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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 https://github.com/movingpandas/qgis-processing-trajectory.

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: https://github.com/movingpandas/qgis-processing-trajectory


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

Analyzing mobility hotspots with MovingPandas & CARTO

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.

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: 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.

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):
    try: 
        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)
new_df

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(
    gpd.GeoDataFrame(new_df), 
    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: https://github.com/anitagraser/desirelines/blob/main/mpd.ipynb

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:

1
0
0
-1
0
360

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.

How to use Kaggle’s Taxi Trajectory Data in MovingPandas

Kaggle’s “Taxi Trajectory Data from ECML/PKDD 15: Taxi Trip Time Prediction (II) Competition” is one of the most used mobility / vehicle trajectory datasets in computer science. However, in contrast to other similar datasets, Kaggle’s taxi trajectories are provided in a format that is not readily usable in MovingPandas since the spatiotemporal information is provided as:

  • TIMESTAMP: (integer) Unix Timestamp (in seconds). It identifies the trip’s start;
  • POLYLINE: (String): It contains a list of GPS coordinates (i.e. WGS84 format) mapped as a string. The beginning and the end of the string are identified with brackets (i.e. [ and ], respectively). Each pair of coordinates is also identified by the same brackets as [LONGITUDE, LATITUDE]. This list contains one pair of coordinates for each 15 seconds of trip. The last list item corresponds to the trip’s destination while the first one represents its start;

Therefore, we need to create a DataFrame with one point + timestamp per row before we can use MovingPandas to create Trajectories and analyze them.

But first things first. Let’s download the dataset:

import datetime
import pandas as pd
import geopandas as gpd
import movingpandas as mpd
import opendatasets as od
from os.path import exists
from shapely.geometry import Point

input_file_path = 'taxi-trajectory/train.csv'

def get_porto_taxi_from_kaggle():
    if not exists(input_file_path):
        od.download("https://www.kaggle.com/datasets/crailtap/taxi-trajectory")

get_porto_taxi_from_kaggle()
df = pd.read_csv(input_file_path, nrows=10, usecols=['TRIP_ID', 'TAXI_ID', 'TIMESTAMP', 'MISSING_DATA', 'POLYLINE'])
df.POLYLINE = df.POLYLINE.apply(eval)  # string to list
df

And now for the remodelling:

def unixtime_to_datetime(unix_time):
    return datetime.datetime.fromtimestamp(unix_time)

def compute_datetime(row):
    unix_time = row['TIMESTAMP']
    offset = row['running_number'] * datetime.timedelta(seconds=15)
    return unixtime_to_datetime(unix_time) + offset

def create_point(xy):
    try: 
        return Point(xy)
    except TypeError:  # when there are nan values in the input data
        return None
 
new_df = df.explode('POLYLINE')
new_df['geometry'] = new_df['POLYLINE'].apply(create_point)
new_df['running_number'] = new_df.groupby('TRIP_ID').cumcount()
new_df['datetime'] = new_df.apply(compute_datetime, axis=1)
new_df.drop(columns=['POLYLINE', 'TIMESTAMP', 'running_number'], inplace=True)
new_df

And that’s it. Now we can create the trajectories:

trajs = mpd.TrajectoryCollection(
    gpd.GeoDataFrame(new_df, crs=4326), 
    traj_id_col='TRIP_ID', obj_id_col='TAXI_ID', t='datetime')
trajs.hvplot(title='Kaggle Taxi Trajectory Data', tiles='CartoLight')

That’s it. Now our MovingPandas.TrajectoryCollection is ready for further analysis.

By the way, the plot above illustrates a new feature in the recent MovingPandas 0.16 release which, among other features, introduced plots with arrow markers that show the movement direction. Other new features include a completely new custom distance, speed, and acceleration unit support. This means that, for example, instead of always getting speed in meters per second, you can now specify your desired output units, including km/h, mph, or nm/h (knots).


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

MovingPandas v0.13 & v0.14 released!

December has been busy with two new MovingPandas releases: v0.13 and v0.14.

The latest v0.14 release is now available from conda-forge.

These releases are a huge step forward towards making MovingPandas easier to install with fewer mandatory dependencies. All interactive plotting libraries are now optional. So if you are using MovingPandas for trajectory data processing in the background and don’t need the interactive visualization features, the number of necessary libraries is now much lower. This (and the fact that GeoPandas is now shipped with OSGeo4W) will also make it easier to use MovingPandas in QGIS plugins.

New features:

  • #268 New add_angular_difference method

Includes fixes and enhancements for:

  • #267 Improved documentation: direction values are [0, 360)

Behind the scenes:

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.

MovingPandas v0.12 released!

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.

MovingPandas v0.11 released!

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.

MovingPandas v0.10 released!

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:

MF-JSON update & tutorial with official sample

Since last week’s post, I’ve learned that there is an official OGC Moving Features JSON Encodings repository with more recent sample datasets, including MovingPoint, MovingPolygon, and Trajectory JSON examples.

