An earthquake is rarely defined by a single measurement. Every seismic event generates multiple layers of data that, together, help reveal what occurred beneath the Earth’s surface.

The challenge is that earthquake data rarely exists in a form that’s easy to interpret and communicate. Information about location, magnitude, depth, frequency, and other variables is often spread across multiple visuals. Even when each one is accurate, understanding and sharing the full story requires constantly piecing those views together.

That’s where multivariate visualization comes in. 

By bringing multiple dimensions together into a single graph, you can understand and communicate complex relationships more effectively. But how exactly does this happen? Let’s explore how to create multivariate visualizations for earthquake data so you can easily interpret and share the complete picture with stakeholders. 

First, it’s worth asking: why isn’t it enough to analyze earthquake data one variable at a time? The answer lies in the fact that earthquakes are inherently multivariable events. 

Every earthquake has a location, magnitude, depth, time of occurrence, and many other characteristics that work together to determine its overall impact. Looking at each variable separately can certainly provide useful information, but it rarely reveals how those variables interact.

Consider a histogram showing the distribution of earthquake magnitudes. It does an excellent job of answering one question: How strong were the recorded earthquakes? However, it doesn’t reveal where those earthquakes occurred, how deep they originated, or whether they were isolated events or part of a larger seismic sequence. Important context is missing.

The same limitation applies to other single-variable visualizations. A time-series graph may reveal periods of increased seismic activity, while a depth distribution graph shows how earthquakes are distributed beneath the Earth’s surface. Each visualization tells part of the story, but none of them shows how magnitude, depth, location, and timing relate to one another.

Those relationships often make all the difference. For example, two earthquakes may both register a magnitude of 6.0, yet produce drastically different impacts depending on their focal depth. A shallow earthquake concentrates its energy near the surface, generating intense, high-frequency ground shaking directly above the focus. Conversely, a deeper earthquake of the same magnitude must travel farther through the crust to reach the surface; its energy dissipates along the way, arriving weaker directly above the epicenter and producing less severe localized damage, but spreading the seismic energy across a much broader geographic area. Looking at magnitude alone doesn’t reveal these important differences. 

Visualizing multivariate earthquake data on a single graph is critical for interpreting and communicating the full picture of seismic events. But what does this type of graph even look like?

The answer lies in an XYZC data structure, where each visual property represents a different dimension of the earthquake-related dataset. Rather than displaying one variable at a time, an XYZC data structure can fuse chronological, physical, spatial, and categorical variables into a single plot, transforming raw measurements like location, time, magnitude, or focal depth into one highly intuitive visual narrative.

When using this data structure, you might organize your graph like this:

• X and Y represent the geographic coordinates of each earthquake epicenter, establishing where each seismic event occurred.
• Z or Bubble Size represents earthquake magnitude, with larger bubbles indicating stronger earthquakes.
• C (Color) represents the depth of the earthquake’s focus, with different colors corresponding to shallow or deep events.

By distributing the data across various visual attributes, a single plot provides a holistic view of a seismic event. This eliminates the guesswork of cross-referencing disjointed charts, ensuring critical patterns in the data emerge more naturally.

While understanding the abstract data structure of an XYZC plot is a great foundation, examining a real-world seismic event demonstrates exactly how these dimensions operate in practice.

Consider the graph below, which visualizes magnitude 4.5 and greater earthquakes that occurred along the Chilean subduction zone during 2024. It uses an XYZC dataset, where X and Y represent the geographic coordinates of each earthquake epicenter, Bubble Size represents earthquake magnitude, and C represents focal depth through color. Instead of separating these variables into individual graphs, the plot combines them into a single visualization.

At first glance, the graph immediately communicates where earthquakes occurred because each bubble is positioned according to its latitude and longitude. But the additional variables quickly add another layer of insight.

Notice how bubble color gradually changes from darker shades to brighter yellows as you move east across the graph. Because color represents focal depth, this trend reveals that earthquakes become progressively deeper farther inland. This sloping band of ever-deeper quakes is known as the Wadati-Benioff zone, and it directly traces the Nazca Plate descending beneath the South American Plate.

Bubble size adds even more context. Larger bubbles identify higher-magnitude earthquakes, allowing you to quickly distinguish the most powerful events from the many smaller earthquakes occurring throughout the region. Instead of searching through a table of magnitudes, the largest events immediately stand out.

The real power of this graph, however, comes from seeing all four variables interact simultaneously. Rather than asking separate questions such as Where did earthquakes occur?, How large were they?, and How deep were they?, you can answer them all within a single view. Relationships between earthquake location, magnitude, and focal depth become immediately apparent, making it easier to recognize regional seismic patterns and communicate those findings with confidence.

As the Chile subduction zone example demonstrates, a well-designed multivariate graph can communicate a remarkable amount of information at once. But combining multiple variables into a single visualization also introduces a challenge: if the graph becomes too cluttered, the insights can quickly become harder to find than the data itself. To present the most important relationships in a way that’s easy to interpret, here are a few best practices to help you create clear, effective multivariate graphs.