This document explains the functionality planned for various releases.
Steps until we get to version 1.0.0.
- ✔️ Read positionfixes, triplegs, staypoints, locations, trips from CSV files.
- ✔️ Read positionfixes, triplegs, staypoints, locations, trips from PostGIS.
- ✔️ Write positionfixes, triplegs, staypoints, locations, trips to CSV files.
- ✔️ Write positionfixes, triplegs, staypoints, locations, trips to PostGIS.
- ✔️ Visualize positionfixes, triplegs, staypoints geographically.
- ✔️ Extract triplegs and staypoints from positionfixes.
- Functions for smoothening tripleg data (e.g., Douglas-Peucker).
- Extract trips from triplegs and staypoints
- ✔️ Extract locations from staypoints.
- ✔️ Create transition graphs from locations.
- ✔️ Provide and visualize a range of (transition) graph measures.
- Simple outlier filtering for triplegs
- Augment model of triplegs and staypoints in such a way that they can hold references to trips and locations.
- Support flexible geometry columns Issue15
- Filter data by geographic location (e.g., all data within Switzerland)
- Enhance tripleg extraction to be gap aware issue27
- Simple transport mode prediction. This functionality is based on properties of the movement data only.
- Simple activity inference. This functionality is based on properties of the movement data only and should include {Home, Work, Other}.
- Data quality assessments: statistical measures and visualization (e.g., of tracking frequency, both spatial and temporal).
- Tutorial for trackintel
- Documentation
- Link a guide to the readme how to contribute to the documentation
- The documentation should be complete (all functions and methods are mentioned)
- The documentation should not be focused on PostGIS
Ideas for a later version.
I/O
- Read customermovements, tours from files.
- Read customermovements, tours from PostGIS.
- Write customermovements, tours to files.
- Write customermovements, tours to PostGIS.
- Include data input/output methods for more common datasets
Preprocessing
- Implement tours (starting and ending at a persons home location) and customermovements (consecutive triplegs with using transport provided by a single provider (e.g., a local bus company)).
- Short walks to/from cars/buses/etc.: These are often not recognized by the tracking applications.
- Map match triplegs (based on transport mode identification).
- Imputation of trivial gaps in tracking data (triplegs and staypoints).
Augment tracking data:
- Better transport mode prediction based on movement data (speed, associated features from accelerometer data, etc.) as well as by aligning them with context data such as from OpenStreetMap or GTFS departure schedules.
- Add context data to movement trajectories (using spatio-temporal map algebra).
- Better activity prediction. This functionality is based on properties of the movement data and on context data such as points of interests.
- Provide holistic inference of transport modes (take into account the fact that if someone uses the car to reach a certain location, the person is likely to leave by car as well).
Analysis
- Mobility behaviour (and its changes): automatic detection of uncharacteristic changes in mobility patterns.
- User profiling and clustering.
- Anomaly detection (based on properties of movement data as well as contextual factors).
- Extract customermovements from triplegs and trips.
- next place prediction (e.g., markov-model)
- Anomaly detection (based on properties of movement data as well as contextual factors).
- Clustering of triplegs using similarity metrics
- Include the calculation of common mobility indicators (e.g., radius of gyration)
Visualization
- Visualize customermovements, tours geographically.
- Visualize trips and locations geographically, e.g., by coloring triplegs differently or by making a buffer around all staypoints that are part of a locations.
Various
- Parallelization