What factors determine whether to use a 2D or 3D visualization for your statistical data?

1 Data structure and variables

One of the main factors that determines whether to use a 2D or a 3D visualization is the structure and the number of variables in your data. Generally, 2D visualizations are more effective for data that has one or two variables, such as bar charts, line charts, or scatter plots. 3D visualizations can be useful for data that has three or more variables, such as surface plots, volume plots, or network graphs. However, you should also consider the trade-offs between adding more dimensions and reducing the clarity and readability of your visualization. Sometimes, using multiple 2D visualizations can be better than using a single 3D visualization to show complex data.

2 Data distribution and outliers

Another factor that influences whether to use a 2D or a 3D visualization is the distribution and the presence of outliers in your data. Outliers are data points that deviate significantly from the rest of the data and may indicate errors, anomalies, or interesting phenomena. 2D visualizations can be effective for data that has a normal or symmetric distribution, where most of the data points are clustered around the mean or the median. 3D visualizations can be helpful for data that has a skewed or multimodal distribution, where there are multiple peaks or tails in the data. 3D visualizations can also highlight outliers more easily than 2D visualizations, as they can show the depth and distance of the data points from the main surface or plane.

3 Data domain and audience

A third factor that affects whether to use a 2D or a 3D visualization is the domain and the audience of your data. Different domains and audiences may have different preferences, expectations, and familiarity with data visualizations. For example, scientific or engineering domains may favor 3D visualizations, as they can show more details and complexity of the data. Business or marketing domains may prefer 2D visualizations, as they can convey simple and clear messages and comparisons. Similarly, different audiences may have different levels of data literacy and attention span. You should tailor your visualization to match the needs and interests of your audience, and avoid using 3D visualizations that may confuse or distract them.

4 Data interactivity and animation

A fourth factor that impacts whether to use a 2D or a 3D visualization is the interactivity and animation of your data. Interactivity and animation are features that allow users to manipulate, explore, or animate the data visualization, such as zooming, rotating, filtering, or playing. 2D visualizations can benefit from interactivity and animation, as they can enhance the user engagement and understanding of the data. 3D visualizations can also benefit from interactivity and animation, as they can overcome some of the limitations and challenges of 3D visualizations, such as occlusion, perspective, or distortion. However, you should also be careful not to overload your visualization with too much interactivity and animation, as they can make your visualization more complex and less intuitive.

5 Data aesthetics and design

A fifth factor that influences whether to use a 2D or a 3D visualization is the aesthetics and design of your data. Aesthetics and design are aspects that relate to the visual appeal, style, and quality of your data visualization, such as color, shape, size, or texture. 2D visualizations can benefit from aesthetics and design, as they can attract the user attention and emotion, and convey the tone and mood of your data. 3D visualizations can also benefit from aesthetics and design, as they can create a sense of realism, depth, and immersion for your data. However, you should also be mindful not to sacrifice the accuracy and clarity of your data for the sake of aesthetics and design, as they can mislead or deceive the user.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?