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QGIS Server — Docker edition

Today’s post is a QGIS Server update. It’s been a while (12 years 😵) since I last posted about QGIS Server. It would be an understatement to say that things have evolved since then, not least due to the development of Docker which, Wikipedia tells me, was released 11 years ago.

There have been multiple Docker images for QGIS Server provided by QGIS Community members over the years. Recently,’s Docker image has been adopted as official QGIS Server image which aims to be a starting point for users to develop their own customized applications.

The following steps have been tested on Ubuntu (both native and in WSL).

First, we need Docker. I installed Docker from the apt repository as described in the official docs.

Once Docker is set up, we can get the QGIS Server, e.g. for the LTR:

docker pull qgis/qgis-server:ltr

Now we only need to start it:

docker run -v $(pwd)/qgis-server-data:/io/data --name qgis-server -d -p 8010:80 qgis/qgis-server:ltr

Note how we are mapping the qgis-server-data directory in our current working directory to /io/data in the container. This is where we’ll put our QGIS project files.

We can already check out the OGC API landing page at http://localhost:8010/wfs3/

Let’s add a sample project from the Training demo data repo. (You may need to install unzip if you haven’t yet.)

mkdir qgis-server-data
cd qgis-server-data
mkdir world
cp QGIS-Training-Data-release_3.22/exercise_data/qgis-server-tutorial-data/world.qgs world/
cp QGIS-Training-Data-release_3.22/exercise_data/qgis-server-tutorial-data/naturalearth.sqlite world

Giving us:

QGIS Server should now be serving this sample project. Let’s check with a WMS GetMap request:


Giving us:

If you instead get the error “<ServerException>Project file error. For OWS services: please provide a SERVICE and a MAP parameter pointing to a valid QGIS project file</ServerException>”, it probably means that the world.qgs file is not found in the qgis-server-data/world directory.

Of course, we can also add http://localhost:8010/ogc/world to the WMS and WFS server connections in our QGIS Desktop:

Getting started with pygeoapi processes

Today’s post is a quick introduction to pygeoapi, a Python server implementation of the OGC API suite of standards. OGC API provides many different standards but I’m particularly interested in OGC API – Processes which standardizes geospatial data processing functionality. pygeoapi implements this standard by providing a plugin architecture, thereby allowing developers to implement custom processing workflows in Python.

I’ll provide instructions for setting up and running pygeoapi on Windows using Powershell. The official docs show how to do this on Linux systems. The pygeoapi homepage prominently features instructions for installing the dev version. For first experiments, however, I’d recommend using a release version instead. So that’s what we’ll do here.

As a first step, lets install the latest release (0.16.1 at the time of writing) from conda-forge:

conda create -n pygeoapi python=3.10
conda activate pygeoapi
mamba install -c conda-forge pygeoapi

Next, we’ll clone the GitHub repo to get the example config and datasets:

cd C:\Users\anita\Documents\GitHub\
git clone
cd pygeoapi\

To finish the setup, we need some configurations:

cp pygeoapi-config.yml example-config.yml  
# There is a known issue in pygeoapi 0.16.1:
# To fix it, edit the example-config.yml: uncomment the TinyDB option in the server settings (lines 51-54)

$Env:PYGEOAPI_CONFIG = "F:/Documents/GitHub/pygeoapi/example-config.yml"
$Env:PYGEOAPI_OPENAPI = "F:/Documents/GitHub/pygeoapi/example-openapi.yml"
pygeoapi openapi generate $Env:PYGEOAPI_CONFIG --output-file $Env:PYGEOAPI_OPENAPI

Now we can start the server:

pygeoapi serve

And once the server is running, we can send requests, e.g. the list of processes:

curl.exe http://localhost:5000/processes

And, of course, execute the example “hello-world” process:

curl.exe --% -X POST http://localhost:5000/processes/hello-world/execution -H "Content-Type: application/json" -d "{\"inputs\":{\"name\": \"hi there\"}}"

As you can see, writing JSON content for curl is a pain. Luckily, pyopenapi comes with a nice web GUI, including Swagger UI for playing with all the functionality, including the hello-world process:

It’s not really a geospatial hello-world example, but it’s a first step.

