Data Model - mie-lab/trackintel GitHub Wiki

In trackintel, tracking data is split into several classes. It is not generally assumed that data is already available in all these classes, instead, trackintel provides functionality to generate everything starting from the raw GPS data (consisting of (longitude, latitude, accuracy, timestamp, user_id) pairs).

• positionfixes: Raw GPS data
• triplegs: Segments covered with one mode of transport
• trips: Segments between consecutive staypoints
• customer movements: Sequences of triplegs which use only public transport
• tours: Sequences of trips which start and end at the same place (if journey is set to True, this place is home)
• staypoints: Locations where a user spent a minimal time
• places: Clustered staypoints

Additionally, some of the more time-consuming functions of trackintel generate logging data, as well as extracted features data, and they assume more data about geographic features or characteristics of transport modes are available. These are explained here:

• Tracking data
• Transport mode data
• User data
• Geographic data
• Logging data
• Extracted features data
• Visualization data