This page defines key terms and concepts for using geotemporal series in Foundry. We recommend reviewing these concepts to better understand how to use geotemporal series in your organization.
A sequence of position and timestamp data representing the location of an entity over time. Each sequence is referred to as a geotemporal series and is identified by a series ID. Individual points in the series are referred to as observations. For example, a flight from San Francisco to New York City could be represented as a geotemporal series where each reported location from the plane during the flight is an observation.
Geotemporal data is also called spatiotemporal, "geotime", or "track" data.
An identifier that groups multiple geotemporal observations into a single series. The series ID must be unique within a given geotemporal series integration. For example, the concatenation of the flight number, origin, destination, and date could be used to uniquely identify a single flight.
An object type containing one or more geotemporal series reference properties, and optionally, other properties about the geotemporal series being referenced. For example, an object type representing a flight may include the origin and destination airports as string properties along with the flight path as a geotemporal series reference.
A property type used to reference a particular geotemporal series from a geotemporal series integration. Foundry applications use this reference to fetch the backing geotemporal data for the series.
Indexes the geotemporal series data into Foundry's geotemporal series database. Once indexed, the geotemporal data is accessible from the GTSR on objects. All values for a series ID should be contained in the same sync. The sync can be created using the geotemporal series sync output in Pipeline Builder.
An individual point in a geotemporal series that consists of a series ID, timestamp, position, and other integration-defined properties. For example, a single GPS ping from a plane would be an observation in a geotemporal series. These can also be called "ticks".