Metadata-Version: 2.1
Name: trackintel
Version: 0.5.0
Summary: Human mobility and movement analysis framework.
Home-page: https://github.com/mie-lab/trackintel
Author: Dominik Bucher, Henry Martin, Ye Hong
Author-email: dobucher@ethz.ch, martinhe@ethz.ch
License: MIT
Description: 
        # The trackintel Framework
        
        
        [![PyPI version](https://badge.fury.io/py/trackintel.svg)](https://badge.fury.io/py/trackintel)
        [![Build Status](https://travis-ci.org/mie-lab/trackintel.svg?branch=master)](https://travis-ci.org/mie-lab/trackintel)
        [![Documentation Status](https://readthedocs.org/projects/trackintel/badge/?version=latest)](https://trackintel.readthedocs.io/en/latest/?badge=latest)
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        *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 transport planning [[1]](#1). 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](https://pandas.pydata.org/) and [GeoPandas](https://geopandas.org/#)
        
        You can find the documentation on the [trackintel documentation page](https://trackintel.readthedocs.io/en/latest).
        
        Try *trackintel* online in a MyBinder notebook: [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/mie-lab/trackintel/master?filepath=%2Fexamples%2Fexample_geolife%2FTrackintel_introduction.ipynb)
        
        ## Data model
        
        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)
        
        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: `geopandas dataframes` (recommended), `csv files` in a specified format, `postGIS` databases. 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
        
        ## Installation and Usage
        *trackintel* is on [pypi.org](https://pypi.org/project/trackintel/), you can install it with `pip install trackintel` as long as `GeoPandas` is already installed. 
        
        You should then be able to run the examples in the `examples` folder or import trackintel using:
        ```{python}
        import trackintel
        ```
        
        ## Development
        You can install *trackintel* locally using `pip install .`.
        For quick testing, use `trackintel.print_version()`.
        
        Testing is done using [pytest](https://docs.pytest.org/en/latest).
        Simply run the tests using `pytest` in the top-level trackintel folder.
        In case you use `pipenv`, install *pytest* first (`pip install pytest`), then run *pytest* using this version: `python -m pytest`.
        The use of [fixtures](https://pypi.org/project/fixtures/) for data generation (e.g., trips and trackpoints) is still an open todo.
        As for now, there are some smaller datasets in the `tests` folder.
        
        Versions use [semantic numbering](https://semver.org/).
        Commits follow the standard of [Conventional Commits](https://www.conventionalcommits.org).
        You can generate them easily using [Commitizen](https://github.com/commitizen/cz-cli).
        
        You can find the development roadmap under `ROADMAP.md` and coding conventions under `CONTRIBUTING.md`.
        
        ### Documentation
        
        The documentation follws the [pandas resp. numpy docstring standard](https://pandas-docs.github.io/pandas-docs-travis/development/contributing.html#contributing-to-the-documentation).
        In particular, it uses [Sphinx](http://www.sphinx-doc.org/en/master/) to create the documentation.
        You can install Sphinx using `pip install -U sphinx` or `conda install sphinx`.
        
        If you use additional dependencies during development, do not forget to add them to `autodoc_mock_imports` in `docs/conf.py` for readthedocs.org to work properly.
        
        You can then generate the documentation using `sphinx-build -b html docs docs.gen`.
        This will put the documentation in `docs.gen`, which is in `.gitignore`.
        
        ### Continuous Integration
        
        There are travis and appveyor CIs set up for Unix/Windows builds.
        You can find the corresponding scripts in `.travis.yml` and `appveyor.yml`.
        Adding [Coveralls](https://coveralls.io) is an open todo.
        
        ## Contributors
        
        *trackintel* is primarily maintained by the Mobility Information Engineering Lab at ETH Zurich ([mie-lab.ethz.ch](http://mie-lab.ethz.ch)).
        If you want to contribute, send a pull request and put yourself in the `AUTHORS.md` file.
        
        ## References
        <a id="1">[1]</a>
        [Axhausen, K. W. (2007). Definition Of Movement and Activity For Transport Modelling. In Handbook of Transport Modelling. Emerald Group Publishing Limited.](
        https://www.researchgate.net/publication/251791517_Definition_of_movement_and_activity_for_transport_modelling)
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Requires-Python: >=3.6.0
Description-Content-Type: text/markdown
