What are some effective techniques for using 3D visualizations to show variability in data?

2 Use color, size, and shape to encode variability

One of the main challenges of 3D visualizations is to convey variability in data effectively and accurately. To do so, you need to use visual variables that can encode variability without compromising the perception of the data structure and patterns. Color, size, and shape are three common visual variables that can be used to encode variability in 3D visualizations. For example, you can use color to show uncertainty, outliers, or ranges in data by using gradients, hues, or saturation. You can use size to show variability in magnitude, frequency, or importance by using scaling, binning, or clustering. You can use shape to show variability in categories, types, or groups by using symbols, glyphs, or icons. However, you also need to be careful with the choice and combination of these visual variables, as they may interact with each other and affect the perception of depth, distance, and orientation in 3D space. For instance, you may want to avoid using color and size simultaneously to encode different aspects of variability, as they may create conflicting cues and illusions. You may also want to avoid using shape to encode continuous or ordinal variables, as they may create artificial boundaries and discontinuities.

3 Provide multiple views and interactions

Another challenge of 3D visualizations is to provide a comprehensive and intuitive view of the data from different angles and perspectives. To do so, you need to provide multiple views and interactions that can help the user explore, compare, and manipulate the data in 3D space. Multiple views can be achieved by using different projections, orientations, or subplots that can show different facets or aspects of the data. For example, you can use orthogonal, perspective, or isometric projections to show different levels of depth and distortion. You can use parallel, perpendicular, or tilted orientations to show different alignments and relationships. You can use side-by-side, stacked, or nested subplots to show different subsets or aggregations of the data. Interactions can be achieved by using different controls, widgets, or gestures that can enable the user to zoom, rotate, pan, or select the data in 3D space. For example, you can use sliders, buttons, or menus to control the parameters, filters, or options of the visualization. You can use mouse, keyboard, or touch to manipulate the view, orientation, or position of the visualization. You can use clicks, drags, or taps to select, highlight, or annotate the data in the visualization.

4 Follow 3D visualization design principles

To create effective 3D visualizations that show variability in data, you should adhere to some general design principles to enhance the clarity, readability, and aesthetics of the visualization. These include keeping it simple and consistent, using contrast and balance, ensuring alignment and legibility. Simplicity means using the minimum number of visual elements and details necessary to convey the message. Consistency requires using the same visual encoding and style for the same type of data. Contrast can be achieved by using different visual variables and values to create distinction between different data points and groups. Balance involves using appropriate proportions and distributions of visual elements and space. Alignment entails proper alignment and orientation of visual elements and axes. Legibility requires clear fonts, labels, and legends that provide context for the visualization.