Finally, I wan’t to leave you with a teaser since there are more interesting things going on in this space, including work on OGC API – Moving Features as shared by the pygeoapi team recently:

So, stay tuned.

Trajectools 2.0 released 🎉

It’s my pleasure to share with you that Trajectools 2.0 just landed in the official QGIS Plugin Repository.

This is the first version without the “experimental” flag. If you look at the plugin release history, you will see that the previous release was from 2020. That’s quite a while ago and a lot has happened since, including the development of MovingPandas.

Let’s have a look what’s new!

The old “Trajectories from point layer”, “Add heading to points”, and “Add speed (m/s) to points” algorithms have been superseded by the new “Create trajectories” algorithm which automatically computes speeds and headings when creating the trajectory outputs.

“Day trajectories from point layer” is covered by the new “Split trajectories at time intervals” which supports splitting by hour, day, month, and year.

“Clip trajectories by extent” still exists but, additionally, we can now also “Clip trajectories by polygon layer”

There are two new event extraction algorithms to “Extract OD points” and “Extract OD points”, as well as the related “Split trajectories at stops”. Additionally, we can also “Split trajectories at observation gaps”.

Trajectory outputs, by default, come as a pair of a point layer and a line layer. Depending on your use case, you can use both or pick just one of them. By default, the line layer is styled with a gradient line that makes it easy to see the movement direction:

while the default point layer style shows the movement speed:

How to use Trajectools

Trajectools 2.0 is powered by MovingPandas. You will need to install MovingPandas in your QGIS Python environment. I recommend installing both QGIS and MovingPandas from conda-forge:

(base) conda create -n qgis -c conda-forge python=3.9 
(base) conda activate qgis
(qgis) mamba install -c conda-forge qgis movingpandas

The plugin download includes small trajectory sample datasets so you can get started immediately.


There is still some work to do to reach feature parity with MovingPandas. Stay tuned for more trajectory algorithms, including but not limited to down-sampling, smoothing, and outlier cleaning.

I’m also reviewing other existing QGIS plugins to see how they can complement each other. If you know a plugin I should look into, please leave a note in the comments.

Finding geospatial accounts on Mastodon

Besides following hashtags, such as #GISChat, #QGIS, #OpenStreetMap, #FOSS4G, and #OSGeo, curating good lists is probably the best way to stay up to date with geospatial developments.

To get you started (or to potentially enrich your existing lists), I thought I’d share my Geospatial and SpatialDataScience lists with you. And the best thing: you don’t need to go through all the >150 entries manually! Instead, go to your Mastodon account settings and under “Import and export” you’ll find a tool to import and merge my list.csv with your lists:

And if you are not following the geospatial hashtags yet, you can search or click on the hashtags you’re interested in and start following to get all tagged posts into your timeline:

Trajectools update: stop detection & trajectory styling

The Trajectools toolbox has continued growing:

I’m continuously testing the algorithms integrated so far to see if they work as GIS users would expect and can to ensure that they can be integrated in Processing model seamlessly.

Because naming things is tricky, I’m currently struggling with how to best group the toolbox algorithms into meaningful categories. I looked into the categories mentioned in OGC Moving Features Access but honestly found them kind of lacking:

Andrienko et al.’s book “Visual Analytics of Movement” comes closer to what I’m looking for:

… but I’m not convinced yet. So take the above listed three categories with a grain of salt. Those may change before the release. (Any inputs / feedback / recommendation welcome!)

Let me close this quick status update with a screencast showcasing stop detection in AIS data, featuring the recently added trajectory styling using interpolated lines:

While Trajectools is getting ready for its 2.0 release, you can get the current development version directly from

QGIS Processing Trajectools v2 in the works

Trajectools development started back in 2018 but has been on hold since 2020 when I realized that it would be necessary to first develop a solid trajectory analysis library. With the MovingPandas library in place, I’ve now started to reboot Trajectools.

Trajectools v2 builds on MovingPandas and exposes its trajectory analysis algorithms in the QGIS Processing Toolbox. So far, I have integrated the basic steps of

  1. Building trajectories including speed and direction information from timestamped points and
  2. Splitting trajectories at observation gaps, stops, or regular time intervals.

The algorithms create two output layers:

  • Trajectory points with speed and direction information that are styled using arrow markers
  • Trajectories as LineStringMs which makes it straightforward to count the number of trajectories and to visualize where one trajectory ends and another starts.

