable across various fields, including industrial processes (material handling), energy systems, financial transactions, transportation (traffic flow), and information visualization (website navigation).
**Key Features**
Sankey diagrams have several key features that make them incredibly useful for data flow visualization:
– **Arrows and Volumes**: The thickness of the arrows represents the quantity (volume) of data. Bigger, fatter lines indicate more significant flows, while thinner lines represent smaller flows.
– **Node Labels**: The nodes, or endpoints, in Sankey diagrams represent sources, destinations, or any point along the flow process. Node labels provide context for the data source and target.
– **Flow Pathways**: The nodes are organized in a manner that clearly depicts the pathway that each flow follows from source to destination.
**When to Use Sankey Charts**
Sankey charts are especially useful in the following scenarios:
– **Business Processes**: Understanding and optimizing workflows in manufacturing, logistics, or service industries.
– **Energy and Resource Flows**: Displaying the production and consumption patterns of energy, water, and other resources.
– **Website Navigation**: Analyzing the movement and behavior of website visitors across different pages.
– **Financial Transactions**: Visualizing money flows between accounts or parties in a financial system.
**Creating Sankey Charts with Popular Tools**
**Tableau**
Tableau simplifies the creation of Sankey charts due to its intuitive drag-and-drop interface. To create a Sankey chart in Tableau, you need at least four data fields:
1. a Source column (identifies the origin)
2. an Origin column (specifically for flows between multiple sources in the same node; if not available, the Source field is sufficient)
3. a Destination column (identifies the target)
4. an Amount column (for the size of the flow; Tableau defaults to using this field for arrow thickness)
After connecting your data, drag all four fields onto your sheet. Then, click ‘Sankey’ from the ‘Show Me’ tab. Customize the chart by adjusting the sizes, colors, and labels.
**Microsoft Power BI**
Power BI offers a straightforward method to create Sankey charts through its ‘Sankey’ visual. Start by arranging your data into the following columns:
1. Source
2. Source Label
3. Destination
4. Destination Label
5. Amount
Insert a new Sankey chart through the ‘Visualizations’ pane, then drag your columns into the respective fields on the ‘Visualizations’ pane. Adjust the appearance in the pane’s options menu.
**Plotly & Bokeh in Python**
For Python enthusiasts, libraries like Plotly and Bokeh provide the flexibility to create Sankey charts from scratch.
With `Plotly`, you can use the `plotly.graph_objects.Sankey()` class.
“`python
import plotly.graph_objects as go
data = [
dict( # Node number 1 – Source
label=”Node 1″,
x=200,
y=200,
),
dict( # Node number 2 – Destination
label=”Node 2″,
x=200,
y=-200,
),
dict( # Define the connections
source=[0], target=[1],
value=[10],
color=[“#ff7f0e”],
),
data.append(dict( # Another data point
source=[1],
target=[2],
value=[5],
color=[“#6baed6”]
))
fig = go.Figure(go.Sankey(
node = dict(
pad = 15,
thickness = 20,
line = dict(color = “black”, width = 0.5),
label = [“Node 1”, “Node 2”],
x = [200, -200],
y = [200, -200]
),
link = dict(
source=data[0][“source”],
target=data[0][“target”],
value=data[0][“value”],
color=data[0][“color”]
)))
fig.show()
“`
For the more comprehensive and visually pleasing charts, `Bokeh` also offers a `Sankey` module that is part of the `bokeh.plotting` module.
**Conclusion**
Sankey charts are a robust way to visualise data flows in a dynamic and comprehensible manner. They’re particularly useful in scenarios where you want to demonstrate the movement, conversion, or distribution of quantities between multiple categories or stages. Whether you’re analyzing business processes, energy consumption, or web traffic, Sankey charts can provide valuable insights with their intuitive designs, making it easier to interpret your data and optimize processes effectively. As part of a comprehensive data visualization toolkit, they help in communicating complex data relationships in a clear and accessible way to your stakeholders.
