Infostop
Python package for detecting stop locations in mobility data
This package implements the algorithm described in https://arxiv.org/pdf/2003.14370.pdf, for detecting stop locations in time-ordered location data.
Infostop is useful to anyone who wishes to detect stationary events in location coordinate streams. It is, thus, a framework to simplify dense and rich location time-series into sequences of events.
Usage
Given a location trace such as:
>>> data
array([[ 55.75259295, 12.34353885 ],
[ 55.7525908 , 12.34353145 ],
[ 55.7525876 , 12.3435386 ],
...,
[ 63.40379175, 10.40477095 ],
[ 63.4037841 , 10.40480265 ],
[ 63.403787 , 10.4047871 ]])
Or with time information
>>> data
array([[ 55.75259295, 12.34353885, 1581401760 ],
[ 55.7525908 , 12.34353145, 1581402760 ],
[ 55.7525876 , 12.3435386 , 1581403760 ],
...,
[ 63.40379175, 10.40477095, 1583401760 ],
[ 63.4037841 , 10.40480265, 1583402760 ],
[ 63.403787 , 10.4047871 , 1583403760 ]])
A stop location solution can be obtained using:
>>> from infostop import Infostop
>>> model = Infostop()
>>> labels = model.fit_predict(data)
labels has one integer per row of data: the label of the stop location the point belongs to, or -1 if the point is not part of a stop (for example, it was recorded while moving). With the default distance_metric="haversine", the distances r1 and r2 are in meters. The time parameters min_staying_time and max_time_between are in the units of the time column, and their defaults assume seconds.
Alternatively, data can also be a list of numpy.arrays, in which case it is assumed that list elements are separate traces in the same space. In this multi segment (or multi user) case, Infostop finds stop locations that are shared by different segments.
Solutions can be plotted using:
>>> from infostop import plot_map
>>> folmap = plot_map(model)
>>> folmap.m
Stop locations on a university campus, with points coloured by their label:
The documentation has runnable examples and explains every parameter.
Advantages
- Simplicity: At its core, the method works by two steps. (1) Reducing the location trace to the medians of each stationary event and (2) embedding the resulting locations into a network that connects locations that are within a user-defined distance and clustering that network.
- Multi-trace support: Currently, no other libraries support clustering multiple traces at once to find global stop locations. Infostop does. The image above visualizes stop locations at a campus for a population of almost 1000 university students.
- Flow based: Spatial clusters correspond to collections of location points that contain large amounts of flow when represented as a network. This enables the recovery of locations where traces slightly overlap.
- Speed: First the point space is reduced to the median of stationary points (executed in a fast C++ module), then spatially neighboring points connected using a Ball search tree algorithm, and finally the network is clustered using the C++ based Infomap program. For example, clustering 100.000 location points takes about a second.
Installation
pip install infostop
macOS without Infomap wheels (Intel Macs, and Apple Silicon Macs older than macOS 15, on Python 3.11+): pip has to compile Infomap, and Infomap's build needs OpenMP unless you switch it off (#77). Install Infomap without OpenMP first, then infostop:
$ OPENMP=0 pip install infomap
$ pip install infostop
If you use Homebrew and your Python runs natively (not an x86_64 Python under Rosetta on Apple Silicon), brew install libomp before pip install infostop also works.
Reproducing earlier results. Since infostop 0.2.0, Infomap clusters the network 50 times (num_trials=50) and keeps the best result. This makes labels agree between Infomap versions and between operating systems for almost all inputs. Compared with infostop 0.1.9, some labels change: on some crowded stops (stops that overlap within r2), and, with label_singleton=False, at places whose stop events share one coordinate. To get the labels of 0.1.9, use the same operating system as before, and either:
- pin
infostop==0.1.9andinfomap==1.0.6, or - use infostop 0.2.0 with Infomap 1.0.6 and
num_trials=1. This keeps thelabel_singleton=Falsechange.
The changelog has the details.
Development notes
We welcome contributions. Before you get started, you may want to read the notes below.
You should create a virtual environment with a Python from 3.9 to 3.14 (CI tests 3.9 and 3.14). make env uses the python3 on your PATH. On Python 3.9 and 3.10, pip installs Infomap 2.9.2, the last Infomap for these versions. It has no wheels for them, so pip compiles it, which needs a C++ compiler. In your local infostop folder, do:
$ make env
$ source env/bin/activate
Install the development tools (pytest, black, coverage, build) and the dependencies:
(env) $ make deps
Install infostop into your virtual environment.
Do this by running:
(env) $ make install
This command builds the package and replaces any pre-existing installation of Infostop, so you will probably want to run it after each code update.
Run tests:
(env) $ make test
Like CI, this runs the tests from a temporary folder against the installed package (not the source folder), so run make install first.
Check test coverage:
(env) $ make coverage
(env) $ cd htmlcov
(env) $ python -m http.server 8001
Then go to localhost:8001 in your browser to look at the coverage report.
Format code with black. We don't want to argue about code formatting. Please run black to apply standard formatting to your code before you make a pull request.
The Makefile implements a number of commands that are useful during development.
Go ahead and execute make help to see descriptions of available commands, or inspect the file so you understand what's going on.
Convenient: create an ipykernel for the virtual environment If you use Jupyter notebooks, you can install the virtual environment into Jupyter as a kernel. Run:
(env) $ pip install ipykernel
(env) $ python -m ipykernel install --user --name=infostop_env
This lets you select the virtual environment as a kernel in a Jupyter notebook.
Versioning and deployment to PyPI Releases are made by the maintainer. A version number on PyPI can never be reused, so a release cannot be undone.
- On an up-to-date
masterthat has everything you want to release, execute theincrement_version.pyscript ONCE. It increments the last number of__version__ininfostop/metadata.py. Commit the change. - Tag that commit with
vfollowed by the version ininfostop/metadata.py. For example, if it says0.2.1:
(env) $ git tag -a v0.2.1 -m "Infostop version 0.2.1"
- Push the commit and the tag together:
(env) $ git push --atomic origin master v0.2.1
Pushing a v* tag starts the Release workflow. It builds the sdist and the wheels (Linux x86_64 and aarch64, macOS x86_64 and arm64, Windows x86_64, for each supported Python version), runs the tests against each wheel, and then uploads all of them to PyPI with Trusted Publishing, so no API token is stored on GitHub. It uploads nothing if the tag is not v + __version__, if the tagged commit is not on master, or if a build or a test fails. Every PR builds and tests the same wheels, so a broken build shows up before the release.
Do not upload the output of make build by hand: on Linux it is a linux_x86_64 wheel, which PyPI rejects.
1 maintainer
Metadata
1 maintainer
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