The process of visualising data is known as visual encoding.
Visual encoding is the process of mapping data variables to visual properties in order to create a visual representation of the data.
These visual properties include;
Colour 🟢
Line weight —
Area ▭
Position ↸
Height ↕
Length ↔
Sound complicated? don't worry! there are many online tools auto-generate the process of visual encoding using templates
User only needs to upload the data.
Then choose from a selection of different visualisation options.
And customise with labels, annotations, colour and images.
Graphic: Alberto Cairo, The Truthful Art: Data, Charts and Maps for Communication
Click the expander below to view our step-by-step visual encoding guide
Visual Encoding Guide | Step-by-step
Visual encoding is a process of mapping data variables to visual properties such as size, shape, color, position, and texture, in order to create a visual representation of the data that is easy to understand and interpret. The following are the steps to conduct visual encoding:
Identify the data: The first step is to identify the data that needs to be visualized. This might involve collecting data from various sources or using data that has already been collected.
Determine the purpose: Determine the purpose of the visualization, including the questions that need to be answered and the audience that will be viewing the visualization.
Select the appropriate visualization technique: Choose the most appropriate visualization technique based on the type and complexity of the data, as well as the purpose of the visualization. This might involve using charts, graphs, maps, or other types of visualizations.
Map data variables to visual properties: Map the data variables to the visual properties selected in the previous step. For example, if using a scatter plot, map the x-axis to one variable and the y-axis to another variable.
Choose appropriate scales: Choose appropriate scales for the visual properties used to encode the data. This might involve selecting appropriate color palettes, size ranges, or axis ranges.
Add contextual elements: Add contextual elements to the visualization to provide additional information or context to the audience. This might include labels, annotations, or other explanatory text.
Refine the visualization: Refine the visualization by adjusting the design, layout, or other elements to ensure that it effectively communicates the desired message or insights to the audience.
Test and validate: Test and validate the visualization with the intended audience to ensure that it effectively communicates the desired message and insights.
By following these steps, data analysts and visualizers can effectively create visualizations that are easy to understand and communicate complex data insights to a wide range of audiences.