So far, the default style for the trajectory points is hard-coded to apply the Turbo color ramp on the speed column with values from 0 to 50 (since I’m simply loading a ready-made QML). By default, the speed is calculated as km/h but that can be customized:

I don’t have a solution yet to automatically create a style for the trajectory lines layer. Ideally, the style should be a categorized renderer that assigns random colors based on the trajectory id column. But in this case, it’s not enough to just load a QML.

In the meantime, I might instead include an Interpolated Line style. What do you think?

Of course, the goal is to make Trajectools interoperable with as many existing QGIS Processing Toolbox algorithms as possible to enable efficient Mobility Data Science workflows.

The easiest way to set up QGIS with MovingPandas Python environment is to install both from conda. You can find the instructions together with the latest Trajectools development version at:

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

Offline Vector Tile Package .vtpk in QGIS

Starting from 3.26, QGIS now supports .vtpk (Vector Tile Package) files out of the box! From the changelog:

ESRI vector tile packages (VTPK files) can now be opened directly as vector tile layers via drag and drop, including support for style translation.

This is great news, particularly for users from Austria, since this makes it possible to use the open government vector tiles directly, without any fuss:

1. Download the 2GB offline vector basemap from

2. Add the .vtpk as a layer using the Data Source Manager or via drag-and-drop from the file explorer

3. All done and ready, including the basemap styling and labeling — which we can customize as well:

Kudos to for bringing this new feature to my attention.

PS: And interesting tidbit from the developer of this feature, Nyall Dawson:

Hi ‘Geocomputation with Python’

Today, I want to point out a blog post over at

In this post, Jakub Nowosad introduces our book “Geocomputation with Python”, also known as geocompy. It is an open-source book on geographic data analysis with Python, written by Michael Dorman, Jakub Nowosad, Robin Lovelace, and me with contributions from others. You can find it online at

Mapping relationships between Neo4j spatial nodes with GeoPandas

Previously, we mapped neo4j spatial nodes. This time, we want to take it one step further and map relationships.

A prime example, are the relationships between GTFS StopTime and Trip nodes. For example, this is the Cypher query to get all StopTime nodes of Trip 17:

    (t:Trip  {id: "17"})

To get the stop locations, we also need to get the stop nodes:

    (t:Trip {id: "17"})
RETURN st ,s

Adapting our code from the previous post, we can plot the stops:

from shapely.geometry import Point

    t:Trip {id: "17"})
RETURN st ,s
ORDER BY st.stopSequence

with driver.session(database="neo4j") as session:
    tx = session.begin_transaction()
    results =
    df = results.to_df(expand=True)
    gdf = gpd.GeoDataFrame(
        df[['s()']], crs=4326,

m = gdf.explore()

Ordering by stop sequence is actually completely optional. Technically, we could use the sorted GeoDataFrame, and aggregate all the points into a linestring to plot the route. But I want to try something different: we’ll use the NEXT_STOP relationships to get a DataFrame of the start and end stops for each segment:

QUERY = """
MATCH (t:Trip {id: "17"})
MATCH (st1)-[:STOPS_AT]->(s1:Stop)
MATCH (st2)-[:STOPS_AT]->(s2:Stop)
RETURN st1, st2, s1, s2

from shapely.geometry import Point, LineString

def make_line(row):
    s1 = Point(row["s1().prop.location"])
    s2 = Point(row["s2().prop.location"])
    return LineString([s1,s2])

with driver.session(database="neo4j") as session:
    tx = session.begin_transaction()
    results =
    df = results.to_df(expand=True)
    gdf = gpd.GeoDataFrame(
        df[['s1()']], crs=4326,
        geometry=df.apply(make_line, axis=1)


Finally, we can also use Cypher to calculate the travel time between two stops:

MATCH (t:Trip {id: "17"})
MATCH (st1)-[:STOPS_AT]->(s1:Stop)
MATCH (st2)-[:STOPS_AT]->(s2:Stop)
RETURN st1.departureTime AS time1, 
   st2.arrivalTime AS time2, 
   s1.location AS geom1, 
   s2.location AS geom2, 
   ).seconds AS traveltime

As always, here’s the notebook:

Mapping Neo4j spatial nodes with GeoPandas

In the recent post Setting up a graph db using GTFS data & Neo4J, we noted that — unfortunately — Neomap is not an option to visualize spatial nodes anymore.

