In the previous post, we — creatively ;-) — used MobilityDB to visualize stationary IOT sensor measurements.

This post covers the more obvious use case of visualizing trajectories. Thus bringing together the MobilityDB trajectories created in Detecting close encounters using MobilityDB 1.0 and visualization using Temporal Controller.

Like in the previous post, the valueAtTimestamp function does the heavy lifting. This time, we also apply it to the geometry time series column called trip:

SELECT mmsi,
    valueAtTimestamp(trip, '2017-05-07 08:55:40') geom,
    valueAtTimestamp(SOG, '2017-05-07 08:55:40') SOG
FROM "public"."ships"

Using this SQL query, we again set up a — not yet Temporal Controller-controlled — QueryLayer.

To configure Temporal Controller to update the timestamp in our SQL query, we again need to run the Python script from the previous post.

With this done, we are all set up to animate and explore the movement patterns in our dataset:

This post is part of a series. Read more about movement data in GIS.

The latest v0.12 release is now available from conda-forge.

This release contains some really cool new features, including:

  • New function to add an acceleration column #253
  • We have further improved our repo setup by adding an action that automatically creates and publishes packages from releases, heavily inspired by the work of the GeoPandas team.
  • Last but not least, we’ve created a Twitter account for the project. (And might soon add a Mastodon account as well.)

As always, all tutorials are available from the movingpandas-examples repository and on MyBinder:

If you have questions about using MovingPandas or just want to discuss new ideas, you’re welcome to join our discussion forum.

Today’s post presents an experiment in modelling a common scenario in many IOT setups: time series of measurements at stationary sensors. The key idea I want to explore is to use MobilityDB’s temporal data types, in particular the tfloat_inst and tfloat_seq for instances and sequences of temporal float values, respectively.

For info on how to set up MobilityDB, please check my previous post.

Setting up our DB tables

As a toy example, let’s create two IOT devices (in table iot_devices) with three measurements each (in table iot_measurements) and join them to create the tfloat_seq (in table iot_joined):

CREATE TABLE iot_devices (
    id integer,
    geom geometry(Point, 4326)

INSERT INTO iot_devices (id, geom) VALUES
(1, ST_SetSRID(ST_MakePoint(1,1), 4326)),
(2, ST_SetSRID(ST_MakePoint(2,3), 4326));

CREATE TABLE iot_measurements (
    device_id integer,
    t timestamp,
    measurement float

INSERT INTO iot_measurements (device_id, t, measurement) VALUES
(1, '2022-10-01 12:00:00', 5.0),
(1, '2022-10-01 12:01:00', 6.0),
(1, '2022-10-01 12:02:00', 10.0),
(2, '2022-10-01 12:00:00', 9.0),
(2, '2022-10-01 12:01:00', 6.0),
(2, '2022-10-01 12:02:00', 1.5);

CREATE TABLE iot_joined AS
        tfloat_inst(m.measurement, m.t) ORDER BY t
    )) measurements
FROM iot_devices dev 
JOIN iot_measurements m
  ON = m.device_id
GROUP BY, dev.geom;

We can load the resulting layer in QGIS but QGIS won’t be happy about the measurements column because it does not recognize its data type:

Query layer with valueAtTimestamp

Instead, what we can do is create a query layer that fetches the measurement value at a specific timestamp:

SELECT id, geom, 
    valueAtTimestamp(measurements, '2022-10-01 12:02:00') 
FROM iot_joined

Which gives us a layer that QGIS is happy with:

Time for TemporalController

Now the tricky question is: how can we wire our query layer to the Temporal Controller so that we can control the timestamp and animate the layer?

I don’t have a GUI solution yet but here’s a way to do it with PyQGIS: whenever the Temporal Controller signal updateTemporalRange is emitted, our update_query_layer function gets the current time frame start time and replaces the datetime in the query layer’s data source with the current time:

l = iface.activeLayer()
tc = iface.mapCanvas().temporalController()

def update_query_layer():
    tct = tc.dateTimeRangeForFrameNumber(tc.currentFrameNumber()).begin().toPyDateTime()
    s = l.source()
    new = re.sub(r"(\d{4})-(\d{2})-(\d{2}) (\d{2}):(\d{2}):(\d{2})", str(tct), s)
    l.setDataSource(new, l.sourceName(), l.dataProvider().name())


Future experiments will have to show how this approach performs on lager datasets but it’s exciting to see how MobilityDB’s temporal types may be visualized in QGIS without having to create tables/views that join a geometry to each and every individual measurement.

