Spatio-temporal
Spatio-temporal datasets link each data point to a specific point in time and a location on the Earth’s surface.
Each spatio-temporal dataset comes with a set of required and auto-generated fields for each data point.
Required fields
While the specific data fields between different time series datasets can vary, there are common fields that all time series datasets share.
The timestamp associated with each data point. Timestamps are always in UTC.
For indexing and querying, Tilebox truncates timestamps to millisecond precision. But Timeseries datasets may contain arbitrary custom Timestamp fields that store timestamps up to a nanosecond precision.
A location on the earth’s surface associated with each data point. Supported geometry types are Point, Polygon and MultiPolygon.
Auto-generated fields
A universally unique identifier (UUID) that uniquely identifies each data point. IDs are generated so that sorting them lexicographically also sorts them by time.
IDs generated by Tilebox are deterministic, meaning that ingesting the exact same data values into the same collection will always result in the same ID.
The time the data point was ingested into the Tilebox API.
Creating a spatio-temporal dataset
To create a spatio-temporal dataset, use the Tilebox Console and select Spatio-temporal Dataset as the dataset type. The required and auto-generated fields
already outlined will be automatically added to the dataset schema.
Spatio-temporal queries
Spatio-temporal datasets support efficient time-based and spatially filtered queries. To query a specific location in a given time interval, specify a time range and a geometry when querying data points from a collection.
Geometries
Handling Geometries can traditionally be a bit tricky, especially when working with geometries that cross the antimeridian or cover a pole. Tilebox is designed to take away most of the friction involved in this, but it’s still recommended to follow the best practices for handling geometries.