GeoPandas to the rescue!

But first we need the neo4j Python driver:

pip install neo4j

Then we can connect to our database. The default user name is neo4j and you get to pick the password when creating the database:

from neo4j import GraphDatabase

URI = "neo4j://localhost"
AUTH = ("neo4j", "password")

with GraphDatabase.driver(URI, auth=AUTH) as driver:

Once we have confirmed that the connection works as expected, we can run a query:

QUERY = "MATCH (p:Stop) RETURN AS name, p.location AS geom"

records, summary, keys = driver.execute_query(
    QUERY, database_="neo4j",

for rec in records:

Nice. There we have our GTFS stops, their names and their locations. But how to put them on a map?

Conveniently, there is a to_db() function in the Neo4j driver:

import geopandas as gpd
import numpy as np

with driver.session(database="neo4j") as session:
    tx = session.begin_transaction()
    results =
    df = results.to_df(expand=True)
    df = df[df["geom[].0"]>0]
    gdf = gpd.GeoDataFrame(
        df['name'], crs=4326,
        geometry=gpd.points_from_xy(df['geom[].0'], df['geom[].1']))


Since some of the nodes lack geometries, I added a quick and dirty hack to get rid of these nodes because — otherwise — gdf.explore() will complain about None geometries.

You can find this notebook at:

Next step will have to be the relationships. Stay posted.

Analyzing mobility hotspots with MovingPandas & CARTO

Today, I want to point out a blog post over at

written together with my fellow co-authors and EMERALDS project team members Argyrios Kyrgiazos and Helen McKenzie.

In this blog post, we walk you through a trajectory hotspot analysis using open taxi trajectory data from Kaggle, combining data preparation with MovingPandas (including the new OutlierCleaner illustrated above) and spatiotemporal hotspot analysis from Carto.

Setting up a graph db using GTFS data & Neo4J

In a recent post, we looked into a graph-based model for maritime mobility data and how it may be represented in Neo4J. Today, I want to look into another type of mobility data: public transport schedules in GTFS format.

In this post, I’ll be using the public GTFS data for Riga since Riga is one of the demo sites for our current EMERALDS research project.

The workflow is heavily inspired by Bert Radke‘s post “Loading the UK GTFS data feed” from 2021 and his import Cypher script which I used as a template, adjusted to the requirements of the Riga dataset, and updated to recent Neo4J changes.

Here we go.

Since a GTFS export is basically a ZIP archive full of CSVs, we will be making good use of Neo4Js CSV loading capabilities. The basic script for importing the stops file and creating point geometries from lat and lon values would be:

LOAD CSV with headers 
FROM "file:///stops.txt" 
AS row 
CREATE (:Stop {
   stop_id: row["stop_id"],
   name: row["stop_name"], 
   location: point({
    longitude: toFloat(row["stop_lon"]),
    latitude: toFloat(row["stop_lat"])

This requires that the stops.txt is located in the import directory of your Neo4J database. When we run the above script and the file is missing, Neo4J will tell us where it tried to look for it. In my case, the directory ended up being:


So, let’s put all GTFS CSVs into that directory and we should be good to go.

Let’s start with the agency file:

load csv with headers from
'file:///agency.txt' as row
create (a:Agency {
   id: row.agency_id, 
   name: row.agency_name, 
   url: row.agency_url, 
   timezone: row.agency_timezone, 
   lang: row.agency_lang

… Added 1 label, created 1 node, set 5 properties, completed after 31 ms.

The routes file does not include agency info but, luckily, there is only one agency, so we can hard-code it:

load csv with headers from
'file:///routes.txt' as row
match (a:Agency {id: "rigassatiksme"})
create (a)-[:OPERATES]->(r:Route {
   id: row.route_id, 
   shortName: row.route_short_name,
   longName: row.route_long_name, 
   type: toInteger(row.route_type)

… Added 81 labels, created 81 nodes, set 324 properties, created 81 relationships, completed after 28 ms.