It’s been a while since we last talked about MobilityDB in 2019 and 2020. Since then, the project has come a long way. It joined OSGeo as a community project and formed a first PSC, including the project founders Mahmoud Sakr and Esteban Zimányi as well as Vicky Vergara (of pgRouting fame) and yours truly.

This post is a quick teaser tutorial from zero to computing closest points of approach (CPAs) between trajectories using MobilityDB.

Setting up MobilityDB with Docker

The easiest way to get started with MobilityDB is to use the ready-made Docker container provided by the project. I’m using Docker and WSL (Windows Subsystem Linux on Windows 10) here. Installing WLS/Docker is out of scope of this post. Please refer to the official documentation for your operating system.

Once Docker is ready, we can pull the official container and fire it up:

docker pull mobilitydb/mobilitydb
docker volume create mobilitydb_data
docker run --name "mobilitydb" -d -p 25432:5432 -v mobilitydb_data:/var/lib/postgresql mobilitydb/mobilitydb
psql -h localhost -p 25432 -d mobilitydb -U docker

Currently, the container provides PostGIS 3.2 and MobilityDB 1.0:

Loading movement data into MobilityDB

Once the container is running, we can already connect to it from QGIS. This is my preferred way to load data into MobilityDB because we can simply drag-and-drop any timestamped point layer into the database:

For this post, I’m using an AIS data sample in the region of Gothenburg, Sweden.

After loading this data into a new table called ais, it is necessary to remove duplicate and convert timestamps:

FROM ais;

ALTER TABLE AISInputFiltered ADD COLUMN t timestamp;
UPDATE AISInputFiltered SET t = "Timestamp"::timestamp;

Afterwards, we can create the MobilityDB trajectories:

tgeompoint_seq(array_agg(tgeompoint_inst(Geom, t) ORDER BY t)) AS Trip,
tfloat_seq(array_agg(tfloat_inst("SOG", t) ORDER BY t) FILTER (WHERE "SOG" IS NOT NULL) ) AS SOG,
tfloat_seq(array_agg(tfloat_inst("COG", t) ORDER BY t) FILTER (WHERE "COG" IS NOT NULL) ) AS COG
FROM AISInputFiltered

ALTER TABLE Ships ADD COLUMN Traj geometry;
UPDATE Ships SET Traj = trajectory(Trip);

Once this is done, we can load the resulting Ships layer and the trajectories will be loaded as lines:

Computing closest points of approach

To compute the closest point of approach between two moving objects, MobilityDB provides a shortestLine function. To be correct, this function computes the line connecting the nearest approach point between the two tgeompoint_seq. In addition, we can use the time-weighted average function twavg to compute representative average movement speeds and eliminate stationary or very slowly moving objects:

SELECT S1.MMSI mmsi1, S2.MMSI mmsi2, 
       shortestLine(S1.trip, S2.trip) Approach,
       ST_Length(shortestLine(S1.trip, S2.trip)) distance
FROM Ships S1, Ships S2
twavg(S1.SOG) > 1 AND twavg(S2.SOG) > 1 AND
dwithin(S1.trip, S2.trip, 0.003)

In the QGIS Browser panel, we can right-click the MobilityDB connection to bring up an SQL input using Execute SQL:

The resulting query layer shows where moving objects get close to each other:

To better see what’s going on, we’ll look at individual CPAs:

Having a closer look with the Temporal Controller

Since our filtered AIS layer has proper timestamps, we can animate it using the Temporal Controller. This enables us to replay the movement and see what was going on in a certain time frame.

I let the animation run and stopped it once I spotted a close encounter. Looking at the AIS points and the shortest line, we can see that MobilityDB computed the CPAs along the trajectories:

A more targeted way to investigate a specific CPA is to use the Temporal Controllers’ fixed temporal range mode to jump to a specific time frame. This is helpful if we already know the time frame we are interested in. For the CPA use case, this means that we can look up the timestamp of a nearby AIS position and set up the Temporal Controller accordingly:


I hope you enjoyed this quick dive into MobilityDB. For more details, including talks by the project founders, check out the project website.

