v1.0.0

Trackintel reaches its v1.0 milestone! We implement all functionalities we promised in the ROADMAP.md. Especially, the changes are:

Major new features and enhancements:

ENH: activity inference module is now available. Activity labels can be inferred for locations based on movement data only (#212)
ENH: tracking quality functions can now be assessed through accessors (#201)
ENH: add function to check and/or transform into WGS84: trackintel.geogr.distances.check_gdf_crs() (#206)

Documentation, testing and clean-up

DOC: we provide a tutorial showing the main functionality of trackintel (#204)
CLN: rearrangement of analysis module (#200)
BUG: bug fixes for preprocessing functions (#217)
CLN: other minor updates (#205, #207, #210)

Acknowledgments:

Thanks to everyone who contributed to this release!

• @abcnishant007
• @bifbof
• @henrymartin1
• @hong2223

v0.6.0

trackintel is now one step closer to our v1.0 goal. Most notably trackintel has updated the staypoint and tripleg generation algorithms (#194, #193) which covers more cases and are all temporal gap aware! trackintel tutorial is now available and focuses on the core functionalities (#190, #185). Our IO functions are now cleaner (#192) and much more efficient (#175, #179).

Major new features and enhancements:

ENH: rearange trajectory distance module (#187)
ENH: faster csv reading (#175, #179)
ENH: we now use black to reformat our code (#196, #195)
ENH: we update our staypoint and tripleg generation algorithms (#194, #193)
ENH: trackintel tutorial now available (#190, #185)

Documentation, testing and clean-up

DOC: readme.md now contains simple instruction on using trackintel (#198)
DOC: ensure consistency in IO and Model modules (#192, #179)
TST: rearrange test file structure and increases test coverage (#197)
TST: additional tests for spatial filter function (#182)
CLN: other minor updates (#189, #186)

Acknowledgments:

Thanks to everyone who contributed to this release!

• @abcnishant007
• @bifbof
• @henrymartin1
• @hong2223
• @svenruf

v0.5.3

In this release trackintel made great progress in its analysis and visualization capabilities. Most notably trackintel can now be used to analyze the tracking coverage (#140) and calculate the modal split of a user (#166). Furthermore our visualization functions are now cleaner (#135) and we have added an additional plotting function for modal splits (#166).

Major new features and enhancements:

ENH: modal split calculation and visualization (#166)
ENH: More versatile plotting functions (#135)
ENH: Better WGS84 support (#147, #161)
ENH: tracking quality module (#140, #164)
ENH: Support Geolife modes (#155)

Documentation, testing and clean-up

TST: Use standard geopandas testing function (#165)
DOC: Added convention for trackintel datatypes (#145)
DOC: Update columns in datamodel for hierarchical model (#151)
DOC: update image path (#156)
DOC: add plot_tripleg in docs and change docstrings (#157)
CLN: Added explicit parameters for read_csv functions. (#148)
CLN: Other minor updates (#129, #171, #172)

Acknowledgments:

Thanks to everyone who contributed to this release!

• @abcnishant007
• @bifbof
• @henrymartin1
• @hong2223

v0.5.1

Trackintel is under very active development. Because of that many features that are not on the official roadmap were included and many different issues were fixed. The main contributions featured in this release are concerning consistency and updates to the documentation such as a new illustration of the data model (#127).

New features and improvements:

• The tripleg generation was reworked and tested #124
• Performance update for the generate_staypoints method #115
• the trackintel read_csv functions now support a crs #137

Consistency

• ID is now always used as an index in the dataframes #120
• Missing data is now always modeled as np.nan #126, also in integer datatypes #136

Bug fixes:

• A fix for the auto-release workflow that triggered multiple actions #113
• All accessors now display the correct error message in their validation method #138

Acknowledgments:

Thanks to everyone who contributed to this release!

• bifbof
• henrymartin1
• hong2223
• svenruf

v0.5.0

Hierarchical data model completed.

0.4.1

Update __version__.py

Vanilla

Experimental/efficient trip generation (#72)

* efficient implementation of trip generation

* add tqdm into dependencies

* efficient implementation of trip_id assignment

* add comments

* allow flexible index names for spts and tpls