From stops, I’m removing non-existent or empty columns:

load csv with headers from
'file:///stops.txt' as row
create (s:Stop {
   id: row.stop_id, 
   name: row.stop_name, 
   location: point({
      latitude: toFloat(row.stop_lat), 
      longitude: toFloat(row.stop_lon)
   code: row.stop_code

… Added 1671 labels, created 1671 nodes, set 5013 properties, completed after 71 ms.

From trips, I’m also removing non-existent or empty columns:

load csv with headers from
'file:///trips.txt' as row
match (r:Route {id: row.route_id})
create (r)<-[:USES]-(t:Trip {
   id: row.trip_id, 
   serviceId: row.service_id,
   headSign: row.trip_headsign, 
   direction_id: toInteger(row.direction_id),
   blockId: row.block_id,
   shapeId: row.shape_id

… Added 14427 labels, created 14427 nodes, set 86562 properties, created 14427 relationships, completed after 875 ms.

Slowly getting there. We now have around 16k nodes in our graph:

Finally, it’s stop times time. This is where the serious information is. This file is much larger than all previous ones with over 300k lines (i.e. times when an PT vehicle stops).

This requires another tweak to Bert’s script since using periodic commit is not supported anymore: The PERIODIC COMMIT query hint is no longer supported. Please use CALL { … } IN TRANSACTIONS instead. So I ended up using the following, based on

load csv with headers from
'file:///stop_times.txt' as row
CALL { with row
match (t:Trip {id: row.trip_id}), (s:Stop {id: row.stop_id})
create (t)<-[:BELONGS_TO]-(st:StopTime {
   arrivalTime: row.arrival_time, 
   departureTime: row.departure_time,
   stopSequence: toInteger(row.stop_sequence)})-[:STOPS_AT]->(s)

… Added 351388 labels, created 351388 nodes, set 1054164 properties, created 702776 relationships, completed after 1364220 ms.

As you can see, this took a while. But now we have all nodes in place:

The final statement adds additional relationships between consecutive stop times:

call apoc.periodic.iterate('match (t:Trip) return t',
'match (t)<-[:BELONGS_TO]-(st) with st order by st.stopSequence asc
with collect(st) as stops
unwind range(0, size(stops)-2) as i
with stops[i] as curr, stops[i+1] as next
merge (curr)-[:NEXT_STOP]->(next)', {batchmode: "BATCH", parallel:true, parallel:true, batchSize:1});

This fails with: There is no procedure with the name apoc.periodic.iterate registered for this database instance. Please ensure you've spelled the procedure name correctly and that the procedure is properly deployed.

So, let’s install APOC. That’s a plugin which we can install into our database from within Neo4J Desktop:

After restarting the db, we can run the query:

No errors. Sounds good.

Let’s have a look at what we ended up with. Here are 25 random Trips. I expanded one of them to show its associated StopTimes. We can see the relations between consecutive StopTimes and I’ve expanded the final five StopTimes to show their linked Stops:

I also wanted to visualize the stops on a map. And there used to be a neat app called Neomap which can be installed easily:

However, Neomap does not seem to be compatible with the latest Neo4J:

So this final step will have to wait for another time.

MovingPandas v0.17 released!

Over the last couple of months, I have not been posting release announcements here, so there is quite a bit to catch up.

The latest v0.17.2 release is now available from conda-forge.

New features (since 0.14):

  • Improved MovingFeatures MF-JSON support
    • Ability to parse a MovingFeatureCollection from a json file #330
    • GeoDataFrame to MF-JSON #325
    • Adding read_mf_dict function #357
  • New OutlierCleaner #334
  • Faster stop detection #316
  • New arrow markers to indicate trajectory direction in plots fb1174b 
  • Distance, speed, and acceleration unit handling #295
  • New aggregation parameter (agg) for to_traj_gdf() 5745068 
  • New get_segments_between() for TrajectoryCollection #287 

Behind the scenes:

  • We now have a dedicated Github organization: that houses all related repositories
  • And we finally added https support to the website

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.

Adding basemaps to PyQGIS maps in Jupyter notebooks

In the previous post, we investigated how to bring QGIS maps into Jupyter notebooks.

Today, we’ll take the next step and add basemaps to our maps. This is trickier than I would have expected. In particular, I was fighting with “invalid” OSM tile layers until I realized that my QGIS application instance somehow lacked the “WMS” provider.

