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:
    driver.verify_connectivity()

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

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

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

for rec in records:
    print(rec)

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 = tx.run(QUERY)
    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']))
    print(gdf)

tx.close() 

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: https://github.com/anitagraser/QGIS-resources/blob/1e4ea435c9b1795ba5b170ddb176aa83689112eb/qgis3/notebooks/neo4j.ipynb

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

This autumn, from September to November, 84 new plugins have been published in the QGIS plugin repository.

Here’s the quick overview in reverse chronological order. If any of the names or short descriptions piques your interest, you can find the direct link to the plugin page in the table below:

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

https://carto.com/blog/analyzing-mobility-hotspots-with-movingpandas

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.

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:

C:\Users\Anita\.Neo4jDesktop\relate-data\dbmss\dbms-72882d24-bf91-4031-84e9-abd24624b760\import

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 https://community.neo4j.com/t/best-practice-for-replacement-of-using-periodic-commit-to-call-in-transactions/48636/2:

:auto
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)
} IN TRANSACTIONS OF 10 ROWS;

… 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.

Details of MapTiler QGIS Plugin 3.0 with a global terrain basemap and customizable OpenStreetMap vector tiles.

The new version of the MapTiler plugin pushes our maps from MapTiler Cloud almost to perfection

Very simple way of how to display map service in QGI3 without a map server.

Open-source plugin for QGIS that loads fast vector maps. Change colors and fonts of the map to get unique look.

The new QGIS 3.14 version adds support for the native loading of vector tiles. The easiest way to load them is via the recently released plugin.

Changes

🐛 Bug Fixes

• Search bar's filters list improvement by @qfield-fairy in #4763
• Fix the HTML editor widget by @nirvn in #4758
• Re-introduce a timer to avoid finger hover / stylus interference by @qfield-fairy in #4757
• Fix view parent/children feature panning/zooming on high dpi screens by @qfield-fairy in #4742

Full Changelog: v3.0.6...v3.0.7

Earlier this year, in collaboration with North Road we were awarded a grant from Cesium to introduce 3D tiles support in QGIS. The feature was developed successfully and shipped with QGIS 3.34.

In this blog post, you can read more about how to work with this feature, where to get data and how to display your maps in 2D and 3D. For a video demo of this feature, you can watch Nyall Dawson’s presentation on Youtube.

What are 3D tiles?

3D tiles are a specification for streaming and rendering large-scale 3D geospatial datasets. They use a hierarchical structure to efficiently manage and display 3D content, optimising performance by dynamically loading appropriate levels of detail. This technology is widely used in urban planning, architecture, simulation, gaming, and virtual reality, providing a standardised and interoperable solution for visualising complex geographical data.

Examples of 3D tiles:

3D tiles in QGIS

To be able to use 3D tiles in QGIS, you need to have QGIS 3.34 or later. You can add a new connection to a 3D tile service from within the Data Source Manager under Scene:

Alternatively, you can add the service from your Browser Panel:

To test the feature, you can use the following 3D tiles service:

Name: 3D Tiles example
URL: https://pelican-public.s3.amazonaws.com/3dtiles/agi-hq/tileset.json

You can then add the map from the newly generated connection to QGIS:

By default, the layer is styled using texture, but you can change it to see the wireframe mesh behind the scene:

You can change the mesh fill and line symbols similar to the vector polygons. Alternatively, you can use texture colors. This will render each mesh element with the average value of the full texture. This is ideal when dealing with a large dataset and want to get a quick overview of the data:

To view the data in 3D, you can open a new 3D map. Similar to 2D map, by zooming in/out, finer resolution tiles will be fetched and displayed:

Using data from Cesium ion

Cesium ion is a cloud-based platform for managing and streaming 3D geospatial data. It simplifies data management, visualisation, and sharing.

To add 3D tiles from Cesium ion, you need to first sign up to their service here: https://ion.cesium.com/tokens

Under Asset Depot, you will see a catalogue of publicly available datasets. You can also upload your own 3D models (such as OBJ or PLY), georeference them and get them converted to 3D tiles.

