Most processes around us evolve over time. Air temperatures fluctuate from season to season, cities grow over decades, traffic flows fluctuate throughout the day, and economic indicators respond to booms and busts. If we record such data at a single point, we only get a snapshot of what's happening at that point in time – no dynamics.

Modern analytics increasingly demands not just knowing the value of an indicator, but understanding how and when it changes. This is where time data and the tools that allow for its accurate representation come into play.

What is temporal data and where is it used?

Temporal data is data that contains a time characteristic: a specific date, point in time, or period of action. Without this label, it is impossible to trace the development of a process. There are several common forms:

• Point data: records the state at a specific moment (e.g., temperature at 12:00 PM);

• Interval data: describes the period of an event (construction from 2018 to 2022);

• Time series: a sequence of values ​​ordered by time.

Such data can be stored in tables, databases, vector layers, or specialized analytical systems. In spatial analysis, another characteristic—time—is added to the object's coordinates, turning the map into a four-dimensional model of reality.

The applications of temporal data are extremely broad. In climatology, they allow tracking long-term trends and extreme events. In urban studies, they are used to analyze development growth and changes in traffic flows. In economics, this means identifying cycles and forecasting the dynamics of indicators. In ecology, this means assessing changes in species ranges and the state of ecosystems.

However, the mere presence of time information does not guarantee its understanding. A table with thousands of dates means little without a tool that allows us to see the development of a process over time. That's why our GISCARTA platform includes a Timeslider widget for displaying the dynamics of various processes. More details on working with it are provided later in the article.

Time Slider widget on the GISCARTA platform

Step 1. To start using the widget, enable its availability in the Widgets section of the toolbar.

Step 2. Next, you need to open the widget window.

Now you need to select the layer you plan to work with. The main requirement is that it must have an attribute with the Date data type. The platform will automatically suggest suitable layers by clicking the leftmost button in the layer view.

Step 3. Configure the widget's timeline (years, months, days) and its step size. You can do this by clicking the gear icon in the widget window. It's worth noting that you can also select the timeline and step size of the source layer so that data is only taken from the periods specified in the attributes.

Step 4. Select the starting point.

Step 5. Now that the widget is configured, you can preview its operation using the play icon (the steps will automatically switch) or manually using the arrows on the right side of the widget window.

The widget's operation can be seen below using the example of changes in the composition of the Arctic ice cover.

More details about how the widget works can be found here.

FAQ

What data can be used with the TimeSlider widget?
Any layer that contains a date-type attribute (timestamp or time period) can be used for temporal visualization.

Can the time scale be customized?
Yes. Users can configure the time units (years, months, days) and adjust the step interval manually or based on the source layer’s time attributes.

Is animation automatic or manual?
Both options are available. You can run automatic playback using the play button or move through time steps manually with navigation arrows.

Key Takeaways

Time Slider enables dynamic visualization of temporal data instead of static snapshots.
It works with layers that include date attributes and allows flexible timeline configuration.
The widget helps identify trends, patterns, and changes over time through interactive animation.