In addition, getting basemaps to work also means that we have to take care of layer and project CRSes and on-the-fly reprojections. So let’s get to work:

from IPython.display import Image
from PyQt5.QtGui import QColor
from PyQt5.QtWidgets import QApplication
from qgis.core import QgsApplication, QgsVectorLayer, QgsProject, QgsRasterLayer, \
    QgsCoordinateReferenceSystem, QgsProviderRegistry, QgsSimpleMarkerSymbolLayerBase
from qgis.gui import QgsMapCanvas
app = QApplication([])
qgs = QgsApplication([], False)
qgs.setPrefixPath(r"C:\temp", True)  # setting a prefix path should enable the WMS provider
canvas = QgsMapCanvas()
project = QgsProject.instance()
map_crs = QgsCoordinateReferenceSystem('EPSG:3857')
print("providers: ", QgsProviderRegistry.instance().providerList())

To add an OSM basemap, we use the xyz tiles option of the WMS provider:

urlWithParams = 'type=xyz&url={z}/{x}/{y}.png&zmax=19&zmin=0&crs=EPSG3857'
rlayer = QgsRasterLayer(urlWithParams, 'OpenStreetMap', 'wms')  
if rlayer.isValid():
    print('invalid layer')

If there are issues with the WMS provider, rlayer.error().summary() should point them out.

With both the vector layer and the basemap ready, we can finally plot the map:


Of course, we can get more creative and style our vector layers:


And to switch to other basemaps, we just need to update the URL accordingly, for example, to load Carto tiles instead:

urlWithParams = 'type=xyz&url={z}/{x}/{y}.png&zmax=19&zmin=0&crs=EPSG3857'
rlayer2 = QgsRasterLayer(urlWithParams, 'Carto', 'wms')  
if rlayer2.isValid():
    print('invalid layer')

You can find the whole notebook at:

Bringing QGIS maps into Jupyter notebooks

Earlier this year, we explored how to use PyQGIS in Juypter notebooks to run QGIS Processing tools from a notebook and visualize the Processing results using GeoPandas plots.

Today, we’ll go a step further and replace the GeoPandas plots with maps rendered by QGIS.

The following script presents a minimum solution to this challenge: initializing a QGIS application, canvas, and project; then loading a GeoJSON and displaying it:

from IPython.display import Image

from PyQt5.QtGui import QColor
from PyQt5.QtWidgets import QApplication

from qgis.core import QgsApplication, QgsVectorLayer, QgsProject, QgsSymbol, \
    QgsRendererRange, QgsGraduatedSymbolRenderer, \
    QgsArrowSymbolLayer, QgsLineSymbol, QgsSingleSymbolRenderer, \
    QgsSymbolLayer, QgsProperty
from qgis.gui import QgsMapCanvas

app = QApplication([])
qgs = QgsApplication([], False)
canvas = QgsMapCanvas()
project = QgsProject.instance()

vlayer = QgsVectorLayer("./data/traj.geojson", "My trajectory")
if not vlayer.isValid():
    print("Layer failed to load!")

def saveImage(path, show=True): 
    if show: return Image(path)



When this code is executed, it opens a separate window that displays the map canvas. And in this window, we can even pan and zoom to adjust the map. The line color, however, is assigned randomly (like when we open a new layer in QGIS):

To specify a specific color, we can use:



But regular lines are boring. We could easily create those with GeoPandas plots.

Things get way more interesting when we use QGIS’ custom symbols and renderers. For example, to draw arrows using a QgsArrowSymbolLayer, we can write:



We can also create a QgsGraduatedSymbolRenderer:

geom_type = vlayer.geometryType()
myRangeList = []

symbol = QgsSymbol.defaultSymbol(geom_type)
myRange = QgsRendererRange(0, 1, symbol, 'Group 1')

symbol = QgsSymbol.defaultSymbol(geom_type)
myRange = QgsRendererRange(1, 3, symbol, 'Group 2')

myRenderer = QgsGraduatedSymbolRenderer('speed', myRangeList)


And we can combine both QgsGraduatedSymbolRenderer and QgsArrowSymbolLayer:

geom_type = vlayer.geometryType()
myRangeList = []

symbol = QgsSymbol.defaultSymbol(geom_type)
myRange = QgsRendererRange(0, 1, symbol, 'Group 1')

symbol = QgsSymbol.defaultSymbol(geom_type)
myRange = QgsRendererRange(1, 3, symbol, 'Group 2')

myRenderer = QgsGraduatedSymbolRenderer('speed', myRangeList)