You can also add one of the existing tile service under https://ion.cesium.com/assetdepot and select the tile service and then click on Add to my assets:

You can use the excellent Cesium ion plugin by North Road from the QGIS repository to add the data to QGIS:

Working with Google 3D data

In addition to accessing Google Photorealistic 3D tiles from Cesium ion, you can also add the tiles directly in QGIS. First you will need to follow the instructions below and obtain API keys for 3D tiles: https://developers.google.com/maps/documentation/tile/cloud-setup

During the registration process, you will be asked to add your credit card details. Currently (November 2023), they do not charge you for using the service.

Once you have obtained the API key, you can add Google tiles using the following connection details:

Notes and remarks

• Adjusting map extents for large scenes

When dealing with large scenes, map extents should be set to a smaller area to be able to view it in 3D. This is the current limitation of QGIS 3D maps as it cannot handle scenes larger than 500 x 500 km.

To change the map extent, you can open Project Properties and under View Settings change the extent. In the example below, the map extent has been limited only to a part of London, so we can view Google Photorealistic tiles in the 3D map without rendering issues.

• Network cache size

If you are handling a large dataset, it is recommended to increase network cache size to 1 GB or more. The default value in QGIS is much lower and it results in slower rendering of the data.

• Overlaying other 3D data

When you try to overlay other data sets on top of a global 3D tiles, the vertical datum might not match and hence you will see the data in the wrong place in a 3D map. To fix the issue, you may need to use elevation offsetting to shift the data along the Z axis under Layer Properties:

Future works

This is the first implementation of the 3D tiles in QGIS. For the future, we would like to add more features for handling and creation of the 3D tiles. Our wishlist in no particular order is:

• Globe view: QGIS 3D cannot handle large scenes or unprojected views.
• More advanced styling of meshes: as an example, users will be able to create their own style.
• 3D In-door navigation: as an example users will be able to navigate inside buildings and potentially it will bring BIM data closer to QGIS
• Generation of 3D tiles inside QGIS: adding a processing tool in QGIS to generate 3D Tiles from your map data.

If you would like to see those features in QGIS and want to fund the efforts, do not hesitate to contact us.

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
qgs.initQgis()
canvas = QgsMapCanvas()
project = QgsProject.instance()
map_crs = QgsCoordinateReferenceSystem('EPSG:3857')
canvas.setDestinationCrs(map_crs)
print("providers: ", QgsProviderRegistry.instance().providerList())

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

urlWithParams = 'type=xyz&url=https://tile.openstreetmap.org/{z}/{x}/{y}.png&zmax=19&zmin=0&crs=EPSG3857'
rlayer = QgsRasterLayer(urlWithParams, 'OpenStreetMap', 'wms')  
print(rlayer.crs())
if rlayer.isValid():
    project.addMapLayer(rlayer)
else:
    print('invalid layer')
    print(rlayer.error().summary()) 

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:

canvas.setExtent(rlayer.extent())
plot_layers([vlayer,rlayer])

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

vlayer.renderer().symbol().setColor(QColor("yellow"))
vlayer.renderer().symbol().symbolLayer(0).setShape(QgsSimpleMarkerSymbolLayerBase.Star)
vlayer.renderer().symbol().symbolLayer(0).setSize(10)
plot_layers([vlayer,rlayer])

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=http://basemaps.cartocdn.com/dark_all/{z}/{x}/{y}.png&zmax=19&zmin=0&crs=EPSG3857'
rlayer2 = QgsRasterLayer(urlWithParams, 'Carto', 'wms')  
print(rlayer2.crs())
if rlayer2.isValid():
    project.addMapLayer(rlayer2)
else:
    print('invalid layer')
    print(rlayer2.error().summary()) 
    
plot_layers([vlayer,rlayer2])

You can find the whole notebook at: https://github.com/anitagraser/QGIS-resources/blob/master/qgis3/notebooks/basemaps.ipynb

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: https://github.com/movingpandas 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.

