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QGIS Temporal Controller is a powerful successor of TimeManager. Temporal Controller is a new core feature of the current development version and will be shipped with the 3.14 release. This post demonstrates two key advantages of this new temporal support:

  1. Expression support for defining start and end timestamps
  2. Integration into the PyQGIS API

These features come in very handy in many use cases. For example, they make it much easier to create animations from folders full of GPS tracks since the files can now be loaded and configured automatically:

Script & Temporal Controller in action (click for full resolution)

All tracks start at the same location but at different times. (Kudos for Andrew Fletcher for recordings these tracks and sharing them with me!) To create an animation that shows all tracks start simultaneously, we need to synchronize them. This synchronization can be achieved on-the-fly by subtracting the start time from all track timestamps using an expression:

directory = "E:/Google Drive/QGIS_Course/05_TimeManager/Example_Dayrides/"

def load_and_configure(path):
    path = os.path.join(directory, filename)
    uri = 'file:///' + path + "?type=csv&escape=&useHeader=No&detectTypes=yes"
    uri = uri + "&crs=EPSG:4326&xField=field_3&yField=field_2"
    vlayer = QgsVectorLayer(uri, filename, "delimitedtext")
    QgsProject.instance().addMapLayer(vlayer)

    mode = QgsVectorLayerTemporalProperties.ModeFeatureDateTimeStartAndEndFromExpressions
    expression = """to_datetime(field_1) -
    make_interval(seconds:=minimum(epoch(to_datetime("field_1")))/1000)
    """

    tprops = vlayer.temporalProperties()
    tprops.setStartExpression(expression)
    tprops.setEndExpression(expression) # optional
    tprops.setMode(mode)
    tprops.setIsActive(True)

for filename in os.listdir(directory):
    if filename.endswith(".csv"):
        load_and_configure(filename)

The above script loads all CSV files from the given directory (field_1 is the timestamp, field_2 is y, and field_3 is x), enables sets the start and end expression as well as the corresponding temporal control mode and finally activates temporal rendering. The resulting config can be verified in the layer properties dialog:

To adapt this script to other datasets, it’s sufficient to change the file directory and revisit the layer uri definition as well as the field names referenced in the expression.


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

Podcasts have become huge. I’m an avid listener of podcasts myself. I particularly enjoy formats that take the time to talk about unconventional topics in detail.

My first podcast experience was on the QGIS podcast hosted by Tim Sutton in 2014. Unfortunately, it seems like the podcast episodes are not online anymore.

Recently, I had the pleasure to join the MapScaping Podcast by Daniel O’Donohue to talk about Python for Geospatial

Other guests Daniel has already interviewed include:

Another geospatial podcast I really enjoy is The Mappyist Hour by Silas and Todd. Unfortunately, it’s a bit silent there now but it’s definitely worth to listen into their episode archive. One of my favorites is Episode 9 where Linda Stevens (Hecht) discusses her career at ESRI, the future of GIS, and the role of Open Source Spatial in that future:

If you listen to and want to recommend other spatial podcasts, please share them in the comments!

TimeManager turns 10 this year. The code base has made the transition from QGIS 1.x to 2.x and now 3.x and it would be wrong to say that it doesn’t show ;-)

Now, it looks like the days of TimeManager are numbered. Four days ago, Nyall Dawson has added native temporal support for vector layers to QGIS. This is part of a larger effort of adding time support for rasters, meshes, and now also vectors.

The new Temporal Controller panel looks similar to TimeManager. Layers are configured through the new Temporal tab in Layer Properties. The temporal dimension can be used in expressions to create fancy time-dependent styles:

temporal1

TimeManager Geolife demo converted to Temporal Controller (click for full resolution)

Obviously, this feature is brand new and will require polishing. Known issues listed by Nyall include limitations of supported time fields (only fields with datetime type are supported right now, strings cannot be used) and worse performance than TimeManager since features are filtered in QGIS rather than in the backend.