Maybe the most powerful option is to use data-defined symbology. For example, to control line width and color:

renderer = QgsSingleSymbolRenderer(QgsSymbol.defaultSymbol(geom_type))

exp_width = 'scale_linear("speed", 0, 3, 0, 7)'
exp_color = "coalesce(ramp_color('Viridis',scale_linear(\"speed\", 0, 3, 0, 1)), '#000000')"

    QgsSymbolLayer.PropertyStrokeWidth, QgsProperty.fromExpression(exp_width))
    QgsSymbolLayer.PropertyStrokeColor, QgsProperty.fromExpression(exp_color))
    QgsSymbolLayer.PropertyCapStyle, QgsProperty.fromExpression("'round'"))



Find the full notebook at:

Exploring a hierarchical graph-based model for mobility data representation and analysis

Today’s post is a first quick dive into Neo4J (really just getting my toes wet). It’s based on a publicly available Neo4J dump containing mobility data, ship trajectories to be specific. You can find this data and the setup instructions at:

Maryam Maslek ELayam, Cyril Ray, & Christophe Claramunt. (2022). A hierarchical graph-based model for mobility data representation and analysis [Data set]. Zenodo.

I was made aware of this work since they cited MovingPandas in their paper in Data & Knowledge Engineering“The implementation combines several open source tools such as Python, MovingPandas library, Uber H3 index, Neo4j graph database management system”

Once set up, this gives us a database with three hierarchical levels:

Neo4j comes with a nice graphical browser that lets us explore the data. We can switch between levels and click on individual node labels to get a quick preview:

Level 2 is a generalization / aggregation of level 1. Expanding the graph of one of the level 2 nodes shows its connection to level 1. For example, the level 2 port node “Audierne” actually refers to two level 1 nodes:

Every “road” level 1 relationship between ports provide information about the ship, its arrival, departure, travel time, and speed. We can see that this two level 1 ports must be pretty close since travel times are only 5 minutes:

Further expanding one of the port level 1 nodes shows its connection to waypoints of level1:

Switching to level 2, we gain access to nodes of type Traj(ectory). Additionally, the road level 2 relationships represent aggregations of the trajectories, for example, here’s a relationship with only one associated trajectory:

There are also some odd relationships, for example, trajectory 43 has two ends and begins relationships and there are also two road relationships referencing this trajectory (with identical information, only differing in their automatic <id>). I’m not yet sure if that is a feature or a bug:

On level 1, we also have access to ship nodes. They are connected to ports and waypoints. However, exploring them visually is challenging. Things look fine at first:

But after a while, once all relationships have loaded, we have it: the MIGHTY BALL OF YARN ™:

I guess this is the point where it becomes necessary to get accustomed to the query language. And no, it’s not SQL, it is Cypher. For example, selecting a specific trajectory with id 0, looks like this:

 MATCH (t1 {traj_id: 0}) RETURN t1

But more on this another time.

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

Data engineering for Mobility Data Science (with Python and DVC)

This summer, I had the honor to — once again — speak at the OpenGeoHub Summer School. This time, I wanted to challenge the students and myself by not just doing MovingPandas but by introducing both MovingPandas and DVC for Mobility Data Science.

I’ve previously written about DVC and how it may be used to track geoprocessing workflows with QGIS & DVC. In my summer school session, we go into details on how to use DVC to keep track of MovingPandas movement data analytics workflow.

Here is the recording of the session live stream and you can find the materials at

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

Comparing geographic data analysis in R and Python

Today, I want to point out a blog post over at

written together with my fellow “Geocomputation with Python” co-authors Robin Lovelace, Michael Dorman, and Jakub Nowosad.

In this blog post, we talk about our experience teaching R and Python for geocomputation. The context of this blog post is the OpenGeoHub Summer School 2023 which has courses on R, Python and Julia. The focus of the blog post is on geographic vector data, meaning points, lines, polygons (and their ‘multi’ variants) and the attributes associated with them. We plan to cover raster data in a future post.

