Human mobility and movement analysis framework.
The trackintel Framework
trackintel is a library for the analysis of spatio-temporal tracking data with a focus on human mobility. The core of trackintel is the hierachical data model for movement data that is used in GIS, transport planning and related fields . We provide functionalities for the full life-cycle of human mobility data analysis: import and export of tracking data of different types (e.g, trackpoints, check-ins, trajectories), preprocessing, data quality assessment, semantic enrichment, quantitative analysis and mining tasks, and visualization of data and results. Trackintel is based on Pandas and GeoPandas
You can find the documentation on the trackintel documentation page.
An overview of the data model of trackintel:
- positionfixes (Raw tracking points, e.g., GPS recordings or check-ins)
- staypoints (Locations where a user spent time without moving, e.g., aggregations of positionfixes or check-ins)
- activities (Staypoints with a purpose and a semantic label, e.g., meeting to drink a coffee as opposed to waiting for the bus)
- locations (Important places that are visited more than once, e.g., home or work location)
- triplegs (or stages) (Continuous movement without changing mode, vehicle or stopping for too long, e.g., a taxi trip between pick-up and drop-off)
- trips (The sequence of all triplegs between two consecutive activities)
- tours (A collection of sequential trips that return to the same location)
An example plot showing the hierarchy of the trackintel data model can be found below:
The image below explicitly shows the definition of locations as clustered staypoints, generated by one or several users.
You can enter the trackintel framework if your data corresponds to any of the above mentioned movement data representation. Here are some of the functionalities that we provide:
- Import: Import from the following data formats is supported:
csv filesin a specified format,
postGISdatabases. We also provide specific dataset readers for popular public datasets (e.g, geolife).
- Aggregation: We provide functionalities to aggregate into the next level of our data model. E.g., positionfixes->staypoints; positionfixes->triplegs; staypoints->locations; staypoints+triplegs->trips; trips->tours
- Enrichment: Activity semantics for staypoints; Mode of transport semantics for triplegs; High level semantics for locations
How it works
trackintel provides support for the full life-cycle of human mobility data analysis.
[1.] Import data.
import geopandas as gpd import trackintel as ti # read pfs from csv file pfs = ti.io.file.read_positionfixes_csv(".\examples\data\pfs.csv", sep=";", index_col="id") # or with predefined dataset readers (here geolife) pfs, _ = ti.io.dataset_reader.read_geolife(".\tests\data\geolife_long")
[2.] Data model generation.
# generate staypoints and triplegs pfs, sp = pfs.as_positionfixes.generate_staypoints(method='sliding') pfs, tpls = pfs.as_positionfixes.generate_triplegs(sp, method='between_staypoints')
# plot the generated tripleg result tpls.as_triplegs.plot(positionfixes=pfs, staypoints=sp, staypoints_radius=10)
# e.g., predict travel mode labels based on travel speed tpls = tpls.as_triplegs.predict_transport_mode() # or calculate the temporal tracking coverage of users tracking_coverage = ti.temporal_tracking_quality(tpls, granularity='all')
[5.] Save results.
# save the generated results as csv file sp.as_staypoints.to_csv('.\examples\data\sp.csv') tpls.as_triplegs.to_csv('.\examples\data\tpls.csv')
For example, the plot below shows the generated staypoints and triplegs from the imported raw positionfix data.
Installation and Usage
trackintel is on pypi.org, you can install it in a
GeoPandas available environment using:
pip install trackintel
You should then be able to run the examples in the
examples folder or import trackintel using:
import trackintel as ti ti.print_version()
Requirements and dependencies
You can find the development roadmap under
ROADMAP.md and further development guidelines under
trackintel is primarily maintained by the Mobility Information Engineering Lab at ETH Zurich (mie-lab.ethz.ch).
If you want to contribute, send a pull request and put yourself in the
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