The MovingPoint example seems to describe a storm, including its path (temporalGeometry), pressure, wind strength, and class values (temporalProperties):

You can give the current implementation a spin using this MyBinder notebook

An exciting future step would be to experiment with extending MovingPandas to support the MovingPolygon MF-JSON examples. MovingPolygons can change their size and orientation as they move. I’m not yet sure, however, if the number of polygon nodes can change between time steps and how this would be reflected by the prism concept presented in the draft specification:

Image source: https://ksookim.github.io/mf-json/

New OGC Moving Features JSON support in MovingPandas

First time, we talked about the OGC Moving Features standard in a post from 2017. Back then, we looked at the proposed standard way to encode trajectories in CSV and discussed its issues. Since then, the Moving Features working group at OGC has not been idle. Besides the CSV and XML encodings, they have designed a new JSON encoding that addresses many of the downsides of the previous two. You can read more about this in our 2020 preprint “From Simple Features to Moving Features and Beyond”.

Basically Moving Features JSON (MF-JSON) is heavily inspired by GeoJSON and it comes with a bunch of mandatory and optional key/value pairs. There is support for static properties as well as dynamic temporal properties and, of course, temporal geometries (yes geometries, not just points).

I think this format may have an actual chance of gaining more widespread adoption.

Image source: http://www.opengis.net/doc/BP/mf-json/1.0

Inspired by Pandas.read_csv() and GeoPandas.read_file(), I’ve started implementing a read_mf_json() function in MovingPandas. So far, it supports basic MovingFeature JSONs with MovingPoint geometry:

You’ll need to use the current development version to test this feature.

Next steps will be MovingFeatureCollection JSONs and support for static as well as temporal properties. We’ll have to see if MovingPandas can be extended to go beyond moving point geometries. Storing moving linestrings and polygons in the GeoDataFrame will be the simple part but analytics and visualization will certainly be more tricky.


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

Geospatial: where MovingPandas meets Leafmap

Many of you certainly have already heard of and/or even used Leafmap by Qiusheng Wu.

Leafmap is a Python package for interactive spatial analysis with minimal coding in Jupyter environments. It provides interactive maps based on folium and ipyleaflet, spatial analysis functions using WhiteboxTools and whiteboxgui, and additional GUI elements based on ipywidgets.

This way, Leafmap achieves a look and feel that is reminiscent of a desktop GIS:

Image source: https://github.com/giswqs/leafmap

Recently, Qiusheng has started an additional project: the geospatial meta package which brings together a variety of different Python packages for geospatial analysis. As such, the main goals of geospatial are to make it easier to discover and use the diverse packages that make up the spatial Python ecosystem.

Besides the usual suspects, such as GeoPandas and of course Leafmap, one of the packages included in geospatial is MovingPandas. Thanks, Qiusheng!

I’ve tested the mamba install today and am very happy with how this worked out. There is just one small hiccup currently, which is related to an upstream jinja2 issue. After installing geospatial, I therefore downgraded jinja:

mamba install -c conda-forge geospatial 
mamba install -c conda-forge jinja2=3.0

Of course, I had to try Leafmap and MovingPandas in action together. Therefore, I fired up one of the MovingPandas example notebook (here the example on clipping trajectories using polygons). As you can see, the integration is pretty smooth since Leafmap already support drawing GeoPandas GeoDataFrames and MovingPandas can convert trajectories to GeoDataFrames (both lines and points):

Clipped trajectory segments as linestrings in Leafmap
Leafmap includes an attribute table view that can be activated on user request to show, e.g. trajectory information
And, of course, we can also map the original trajectory points

Geospatial also includes the new dask-geopandas library which I’m very much looking forward to trying out next.

MovingPandas now supports local coordinates

MovingPandas 0.9rc3 has just been released, including important fixes for local coordinate support. Sports analytics is just one example of movement data analysis that uses local rather than geographic coordinates.

Many movement data sources – such as soccer players’ movements extracted from video footage – use local reference systems. This means that x and y represent positions within an arbitrary frame, such as a soccer field.

Since Geopandas and GeoViews support handling and plotting local coordinates just fine, there is nothing stopping us from applying all MovingPandas functionality to this data. For example, to visualize the movement speed of players:

Of course, we can also plot other trajectory attributes, such as the team affiliation.

But one particularly useful feature is the ability to use custom background images, for example, to show the soccer field layout:

To access the full example notebook, visit: https://github.com/anitagraser/movingpandas/blob/master/tutorials/5-local-coordinates.ipynb

An update to the MovingPandas examples repository will follow shortly.

MovingPandas v0.9 released!

The latest v0.9 release is now available from conda-forge.

This release contains some really cool new algorithms:

The Kalman filter in action on the Geolife sample: smoother, less jiggly trajectories.
Top-Down Time Ratio generalization aka trajectory compression in action: reduces the number of positions along the trajectory without altering the spatiotemporal properties, such as speed, too much.

These new algorithms were contributed by Lyudmil Vladimirov and George S. Theodoropoulos.

Behind the scenes, Ray Bell took care of moving testing from Travis to Github Actions, and together we worked through the steps to ensure that the source code is now properly linted using flake8 and black.

