Grafana includes a WorldMap visualization that help you see geospatial data overlaid atop a map of the world. This can be helpful to understand how data changes based on its location.

To complete this tutorial, you need a cursory knowledge of the Structured Query Language (SQL). The tutorial walks you through each SQL command, but it is helpful if you've seen SQL before.

Once your installation of TimescaleDB and Grafana are complete, ingest the data found in the NYC Taxi Cab tutorial and configure Grafana to connect to that database. Be sure to follow the full tutorial if you're interested in background on how to use TimescaleDB.


Be sure to pay close attention to the geospatial query portion of the tutorial and complete those steps.

The NYC Taxi Cab data also contains the location of each ride pickup. In the NYC Taxi Cab tutorial, we examined rides that originated near Times Square. Let's build on that query and visualize rides whose distance traveled was greater than five miles in Manhattan.

We can do this in Grafana using the 'Worldmap Panel'. Start by creating a new panel, selecting 'New Visualization', and selecting the 'Worldmap Panel'.

Once again, you can edit the query directly. In the Query screen, be sure to select your NYC Taxicab Data as the data source. In the 'Format as' dropdown, select 'Table'. Click on 'Edit SQL' and enter the following query in the text window:

SELECT time_bucket('5m', rides.pickup_datetime) AS time,
rides.trip_distance AS value,
rides.pickup_latitude AS latitude,
rides.pickup_longitude AS longitude
FROM rides
WHERE $__timeFilter(rides.pickup_datetime) AND
) < 2000
GROUP BY time,
LIMIT 500;

Let's dissect this query. First, we're looking to plot rides with visual markers that denote the trip distance. Trips with longer distances get different visual treatments on our map. Use the trip_distance as the value for our plot, and store this result in the value field.

In the second and third lines of the SELECT statement, we are using the pickup_longitude and pickup_latitude fields in the database and mapping them to variables longitude and latitude, respectively.

In the WHERE clause, we are applying a geospatial boundary to look for trips within 2000m of Times Square.

Finally, in the GROUP BY clause, we supply the trip_distance and location variables so that Grafana can plot data properly.


This query may take a while, depending on the speed of your Internet connection. This is why we're using the LIMIT statement for demonstration purposes.

Now let's configure our Worldmap visualization. Select the 'Visualization' tab in the far left of the Grafana user interface. You'll see options for 'Map Visual Options', 'Map Data Options', and more.

First, make sure the 'Map Data Options' are set to 'table' and 'current'. Then in the 'Field Mappings' section. Set the 'Table Query Format' to be 'Table'. We can map the 'Latitude Field' to our latitude variable, the 'Longitude Field' to our longitude variable, and the 'Metric' field to our value variable.

In the 'Map Visual Options', set the 'Min Circle Size' to 1 and the 'Max Circle Size' to 5.

In the 'Threshold Options' set the 'Thresholds' to '2,5,10'. This auto configures a set of colors. Any plot whose value is below 2 is a color, any value between 2 and 5 is another color, any value between 5 and 10 is a third color, and any value over 10 is a fourth color.

Your configuration should look like this:

Mapping Worldmap fields to query results in Grafana

At this point, data should be flowing into our Worldmap visualization, like so:

Visualizing time series data in PostgreSQL using the Grafana Worldmap

You should be able to edit the time filter at the top of your visualization to see trip pickup data for different timeframes.

Complete your Grafana knowledge by following all the TimescaleDB + Grafana tutorials.


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