Work with geo-temporal data
Use the Flux Geo package to filter geo-temporal data and group by geographic location or track.
The Geo package is experimental and subject to change at any time. By using it, you agree to the risks of experimental functions.
To work with geo-temporal data:
Import the
experimental/geopackage.import "experimental/geo"Load geo-temporal data. See below for sample geo-temporal data.
Do one or more of the following:
- Shape data to work with the Geo package
- Filter data by region (using strict or non-strict filters)
- Group data by area or by track
Shape data to work with the Geo package
Functions in the Flux Geo package require lat and lon fields and an s2_cell_id tag. Rename latitude and longitude fields and generate S2 cell ID tokens.
import "experimental/geo"
sampleGeoData
|> map(
fn: (r) => ({r with
_field: if r._field == "latitude" then
"lat"
else if r._field == "longitude" then
"lon"
else
r._field,
}),
)
|> map(fn: (r) => ({r with s2_cell_id: geo.s2CellIDToken(point: {lon: r.lon, lat: r.lat}, level: 10)}))
Filter geo-temporal data by region
Use the geo.filterRows function to filter geo-temporal data by box-shaped, circular, or polygonal geographic regions.
import "experimental/geo"
sampleGeoData
|> geo.filterRows(region: {lat: 30.04, lon: 31.23, radius: 200.0}, strict: true)
Group geo-temporal data
Use the geo.groupByArea() to group geo-temporal data by area and geo.asTracks() to group data into tracks or routes.
import "experimental/geo"
sampleGeoData
|> geo.groupByArea(newColumn: "geoArea", level: 5)
|> geo.asTracks(groupBy: ["id"],orderBy: ["_time"])
Sample data
Many of the examples in this section use a sampleGeoData variable that represents
a sample set of geo-temporal data.
The Bird Migration Sample Data
provides sample geo-temporal data that meets the
requirements of the Flux Geo package.
Load bird migration sample data
Use the sample.data() function
to load the sample bird migration data:
import "influxdata/influxdb/sample"
sampleGeoData = sample.data(set: "birdMigration")
sample.data() downloads sample data each time you execute the query (~1.3 MB).
If bandwidth is a concern, use the to() function
to write the data to a bucket, and then query the bucket with from().
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