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Scatter Plots Design

Scatter plots are a powerful tool for uncovering relationships between data, allowing you to visualize correlations and distributions within AI indicator datasets.

mysimulator teamUpdated June 2026≈ 3 min read▶ Open the simulation

Design scatter plots to reveal correlations between AI indicators (fun

Use scatter plots for visualizing correlation and distribution. Clearly label the axes with units and appropriate baselines based on your data.

Avoid overplotting by limiting the number of points displayed or using a jitter technique to spread them out.

Use transparency or hexbin for dense clouds.

Add marginal histograms or boxplots to illustrate the distributions of your data.

Trendlines and reference lines can further highlight relationships within the scatter plot.

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Add optional regression/Loess lines with equation and R². Provide refe

Hover tooltips display information about each data point, including its entity, x-value, y-value, size metric, and rank.

Enable lasso or box selection to filter views and compare points against benchmarks.

Frequently asked questions

What adjustments should be made when displaying scatter plots on small screens?

On small screens, reduce the amount of text displayed and enable panning and zooming functionality. Consider switching to a bubble list or simplified chart if space is limited.

© 2024 AI Usage by Countries. Scatter plot guidelines.

© 2024 AI Usage by Countries. Scatter plot design guidelines.

When was the last update to these scatter plot design guidelines?

These scatter plot design guidelines were last updated in 2024.

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Everything above runs in your browser — open Hash Function Avalanche Visualizer and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

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