Open source software projects thrive on the contributions of the community, not only for the code, but also for making the software accessible to a global audience. One of the critical aspects of this accessibility is the localization or translation of the software’s messages and interfaces. In this context, Weblate (https://weblate.org/) has proven to be […]

The post Translating Open Source Software with Weblate: A GRASS GIS Case Study appeared first on Markus Neteler Consulting.

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): 
    canvas.saveAsImage(path)
    if show: return Image(path)

project.addMapLayer(vlayer)
canvas.setExtent(vlayer.extent())
canvas.setLayers([vlayer])
canvas.show()
app.exec_()

saveImage("my-traj.png")

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:

vlayer.renderer().symbol().setColor(QColor("red"))

vlayer.triggerRepaint()
canvas.show()
app.exec_()
saveImage("my-traj.png")

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:

vlayer.renderer().symbol().appendSymbolLayer(QgsArrowSymbolLayer())

vlayer.triggerRepaint()
canvas.show()
app.exec_()
saveImage("my-traj.png")

We can also create a QgsGraduatedSymbolRenderer:

geom_type = vlayer.geometryType()
myRangeList = []

symbol = QgsSymbol.defaultSymbol(geom_type)
symbol.setColor(QColor("#3333ff"))
myRange = QgsRendererRange(0, 1, symbol, 'Group 1')
myRangeList.append(myRange)

symbol = QgsSymbol.defaultSymbol(geom_type)
symbol.setColor(QColor("#33ff33"))
myRange = QgsRendererRange(1, 3, symbol, 'Group 2')
myRangeList.append(myRange)

myRenderer = QgsGraduatedSymbolRenderer('speed', myRangeList)
vlayer.setRenderer(myRenderer)

vlayer.triggerRepaint()
canvas.show()
app.exec_()
saveImage("my-traj.png")

And we can combine both QgsGraduatedSymbolRenderer and QgsArrowSymbolLayer:

geom_type = vlayer.geometryType()
myRangeList = []

symbol = QgsSymbol.defaultSymbol(geom_type)
symbol.appendSymbolLayer(QgsArrowSymbolLayer())
symbol.setColor(QColor("#3333ff"))
myRange = QgsRendererRange(0, 1, symbol, 'Group 1')
myRangeList.append(myRange)

symbol = QgsSymbol.defaultSymbol(geom_type)
symbol.appendSymbolLayer(QgsArrowSymbolLayer())
symbol.setColor(QColor("#33ff33"))
myRange = QgsRendererRange(1, 3, symbol, 'Group 2')
myRangeList.append(myRange)

myRenderer = QgsGraduatedSymbolRenderer('speed', myRangeList)
vlayer.setRenderer(myRenderer)

vlayer.triggerRepaint()
canvas.show()
app.exec_()
saveImage("my-traj.png")

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')"

# https://qgis.org/pyqgis/3.0/core/Symbol/QgsSymbolLayer.html?highlight=property#qgis.core.QgsSymbolLayer.PropertySize
renderer.symbol().symbolLayer(0).setDataDefinedProperty(
    QgsSymbolLayer.PropertyStrokeWidth, QgsProperty.fromExpression(exp_width))
renderer.symbol().symbolLayer(0).setDataDefinedProperty(
    QgsSymbolLayer.PropertyStrokeColor, QgsProperty.fromExpression(exp_color))
renderer.symbol().symbolLayer(0).setDataDefinedProperty(
    QgsSymbolLayer.PropertyCapStyle, QgsProperty.fromExpression("'round'"))

vlayer.setRenderer(renderer)

vlayer.triggerRepaint()
canvas.show()
app.exec_()
saveImage("my-traj.png")

Find the full notebook at: https://github.com/anitagraser/QGIS-resources/blob/master/qgis3/notebooks/layer-styling.ipynb

Sorry, this entry is only available in Dutch.