This post is part of a series. Read more about movement data in GIS.

Cartographers use all kind of tricks to make their maps look deceptively simple. Yet, anyone who has ever tried to reproduce a cartographer’s design using only automatic GIS styling and labeling knows that the devil is in the details.

This post was motivated by Mika Hall’s retro map style.

There are a lot of things going on in this design but I want to draw your attention to the labels – and particularly their background:

Detail of Mike’s map (c) Mike Hall. You can see that the rail lines stop right before they would touch the A in Valencia (or any other letters in the surrounding labels).

This kind of effect cannot be achieved by good old label buffers because no matter which color we choose for the buffer, there will always be cases when the chosen color is not ideal, for example, when some labels are on land and some over water:

Ordinary label buffers are not always ideal.

Label masks to the rescue!

Selective label masks enable more advanced designs.

Here’s how it’s done:

Selective masking has actually been around since QGIS 3.12. There are two things we need to take care of when setting up label masks:

1. First we need to enable masks in the label settings for all labels we want to mask (for example the city labels). The mask tab is conveniently located right next to the label buffer tab:

2. Then we can go to the layers we want to apply the masks to (for example the railroads layer). Here we can configure which symbol layers should be affected by which mask:

Note: The order of steps is important here since the “Mask sources” list will be empty as long as we don’t have any label masks enabled and there is currently no help text explaining this fact.

I’m also using label masks to keep the inside of the large city markers (the ones with a star inside a circle) clear of visual clutter. In short, I’m putting a circle-shaped character, such as ◍, over the city location:

In the text tab, we can specify our one-character label and – later on – set the label opacity to zero.
To ensure that the label stays in place, pick the center placement in “Offset from Point” mode.

Once we are happy with the size and placement of this label, we can then reduce the label’s opacity to 0, enable masks, and configure the railroads layer to use this mask.

As a general rule of thumb, it makes sense to apply the masks to dark background features such as the railways, rivers, and lake outlines in our map design:

Resulting map with label masks applied to multiple labels including city and marine area labels masking out railway lines and ferry connections as well as rivers and lake outlines.

If you have never used label masks before, I strongly encourage you to give them a try next time you work on a map for public consumption because they provide this little extra touch that is often missing from GIS maps.

Happy QGISing! Make maps not war.

The latest v0.11 release is now available from conda-forge.

This release contains some really cool new algorithms:

  • New minimum and Hausdorff distance measures #37
  • New functions to add a timedelta column and get the trajectory sampling interval #233 

As always, all tutorials are available from the movingpandas-examples repository and on MyBinder:

The new distance measures are covered in tutorial #11:

Computing distances between trajectories, as illustrated in tutorial #11

Computing distances between a trajectory and other geometry objects, as illustrated in tutorial #11

But don’t miss the great features covered by the other notebooks, such as outlier cleaning and smoothing:

Trajectory cleaning and smoothing, as illustrated in tutorial #10

If you have questions about using MovingPandas or just want to discuss new ideas, you’re welcome to join our discussion forum.

The BEV (Austrian Bundesamt für Eich- und Vermessungswesen) has recently published the Austrian cadastre as open data:

The URLs for vector tiles and styles can be found on under Guide – External

The vector tile URL is:{kataster | symbole}/{z}/{x}/{y}.pbf

There are 4 different style variations:{kataster | symbole}/style_{vermv | ortho | basic | gis}.json

When configuring the vector tiles in QGIS, we specify the desired tile and style URLs, for example:

For example, this is the “gis” style:

And this is the “basic” style:

The second vector tile source I want to mention is It has been around for a while, however, early versions suffered from a couple of issues that have now been resolved.

The project provides extensive documentation on how to use the dataset in QGIS and other GIS, including manuals and sample projects:

Here’s the basic configuration: make sure to set the max zoom level to 16, otherwise, the map will not be rendered when you zoom in too far.

The level of detail is pretty impressive, even if it cannot quite keep up with the basemap raster tiles:

Vector tile details at Resselpark, Vienna
Raster basemap details at Resselpark, Vienna

The latest v0.10 release is now available from conda-forge.