Being able to work with so many awesome contributors has made this release really special for me. It’s great to see the project attracting more developer interest.

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

MovingPandas v0.8 released!

The latest v0.8 release is now available from conda-forge.

New features include:

  • More convenient creation of TrajectoryCollection objects from (Geo)DataFrames (#137)
  • Support for different geometry column names (#112)

Last week, I also had the pleasure to speak about MovingPandas at Carto’s Spatial Data Science Conference SDSC21:

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

MovingPandas v0.7 released!

The latest v0.7 release is now available from conda-forge.

New features include:

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

Movement data in GIS #35: stop detection & analysis with MovingPandas

In the last few days, there’s been a sharp rise in interest in vessel movements, and particularly, in understanding where and why vessels stop. Following the grounding of Ever Given in the Suez Canal, satellite images and vessel tracking data (AIS) visualizations are everywhere:

Using movement data analytics tools, such as MovingPandas, we can dig deeper and explore patterns in the data.

The MovingPandas.TrajectoryStopDetector is particularly useful in this situation. We can provide it with a Trajectory or TrajectoryCollection and let it detect all stops, that is, instances were the moving object stayed within a certain area (with a diameter of 1000m in this example) for a an extended duration (at least 3 hours).

stops = mpd.TrajectoryStopDetector(trajs).get_stop_segments(
    min_duration=timedelta(hours=3), max_diameter=1000)

The resulting stop segments include spatial and temporal information about the stop location and duration. To make this info more easily accessible, let’s turn the stop segment TrajectoryCollection into a point GeoDataFrame:

stop_pts = gpd.GeoDataFrame(columns=['geometry']).set_geometry('geometry')
stop_pts['stop_id'] = [track.id for track in stops.trajectories]
stop_pts= stop_pts.set_index('stop_id')

for stop in stops:
    stop_pts.at[stop.id, 'ID'] = stop.df['ID'][0]
    stop_pts.at[stop.id, 'datetime'] = stop.get_start_time()
    stop_pts.at[stop.id, 'duration_h'] = stop.get_duration().total_seconds()/3600
    stop_pts.at[stop.id, 'geometry'] = stop.get_start_location()

Indeed, I think the next version of MovingPandas should include a function that directly returns stops as points.

Now we can explore the stop information. For example, the map plot shows that stops are concentrated in three main areas: the northern and southern ends of the Canal, as well as the Great Bitter Lake in the middle. By looking at the timing of stops and their duration in a scatter plot, we can clearly see that the Ever Given stop (red) caused a chain reaction: the numerous points lining up on the diagonal of the scatter plot represent stops that very likely are results of the blockage:

Before the grounding, the stop distribution nicely illustrates the canal schedule. Vessels have to wait until it’s turn for their direction to go through:

You can see the full analysis workflow in the following video. Please turn on the captions for details.

Huge thanks to VesselsValue for supplying the data!

For another example of MovingPandas‘ stop dectection in action, have a look at Bryan R. Vallejo’s tutorial on detecting stops in bird tracking data which includes some awesome visualizations using KeplerGL:

Kepler.GL visualization by Bryan R. Vallejo

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

Movement data in GIS #34: a protocol for exploring movement data

After writing “Towards a template for exploring movement data” last year, I spent a lot of time thinking about how to develop a solid approach for movement data exploration that would help analysts and scientists to better understand their datasets. Finally, my search led me to the excellent paper “A protocol for data exploration to avoid common statistical problems” by Zuur et al. (2010). What they had done for the analysis of common ecological datasets was very close to what I was trying to achieve for movement data. I followed Zuur et al.’s approach of a exploratory data analysis (EDA) protocol and combined it with a typology of movement data quality problems building on Andrienko et al. (2016). Finally, I brought it all together in a Jupyter notebook implementation which you can now find on Github.

There are two options for running the notebook:

  1. The repo contains a Dockerfile you can use to spin up a container including all necessary datasets and a fitting Python environment.
  2. Alternatively, you can download the datasets manually and set up the Python environment using the provided environment.yml file.

The dataset contains over 10 million location records. Most visualizations are based on Holoviz Datashader with a sprinkling of MovingPandas for visualizing individual trajectories.

Point density map of 10 million location records, visualized using Datashader

Line density map for detecting gaps in tracks, visualized using Datashader

Example trajectory with strong jitter, visualized using MovingPandas & GeoViews

 

I hope this reference implementation will provide a starting point for many others who are working with movement data and who want to structure their data exploration workflow.

If you want to dive deeper, here’s the paper:

[1] Graser, A. (2021). An exploratory data analysis protocol for identifying problems in continuous movement data. Journal of Location Based Services. doi:10.1080/17489725.2021.1900612.

(If you don’t have institutional access to the journal, the publisher provides 50 free copies using this link. Once those are used up, just leave a comment below and I can email you a copy.)

References


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

MovingPandas v0.5 released!

The latest v0.5 release is now available from conda-forge.

New features include:

As always, all tutorials are available on MyBinder:

 

Detected stops (left) and trajectory split at stops (right)

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