Events

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Events
Situational events describe what a user is doing. Events are derived from time, location and motion data.
They are available as a timeseries of stationaries and transports with duration.
Is the user in transport? In a car? Or a train? Walking? What route?
Is the user stationary? What store or business is at that location?
Is the location a home or work location?

Stationary Events
When a user is stationary for a few minutes at a certain location, a Stationary Event will be available.
This Stationary Event is enriched with:
The significance of what this place means to the user based on historical data.
Values: home, work, regular, non-regular, poi (point of interest), new.
Place/venue information like name and categories
Basic address information like country, city and city_type
Example
​Lookup the Stationary event in the data model reference.
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{
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  "type": "Stationary",
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  "start": "2017-02-22T08:34:50.785+01:00",
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  "end": "2017-02-22T16:24:55.244+01:00",
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  "event_id": "6dbc1960-03ef-429a-b6f5-befb7607ae46",
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  "latitude": 51.19666,
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  "longitude": 4.40816,
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  "location": {
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    "significance": "work",
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    "place": {
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      "name": "Sentiance HQ",
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      "category_hierarchy": [
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        "office",
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        "private",
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        "company"
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      ]
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    }
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  },
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  "address": {
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    "country": "België - Belgique - Belgien",
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    "city": "Antwerpen",
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    "city_type": "city"
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  }
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}
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Transport Events
When a user is not stationary at a location, the user is in transport. For each of these transports a Transport Event will be available.
Additional data derived from sensor data--distance, transport mode, advanced map-matching--is computed to build an accurate trajectory and driving behavior analysis.
Transport Modes can include car, walking, biking, train, bus, tram, flight, etc. (check the full list here).
Depending on SDK configuration, additional features are derived from the sensor data. 
Only available in full SDK configuration:
behavior_scores
behavior_features
behavior_annotations
Example
​Lookup the Transport event in the data model reference.
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{
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  "type": "Transport",
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  "start": "2017-02-19T15:13:00.000+01:00",
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  "end": "2017-02-19T17:05:00.000+01:00",
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  "event_id": "830a3534edca6c0eb24faa19f0c137dc7877592384409de43a30586643272f65",
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  "mode": "car",
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  "distance": 158773,
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  "waypoints": [
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    {
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      "type": "Waypoint",
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      "latitude": 52.35004,
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      "longitude": 4.87475,
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      "timestamp": "2017-02-19T15:13:31.968+01:00",
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      "accuracy": 25
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    },
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    ...
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  ],
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  "trajectory": {
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    "type": "TransportTrajectory",
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    "encoded": "quo~Hmaw\\ACW{... polyline encoding}",
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    "waypoints": [
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      {
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        "type": "TrajectoryWaypoint",
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        "latitude": 52.35049,
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        "longitude": 4.87463,
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        "timestamp": "2017-02-19T15:13:29.967+01:00",
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        "road_type": "tertiary",
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        "speed": 0,
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        "distance": 1.4,
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        "speed_limit": 50
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      },
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      ...
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    ]
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  },
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  "behavior_scores": {
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    "type": "CarBehaviorScores",
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    "overall": 0.67,
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    "smooth": 0.91,
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    "legal": 0.79,
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    "anticipative": 0.3
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  },
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  "behavior_annotations": [
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    {
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      "type": "AccelerationBehaviorAnnotation",
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      "start": "2017-02-19T15:43:32.161+01:00",
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      "end": "2017-02-19T15:43:38.851+01:00",
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      "duration": 6690,
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      "acceleration": "accelerate",
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      "magnitude": 0.096
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    },
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    {
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      "type": "AccelerationBehaviorAnnotation",
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      "start": "2017-02-19T15:50:42.146+01:00",
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      "end": "2017-02-19T15:50:55.042+01:00",
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      "duration": 12897,
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      "acceleration": "brake",
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      "magnitude": 0.261
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    },
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    ...
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  ],
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  "behavior_features": {
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    "type": "CarBehaviorFeatures",
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    "phone_handling": 70000,
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    "distance_during_annotations": 27455
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  }
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}
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Contents
Stationary Events
Transport Events