Science Score: 64.0%
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Repository
Python package for detecting stops in trajectory data
Basic Info
Statistics
- Stars: 64
- Watchers: 6
- Forks: 10
- Open Issues: 12
- Releases: 0
Metadata Files
README.md
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:
```Python
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
```Python
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:
```Python
from infostop import Infostop model = Infostop() labels = model.fit_predict(data) ```
Alternatively, data can also be a list of numpy.arrays, in which case it is assumed that list elements are seperate 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:
```Python
from infostop import plotmap folmap = plotmap(model) folmap.m ```
Plotting this onto a map:
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
Development notes
We welcome contributions. Before you get started, you may want to read the notes below.
You should create a virtual environment. In your local infostop folder, do:
Bash
$ make env
Install infostop into your virtual environment.
Do this by running:
Bash
(env) $ make install
This command will also delete any pre-existing installation of Infostop, so you will probably want to run it after each code update.
Run tests:
Bash
(env) $ make test
Check test coverage:
Bash
(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 your 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:
Bash
(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
If your update should trigger a version increment and package rerelease, please execute the increment_version.py script ONCE and tag your final commit. After running the commit command, to tag the commit you would run something like:
Bash
(env) $ git tag -a v1.0.11 -m "Infostop version 1.0.11"
Finally, push first the tags and then your commits.
Bash
(env) $ git push --tags && git push
When mergining a PR with a tagged commit, the PyPI deployment action is triggered, and the new version of Infostop becomes publicly available shortly thereafter.
Citation (CITATION.cff)
cff-version: 1.2.0
title: Infostop
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Ulf
family-names: Aslak
email: ulfaslak@gmail.com
- given-names: Laura
family-names: Alessandretti
email: lauale@dtu.dk
affiliation: Technical University of Denmark
identifiers:
- type: doi
value: 10.48550/arXiv.2003.14370
repository-code: 'https://github.com/ulfaslak/infostop'
abstract: >-
Data-driven research in mobility has prospered in recent
years, providing solutions to real-world challenges
including forecasting epidemics and planning
transportation. These advancements were facilitated by
computational tools enabling the analysis of large-scale
data-sets of digital traces. One of the challenges when
pre-processing spatial trajectories is the so-called stop
location detection, that entails the reduction of raw time
series to sequences of destinations where an individual
was stationary. The most widely adopted solution to this
problem was proposed by Hariharan and Toyama (2004) and
involves filtering out non-stationary measurements, then
applying agglomerative clustering on the stationary
points. This state-of-the-art solution, however, suffers
of two limitations: (i) frequently visited places located
very close (such as adjacent buildings) are likely to be
merged into a unique location, due to inherent measurement
noise, (ii) traces for multiple users can not be analysed
simultaneously, thus the definition of destination is not
shared across users. In this paper, we describe the
Infostop algorithm that overcomes the limitations of the
state-of-the-art solution by leveraging the flow-based
network community detection algorithm Infomap. We test
Infostop for a population of ∼1000 individuals with highly
overlapping mobility. We show that the size of locations
detected by Infostop saturates for increasing number of
users and that time complexity grows slower than for
previous solutions. We demonstrate that Infostop can be
used to easily infer social meetings. Finally, we provide
an open-source implementation of Infostop, written in
Python and C++, that has a simple API and can be used both
for labeling time-ordered coordinate sequences (GPS or
otherwise), and unordered sets of spatial points.
preferred-citation:
type: article
authors:
- family-names: "Aslak"
given-names: "Ulf"
orcid: "https://orcid.org/0000-0003-4704-3609"
- family-names: "Alessandretti"
given-names: "Laura"
orcid: "https://orcid.org/0000-0001-6003-1165"
journal: "arXiv preprint"
doi: 10.48550/arXiv.2003.14370
title: "Infostop: scalable stop-location detection in multi-user mobility data"
year: 2020
arxiv: "2003.14370"
GitHub Events
Total
- Issues event: 2
- Watch event: 2
- Issue comment event: 3
- Push event: 1
- Pull request event: 2
- Fork event: 2
Last Year
- Issues event: 2
- Watch event: 2
- Issue comment event: 3
- Push event: 1
- Pull request event: 2
- Fork event: 2
Committers
Last synced: about 1 year ago
Top Committers
| Name | Commits | |
|---|---|---|
| ulfaslak | u****n@g****m | 59 |
| Ulf | u****k@U****l | 7 |
| Alessandretti | 8****7@s****t | 6 |
| Ulf Aslak Jensen | u****e@h****k | 5 |
| Endre Mark Borza | e****a@g****m | 2 |
| lalessan | l****n | 2 |
| Laura Alessandretti | l****e@h****k | 2 |
| Ulf | u****k@u****e | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 19
- Total pull requests: 10
- Average time to close issues: about 1 month
- Average time to close pull requests: about 24 hours
- Total issue authors: 16
- Total pull request authors: 6
- Average comments per issue: 1.42
- Average comments per pull request: 1.5
- Merged pull requests: 8
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 2
- Pull requests: 2
- Average time to close issues: N/A
- Average time to close pull requests: about 8 hours
- Issue authors: 1
- Pull request authors: 1
- Average comments per issue: 0.0
- Average comments per pull request: 1.5
- Merged pull requests: 2
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- lalessan (2)
- charlesdaigle (2)
- katrinabrock (2)
- carlomarxdk (1)
- lcandeago (1)
- sp794uk (1)
- alexandradec (1)
- jinzhuyu (1)
- dkori (1)
- ulfaslak (1)
- NA-Dev (1)
- janpiskur (1)
- jGaboardi (1)
- VRehnberg (1)
- michelegirolami (1)
Pull Request Authors
- lalessan (3)
- urosjarc (2)
- endremborza (2)
- VRehnberg (1)
- ulfaslak (1)
- michielbakker (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
-
Total downloads:
- pypi 177 last-month
- Total dependent packages: 1
- Total dependent repositories: 2
- Total versions: 20
- Total maintainers: 1
pypi.org: infostop
Temporospatial clustering in Python. Well suited for mobility data.
- Homepage: https://github.com/ulfaslak/infostop
- Documentation: https://infostop.readthedocs.io/
- License: MIT License
-
Latest release: 0.1.9
published about 4 years ago
Rankings
Maintainers (1)
Dependencies
- pip >=18.0
- folium >=0.7.0
- infomap ==1.0.6
- numpy *
- pybind11 >=2.4
- scikit-learn *
- tqdm *
- folium >=0.7.0
- infomap ==1.0.6
- numpy *
- pybind11 >=2.4
- scikit-learn *
- tqdm *
- actions/checkout v2 composite
- actions/setup-python v2 composite
- pypa/gh-action-pypi-publish release/v1 composite