We’ve recently had the opportunity to implement a very exciting feature in QGIS 3.34 — the ability to load and view 3D content in the “Cesium 3D Tiles” format! This was a joint project with our (very talented!) partners at Lutra Consulting, and was made possible thanks to a generous ecosystem grant from the Cesium project.

Before we dive into all the details, let’s take a quick guided tour showcasing how Cesium 3D Tiles work inside QGIS:

What are 3D tiles?

Cesium 3D Tiles are an OGC standard data format where the content from a 3D scene is split up into multiple individual tiles. You can think of them a little like a 3D version of the vector tile format we’ve all come to rely upon. The 3D objects from the scene are stored in a generalized, simplified form for small-scale, “zoomed out” maps, and in more detailed, complex forms for when the map is zoomed in. This allows the scenes to be incredibly detailed, whilst still covering huge geographic regions (including the whole globe!) and remaining responsive and quick to download. Take a look at the incredible level of detail available in a Cesium 3D Tiles scene in the example below:

Where can you get 3D tile content?

If you’re lucky, your regional government or data custodians are already publishing 3D “digital twins” of your area. Cesium 3D Tiles are the standard way that these digital twin datasets are being published. Check your regional data portals and government open data hubs and see whether they’ve made any content available as 3D tiles. (For Australian users, there’s tons of great content available on the Terria platform!).

Alternatively, there’s many datasets available via the Cesium ion platform. This includes global 3D buildings based on OpenStreetMap data, and the entirety of Google’s photorealistic Google Earth tiles! We’ve published a Cesium ion QGIS plugin to complement the QGIS 3.34 release, which helps make it super-easy to directly load datasets from ion into your QGIS projects.

Lastly, users of the OpenDroneMap photogrammetry application will already have Cesium 3D Tiles datasets of their projects available, as 3D tiles are one of the standard outputs generated by OpenDroneMap.

Why QGIS?

So why exactly would you want to access Cesium 3D tiles within QGIS? Well, for a start, 3D Tiles datasets are intrinsically geospatial data. All the 3D content from these datasets are georeferenced and have accurate spatial information present. By loading a 3D tiles dataset into QGIS, you can easily overlay and compare 3D tile content to all your other standard spatial data formats (such as Shapefiles, Geopackages, raster layers, mesh datasets, WMS layers, etc…). They become just another layer of spatial information in your QGIS projects, and  you can utilise all the tools and capabilities you’re familiar with in QGIS for analysing spatial data along with these new data sources.

One large drawcard of adding a Cesium 3D Tile dataset to your QGIS project is that they make fantastic 3D basemaps. While QGIS has had good support for 3D maps for a number of years now, it has been tricky to create beautiful 3D content. That’s because all the standard spatial data formats tend to give generalised, “blocky” representations of objects in 3D. For example, you could use an extruded building footprint file to show buildings in a 3D map but they’ll all be colored as idealised solid blocks. In contrast, Cesium 3D Tiles are a perfect fit for a 3D basemap! They typically include photorealistic textures, and include all types of real-world features you’d expect to see in a 3D map — including buildings, trees, bridges, cliffsides, etc.

What next?

If you’re keen to learn even more about Cesium 3D Tiles in QGIS, you can check out the recent “QGIS Open Day” session we presented. In this session we cover all the details about 3D tiles and QGIS, and talk in depth about what’s possible in QGIS 3.34 and what may be coming in later releases.

Otherwise, grab the latest QGIS 3.34 and start playing…. you’ll quickly find that Cesium 3D Tiles are a fun and valuable addition to QGIS’ capabilities!

Our thanks go to Cesium and their ecosystem grant project for funding this work and making it possible.

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. https://doi.org/10.5281/zenodo.6405212

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.

We are pleased to announce the release of QGIS 3.34 Prizren!

Installers for Windows and Linux are already out. QGIS 3.34 comes with tons of new features, as you can see in our visual changelog. QGIS 3.34 Prizren is named after this year’s FOSS4G host city.