This release contains some really cool new algorithms:

If you have questions about using MovingPandas or just want to discuss new ideas, you’re welcome to join our recently opened discussion forum.

As always, all tutorials are available from the movingpandas-examples repository and on MyBinder:

Besides others examples, the movingpandas-examples repo contains the following tech demo: an interactive app built with Panel that demonstrates different MovingPandas stop detection parameters

To start the app, open the stopdetection-app.ipynb notebook and press the green Panel button in the Jupyter Lab toolbar:

This post aims to show you how to create quick interactive apps for prototyping and data exploration using Panel.

Specifically, the following example demos how to add geocoding functionality based on Geopy and Nominatim. As such, this example brings together tools we’ve previously touched on in Super-quick interactive data & parameter exploration and Geocoding with Geopy.

Here’s a quick preview of the resulting app in action:

To create this app, I defined a single function called my_plot which takes the address and desired buffer size as input parameters. Using Panel’s interact and servable methods, I’m then turning this function into the interactive app you’ve seen above:

import panel as pn
from geopy.geocoders import Nominatim
from utils.converting import location_to_gdf
from utils.plotting import hvplot_with_buffer

locator = Nominatim(user_agent="OGD.AT-Lab")

def my_plot(user_input="Giefinggasse 2, 1210 Wien", buffer_meters=1000):
    location = locator.geocode(user_input)
    geocoded_gdf = location_to_gdf(location, user_input)
    map_plot = hvplot_with_buffer(geocoded_gdf, buffer_meters, 
                                  title=f'Geocoded address with {buffer_meters}m buffer')
    return map_plot.opts(active_tools=['wheel_zoom']) 

kw = dict(user_input="Giefinggasse 2, 1210 Wien", buffer_meters=(0,10000))

    site="Panel", title="Geocoding Demo", 
    main=[pn.interact(my_plot, **kw)]

You can find the full notebook in the OGD.AT Lab repository or run this notebook directly on MyBinder:

To open the Panel preview, press the green Panel button in the Jupyter Lab toolbar:

I really enjoy building spatial data exploration apps this way, because I can start off with a Jupyter notebook and – once I’m happy with the functionality – turn it into a pretty app that provides a user-friendly exterior and hides the underlying complexity that might scare away stakeholders.

Give it a try and share your own adventures. I’d love to see what you come up with.

Today, I’m revisiting work from 2017. In Brezina, Graser & Leth (2017), we looked at different ways to determine the width of sidewalks in Vienna based on the city’s street surface database.

Image source: Brezina, Graser & Leth (2017)

Inscribed and circumscribed circles were a natural starting point. Circumscribed or bounding circle tools (the smallest circle to enclose an input polygon) have been commonly available in desktop GIS and spatial databases. Inscribed circle tools (the largest circle that fits into an input polygon) used to be less readily available. Lately, support has improved since ST_MaximumInscribedCircle has been added in PostGIS 3.1.0 (requires GEOS >= 3.9.0).

The tricky thing is that ST_MaximumInscribedCircle does not behave like ST_MinimumBoundingCircle. While the bounding circle function returns the circle geometry, the inscribed circle function returns a record containing information on the circle center and radius. Handling the resulting records involves some not so intuitive SQL.

Here is what I’ve come up with to get both the circle geometries as well as the radius values:

WITH foo AS 
	SELECT id, 
		ST_MaximumInscribedCircle(geom) AS inscribed_circle,
		ST_MinimumBoundingRadius(geom) AS bounding_circle
	FROM demo.sidewalks 
	(bounding_circle).radius AS bounding_circle_radius,
	ST_MinimumBoundingCircle(geom) AS bounding_circle_geom, 
	(inscribed_circle).radius AS inscribed_circle_radius,
	ST_Buffer((inscribed_circle).center, (inscribed_circle).radius) AS inscribed_circle_geom
FROM foo

And here is how the results look like in QGIS, with purple shapeburst fills for bounding circles and green shapeburst fills for inscribed circles:


Brezina, T., Graser, A., & Leth, U. (2017). Geometric methods for estimating representative sidewalk widths applied to Vienna’s streetscape surfaces database. Journal of Geographical Systems, 19(2), 157-174, doi:10.1007/s10109-017-0245-2.

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