If you want to give the new Temporal Controller a try, you need to install the current development version, e.g. qgis-dev in OSGeo4W.


Update from May 16:

Many of the limitations above have already been addressed.

Last night, Nyall has recorded a one hour tutorial on this new feature, enjoy:

Mapping spatial decision patterns, such as election results, is always a hot topic. That’s why we decided to include a recipe for election maps in our QGIS Map Design books. What’s new is that this recipe is now available as a free video tutorial recorded by Oliver Burdekin:

This video is just one of many recently published video tutorials that have been created by QGIS community members.

For example, Hans van der Kwast and Kurt Menke have recorded a 7-part series on QGIS for Hydrological Applications:

and Klas Karlsson’s Youtube channel is also always worth a follow:

For the Pythonically inclined among you, there is also a new version of Python in QGIS on the Automating GIS-processes channel:

 

This post introduces Holoviz Panel, a library that makes it possible to create really quick dashboards in notebook environments as well as more sophisticated custom interactive web apps and dashboards.

The following example shows how to use Panel to explore a dataset (a trajectory collection in this case) and different parameter settings (relating to trajectory generalization). All the Panel code we need is a dict that defines the parameters that we want to explore. Then we can use Panel’s interact function to automatically generate a dashboard for our custom plotting function:

import panel as pn

kw = dict(traj_id=(1, len(traj_collection)), 
          tolerance=(10, 100, 10), 
          generalizer=['douglas-peucker', 'min-distance'])
pn.interact(plot_generalized, **kw)

Click to view the resulting dashboard in full resolution:

The plotting function uses the parameters to generate a Holoviews plot. First it fetches a specific trajectory from the trajectory collection. Then it generalizes the trajectory using the specified parameter settings. As you can see, we can easily combine maps and other plots to visualize different aspects of the data:

def plot_generalized(traj_id=1, tolerance=10, generalizer='douglas-peucker'):
  my_traj = traj_collection.get_trajectory(traj_id).to_crs(CRS(4088))
  if generalizer=='douglas-peucker':
    generalized = mpd.DouglasPeuckerGeneralizer(my_traj).generalize(tolerance)
  else:
    generalized = mpd.MinDistanceGeneralizer(my_traj).generalize(tolerance)
  generalized.add_speed(overwrite=True)
  return ( 
    generalized.hvplot(
      title='Trajectory {} (tolerance={})'.format(my_traj.id, tolerance), 
      c='speed', cmap='Viridis', colorbar=True, clim=(0,20), 
      line_width=10, width=500, height=500) + 
    generalized.df['speed'].hvplot.hist(
      title='Speed histogram', width=300, height=500) 
    )

Trajectory collections and generalization functions used in this example are part of the MovingPandas library. If you are interested in movement data analysis, you should check it out! You can find this example notebook in the MovingPandas tutorial section.

MovingPandas has come a long way since 2018 when I started to experiment with GeoPandas for trajectory data handling.

This week, MovingPandas passed peer review and was approved for pyOpenSci. This technical review process was extremely helpful in ensuring code, project, and documentation quality. I would strongly recommend it to everyone working on new data science libraries!

The lastest v0.3 release is now available from conda-forge.

All tutorials are available on MyBinder

New features include:

  • Support for GeoPandas 0.7
  • Trajectory collection aggregation functions to generate flow maps

 

We recently published a new paper on “Open Geospatial Tools for Movement Data Exploration” (open access). If you liked Movement data in GIS #26: towards a template for exploring movement data, you will find even more information about the context, challenges, and recent developments in this paper.

It also presents three open source stacks for movement data exploration:

  1. QGIS + PostGIS: a combination that will be familiar to most open source GIS users
  2. Jupyter + MovingPandas: less common so far, but Jupyter notebooks are quickly gaining popularity (even in the proprietary GIS world)
  3. GeoMesa + Spark: for when datasets become too big to handle using other means

and discusses their capabilities and limitations:


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

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