I’ve archived my Tweets: Goodbye Twitter, Hello Mastodon

Today, Jeff Sikes @[email protected], alerted me to the fact that “Twitter has removed all media attachments from 2014 and prior” (source: So far, it seems unclear whether this was intentional or a system failure (source:

Since I’ve been on Twitter since 2011, this means that some media files are now lost. While the loss of a few low-res images is probably not a major loss for humanity, I would prefer to have some control over when and how content I created vanishes. So, to avoid losing more content, I have followed Jeff’s recommendation to create a proper archival page:

It is based on an export I pulled in October 2022 when I started to use Mastodon as my primary social media account. Unfortunately, this export did not include media files.

To follow me in the future, find me on:

Btw, a recent study published on Nature News shows that Mastodon is the top-ranking Twitter replacement for scientists.

To find other interesting people on Mastodon, there are many useful tools and lists, including, for example:

Update 2023-11-04: I’ve completely deleted my X / Twitter account. If you find any account pretending they are me on that platform, it’s an impostor.

Analyzing video-based bicycle trajectories

Did you know that MovingPandas also supports local image coordinates? Indeed, it does.

In today’s post, we will explore how we can use this feature to analyze bicycle tracks extracted from video footage published by Michael Szell @mszll:

The bicycle trajectory coordinates are stored in two separate lists: xs_640x360 and ys640x360:

This format is kind of similar to the Kaggle Taxi dataset, we worked with in the previous post. However, to use the solution we implemented there, we need to combine the x and y coordinates into nice (x,y) tuples:

df['coordinates'] = df.apply(
    lambda row: list(zip(row['xs_640x360'], row['ys_640x360'])), axis=1)
df.drop(columns=['xs_640x360', 'ys_640x360'], inplace=True)

Afterwards, we can create the points and compute the proper timestamps from the frame numbers:

def compute_datetime(row):
    # some educated guessing going on here: the paper states that the video covers 2021-06-09 07:00-08:00
    d = datetime(2021,6,9,7,0,0) + (row['frame_in'] + row['running_number']) * timedelta(seconds=2)
    return d
def create_point(xy):
        return Point(xy)
    except TypeError:  # when there are nan values in the input data
        return None
new_df = df.head().explode('coordinates')
new_df['geometry'] = new_df['coordinates'].apply(create_point)
new_df['running_number'] = new_df.groupby('id').cumcount()
new_df['datetime'] = new_df.apply(compute_datetime, axis=1)
new_df.drop(columns=['coordinates', 'frame_in', 'running_number'], inplace=True)

Once the points and timestamps are ready, we can create the MovingPandas TrajectoryCollection. Note how we explicitly state that there is no CRS for this dataset (crs=None):

trajs = mpd.TrajectoryCollection(
    traj_id_col='id',  t='datetime', crs=None)

Plotting trajectories with image coordinates

Similarly, to plot these trajectories, we should tell hvplot that it should not fetch any background map tiles (’tiles’:None) and that the coordinates are not geographic (‘geo’:False):

If you want to explore the full source code, you can find my Github fork with the Jupyter notebook at:

The repository also contains a camera image of the intersection, which we can use as a background for our trajectory plots:

bg_img = hv.RGB.load_image('img/intersection2.png', bounds=(0,0,640,360)) 

One important caveat is that speed will be calculated in pixels per second. So when we plot the bicycle speed, the segments closer to the camera will appear faster than the segments in the background:

To fix this issue, we would have to correct for the distortions of the camera lens and perspective. I’m sure that there is specialized software for this task but, for the purpose of this post, I’m going to grab the opportunity to finally test out the VectorBender plugin.

Georeferencing the trajectories using QGIS VectorBender plugin

Let’s load the five test trajectories and the camera image to QGIS. To make sure that they align properly, both are set to the same CRS and I’ve created the following basic world file for the camera image:


Then we can use the VectorBender tools to georeference the trajectories by linking locations from the camera image to locations on aerial images. You can see the whole process in action here:

After around 15 minutes linking control points, VectorBender comes up with the following georeferenced trajectory result:

Not bad for a quick-and-dirty hack. Some points on the borders of the image could not be georeferenced since I wasn’t always able to identify suitable control points at the camera image borders. So it won’t be perfect but should improve speed estimates.

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

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