Temporal Series
A temporal series layer plays multiple time-stamped samples per location over the timeline — showing each entity's most recent reading as the clock advances. Use it for sensor networks, air-quality stations, or any dataset where the same location or entity is measured repeatedly over time.
What it does
Most point layers show every row at once. A temporal series layer instead treats repeated rows for the same entity as a sequence of samples: at any given time, the map shows only the most recent sample for each entity, and the display updates automatically as the timeline plays.
AugmentCity automatically detects a temporal-series candidate when you import a point file (GeoJSON or CSV) that has both a time/date column and multiple rows for the same entity or location.
Enabling series mode
- Import a GeoJSON or CSV point file that contains a date/time column and repeated rows per entity — see GeoJSON, GPX & TopoJSON and CSV & TSV.
- Open the layer in the Layers panel and expand its settings.
- Go to the Temporal series section.
- Use the mode toggle to choose:
| Mode | Description |
|---|---|
| Events | Every row is an independent feature, shown or hidden by the timeline's time window — the standard temporal filtering behaviour. |
| Series | One active sample per entity at the current clock time. The layer animates as the clock plays. |
Series mode requires a detected time/date field — the same field used for temporal filtering. Configure the field there first if it wasn't auto-detected.
Series options
Group by
Choose how rows are grouped into entities:
| Option | Description |
|---|---|
| Field | Group rows by an attribute value, such as a sensor or station ID. Choose the entity field below. |
| Coordinate | Group rows that share the same location, regardless of any ID field. Useful when your data has no reliable entity identifier. |
Entity field
When grouping by Field, choose which attribute identifies each entity — for example sensor_id or station_id. All rows sharing the same value in this field are treated as samples of the same entity over time.
Smooth transition
Enable Smooth transition to have values glide continuously toward the next sample as the clock advances — position, size, height, and gradient colour all morph together — instead of snapping at each timestamp. The morph spans the full interval between an entity's samples automatically.
Turn the option off to step values directly at each sample time, which better represents discrete readings that shouldn't be interpolated.
How it renders
Series mode works with the point visualisation styles that already exist on the layer. The active sample for each entity drives:
| Layer style | What animates |
|---|---|
| Point Marker | Marker scale, colour, position (for moving entities), and pole height |
| 3D Bar Chart | Bar height and colour |
| Heatmap | Density, based on which points are currently active |
Combine series mode with gradient colouring on a numeric field like a pollutant reading — the colours shift over time as new samples become active.
Timeline integration
A temporal series layer automatically drives the timeline: the clock range is set to the data's full time span and the timeline bar appears. Press Play or scrub the playhead to animate the layer — see Animate through time.
This works in shared and embedded links too, so recipients see the same animation without needing an account.
Data types
Series mode works with numeric values from GeoJSON and with CSV imports, where every value arrives as text. Numeric-looking text fields (such as "42.3") are parsed automatically and animate smoothly, the same way native GeoJSON numbers do.
If a value can't be parsed as a number for a style that requires one (such as bar height), that sample is skipped for that attribute — the entity keeps its last valid value until the next parseable sample arrives.