We would like to thank the developers, documenters, testers and all the many folks out there who volunteer their time and effort (or fund people to do so). From the QGIS community we hope you enjoy this release! If you wish to donate time, money or otherwise get involved in making QGIS more awesome, please wander along to qgis.org and lend a hand!

QGIS is supported by donors and sustaining members. A current list of donors who have made financial contributions large and small to the project can be seen on our donors list. If you would like to become a sustaining member, please visit our page for sustaining members for details. Your support helps us fund our six monthly developer meetings, maintain project infrastructure and fund bug fixing efforts.

QGIS is Free software and you are under no obligation to pay anything to use it – in fact we want to encourage people far and wide to use it regardless of what your financial or social status is – we believe empowering people with spatial decision making tools will result in a better society for all of humanity.

Recently, we had the pleasure of helping organise the FOSS4G SotM Oceania 2023 conference in Auckland. It was a fantastic week and felt like a worthy return to pre-covid events full of great presentations, catching up with old friends, making new ones, and of course – delicious food! The venue, Auckland University of Technology, put on a really professional event with catering, venue spaces and their Audio Visual operations. Auckland gave us great weather, and great venues to enjoy each others company in.

We were blown away with the variety of presentations and the talent our Oceania region holds. We got to meet some of our clients in person including Koordinates, A.B. Heritage and Soar, which we loved, plus we spent valuable time connecting with our community.

At North Road we are passionate about supporting open-source and our local Oceania region, so we took an active part in making this year’s conference happen. Aside from being a conference sponsor, our very own powerhouse Emma Hain worked tirelessly as Program Chair to organise the conference program and ensure that the talks ran smoothly and were of very high value.  Nyall ran a workshop for Advanced Cartography, presented on his journey in the FOSS4G workspace and contributed as panel member on making  a living in FOSS4G. Emma also presented a lightning talk on GitHub QGIS Issues and was a community day facilitator on an exciting project to develop digital stories with a map component. We really love community and being involved in “building it” is integral. But it is also super fun as well!

One of the great things about this conference is the legacy it leaves, not only in the relationships formed, but in the recordings of all the presentations that end up on the FOSS4G Oceania YouTube channel. This is not only knowledge captured for the community, but also a showcase for the talent that exists in FOSS4G. Not every  presentations was technical or about case studies, they also covered personal observations, hobbies and general mapping topics.

You can view the presentations from our staff below:

Also, check out the presentations from some of the great companies we have worked with:

Soar: Soar: lessons from our start-up experience, From Satellites to Slobs,   Maps, Media and Manipulation

Koordinates: Kart – the geospatial version control system – now with Rasters!,  Making it up: map making bots, generative mapping and TTRPG tools

Community Day

On the last day of the conference we attended the Community Day. This involves a Mapathon run by HOTOSM, and rooms for brainstorming and workshopping of ideas. These were really valuable! First we attended the FOSS4G Hui run by Byron Cochrane from OpenWork (one of the conference sponsors). It was a great discussion on how to make FOSS4G more relevant in Oceania. If you are keen to help out with this, contact the OSGeo Oceania Board – as they need helpers with Communications and members to espouse FOSS4G at outside conferences.

Emma also ran the session on developing digital stories with a map component in a FOSS4G environment. This was well attended with a lot of ideas. We developed our MVP and we are now looking at our next steps. The question here is do we develop a new tool within existing software, find a standalone software where we could add in a map component, or approach a current OS Story Map software and provide our MVP and find funding. If you’re keen to take part in these discussions, you can join in on the Maptime Oceania slack channel under #foss4g-productdev.

A Big Thank-you!

The Organisation committee worked so hard on putting this conference on, and we would like to express our heartfelt thanks for this. This is the keystone event for our community and so much grows from this. Without the Organisation Committee’s sweat, tears, frustration and joy this would not have happened. We thank their families who supported them whilst they gave up their valuable time and their employers who supported them to give their time and effort to making this special event happen.

Bring on FOSS4G/SOTM Oceania 2024!