The basics of timeline data visualization

We’ve talked a lot about timelines since we first announced KronoGraph, our timeline data visualization toolkit, a couple of months ago.

So far we’ve covered some of the key timeline visualization use cases, how to get started with KronoGraph in JavaScript or using the timeline visualization React component, and how to incorporate timelines into your KeyLines and ReGraph applications.

A topic we’ve not covered yet is timelines themselves. It’s about time 🥁 we set that straight.

What is a timeline?

If you didn’t draw a timeline during history class at school, you’ll have seen them in news articles or presentations since. They’re graphical representations of a time period, with key events marked along in chronological order. They can be relatively simple linear representations or more complex visualizations.

Timelines are just one of the ways to represent time.

Histograms and line charts are familiar alternatives. They’re a quick way to get an aggregated summary of activity or value over time. Stacked bars, trend lines, or an adjustable scale can give a more granular view. But, unlike timelines, they’re not designed to show specific events or entities in your data.

Calendars and heatmaps offer great summary views of time-based data. They’re a helpful starting point – showing periods of time that might be of interest. But they’re less helpful to an analyst trying to understand sequences of events or the relationships between them.

Specialist visualizations, like Sankey diagrams and Gantt charts, are more successful at conveying relationships, sequences and dependencies over time in data. But, like the examples above, they’re aggregated views. More often than not, they’re also static snapshots – handy for presentations, less so for active investigations.

All of these examples lack three essential things an investigator needs:

• a view of specific events over time, and the connections between them
• interactivity that allows users to interrogate their data, digging into detail or taking a higher-level view
• a dynamic visualization approach that automatically adapts as data is added or removed

KronoGraph’s timeline data visualizations deliver all of these and more. It means the user can look beyond aggregated views of their data to see specific events, how they unfolded, and how they were connected. The visualizations also transition smoothly from high-level summaries to individual events, giving a clear and adaptable view at any scale.

The result is a much richer, interactive view of events, ideal for data analysts and investigators who need to understand what happened and when.

Why visualize timelines?

Time is a critical dimension to any investigation. Without a clear timeline of events, it’s difficult to ask the right questions or draw accurate conclusions. It’s often impossible to answer the ‘who’, ‘what’, ‘why’, ‘how’ and ‘where’ questions, without first understanding the ‘when’.

KronoGraph timelines pull together disparate and related events into a single view, revealing the complete picture an investigator needs to understand.

Let’s look at an example. Both of these time visualizations show the same fictional dataset of credit card activity.

Imagine you’re a fraud investigator looking at Marc’s disputed transactions (shown in red in both charts). Which visualization makes it easier to identify Marc’s first disputed transaction and the merchants he visited before that transaction?

The graph view, on the left, gives a really clear view of Marc’s transaction history, with different link colors for disputed and undisputed transactions. But with this view alone, we cannot infer the timing of each payment.

The timeline view gives a much clearer answer to time-based questions. We can quickly see the first disputed transaction was in late March at Walgreens. This insight becomes a jumping-off point for deeper investigation.

Explore some of the real-world use cases for timeline data visualization

How do KronoGraph timelines work?

KronoGraph visualizations feature carefully-designed, intuitive visual elements. There’s a scale above and below the timelines, with time running from left to right. We list entities down the left-hand side of the timeline, with events connecting them shown taking place at a point or duration in time. For added context, we can create markers, which add information that’s relevant to the investigation.

You can also use font icons, for added real-world context and faster node recognition.

With this flexible model, you can visualize pretty much any time-based data. As long as it contains timestamps, KronoGraph will visualize every entity, event, and connection.

Fully interactive and scalable

As the scale of the data increases, KronoGraph uses some smart aggregation techniques to remove clutter and help uncover patterns. A handy heatmap view summarizes events over time.

KronoGraph’s lens view allows you to scroll through unlimited timelines, showing a complete overview of all events and entities:

You can also customize the timelines to match your wider application’s look and feel.

Combined, these techniques mean you just load your data and KronoGraph will make sure it looks great. Your users will love them.

Timeline data visualization reveals insight

Hundreds of organizations, and thousands of analysts, already rely on our technologies to drive their investigations. KronoGraph gives them a powerful new way to see their data, and uncover the insight they need to understand.

If you’d like to learn more, simply request a free trial to get started.