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Data Reference A-G

Objects

AccelerationBehaviorAnnotation

Kind: Object
Property
Type
Description
type
'AccelerationBehaviorAnnotation'
start
String
end
String
duration
Int
Duration is in milliseconds.
acceleration
magnitude
Float
The longitudinal g-force you experience during accelerations and brakes, measured in (0.2*m)/s². Doing a fast acceleration will result in a higher value compared to a slower acceleration.

AccelerationEnum

Kind: ENUM
    accelerate
    brake
    coast

AccessToken

Kind: Object
Property
Type
Description
type
String
'AccessToken'
token
String
The access (bearer) token
expires_at
String
When this token will expire

Account

Kind: Object
Property
Type
Description
type
String
'Account'
id
String
display_name
String
created_at
String
applications
The applications that belong to this account.

Address

Kind: Object
An Address describes a reverse geocoded location.
Property
Type
Description
type
String
'Address'
country
String
city
String
city_type
String

AggregatedDistanceAnomaly

Kind: Object
Property
Type
Description
type
'AggregatedDistanceAnomaly'
start
String
end
String
analysis_type
anomaly
Anomaly
sigma
Float
The observed standard deviation from expected behavior for this anomaly. If the standard deviation is high, the anomaly confidence will be low.
probability
Float
The larger the probability, the more anomaly. Value is between 0.0 and 1.0.
observed_distance
Float
Observed distance in meter.
expected_distance
Float
Expected distance in meter.
period
Aggregation period over which the data is calculated.
day_part
DayPart
Optional additional aggregation over which the data is calculated.
place_category
String
location_significance
transport_mode
transport_mode_category
moment_definition_id
String

AggregatedDurationAnomaly

Kind: Object
Property
Type
Description
type
'AggregatedDurationAnomaly'
start
String
end
String
analysis_type
anomaly
Anomaly
sigma
Float
The observed standard deviation from expected behavior for this anomaly. If the standard deviation is high, the anomaly confidence will be low.
probability
Float
The larger the probability, the more anomaly. Value is between 0.0 and 1.0.
observed_duration
Float
Observed duration in seconds.
expected_duration
Float
Expected duration in seconds.
period
Aggregation period over which the data is calculated.
day_part
DayPart
Optional additional aggregation over which the data is calculated.
place_category
String
location_significance
transport_mode
transport_mode_category
moment_definition_id
String

AnalysisType

Kind: ENUM
    processed: The most detailed processing with the highest latency. This processing also takes into account motion sensor data, does advanced timelining corrections.
    indepth: When data is analyzed with potentially more information available compared to the initial preliminary analysis. This type of processing also takes into account motion sensor data.
    preliminary: The initial realtime analysis of data with the lowest latency.
    The platform analyzes data in multiple stages, and updates values over time, how well the data is analyzed, can be identified by the analysis type.

Anomaly

Kind: ENUM
    transport_distance
    transport_duration
    transport_mode_distance
    transport_mode_duration
    transport_mode_category_distance
    transport_mode_category_duration
    transport_day_part_distance
    transport_day_part_duration
    transport_mode_category_occurrence_count
    stationary_location_significance_duration
    stationary_location_significance_day_part_duration
    stationary_place_category_day_count
    stationary_location_significance_day_part_day_count
    stationary_place_category_duration
    stationary_place_category_occurrence_count
    moment_duration
    commute_distance
    commute_duration: This is an aggregation over the commute_from_home and commute_from_work moments, it includes small stationaries during commutes (such as stopping at the gas station).
    commute_transport_mode_category_duration
    working_duration
    dogwalk_occurrence_count

AnomalyBehaviorAnnotation

Kind: Object
Property
Type
Description
type
'AnomalyBehaviorAnnotation'
start
String
end
String
duration
Int
Duration is in milliseconds.

AnomalyTimePeriod

Kind: ENUM
    day: Anomaly calculated on a daily basis.
    week: Anomaly calculated on a weekly basis.
    month: Anomaly calculated on a montly basis.
    semantic_day: Anomaly calculated on the semantic day of the user.
    weekend_day: Anomaly calculated on a daily basis, ex: each day of the weekend you are at home on average N hours, then if you are not home both days = 2 anomalies (one for saturday, one for sunday) will be available.

AnomalyType

Kind: ENUM
    DistanceAnomaly
    AggregatedDistanceAnomaly
    DurationAnomaly
    AggregatedDurationAnomaly
    DayCountAnomaly
    OccurrenceCountAnomaly

Application

Kind: Object
An Application refers to an integration of the mobile SDK and pools together the users from this mobile app.
Property
Type
Description
type
String
'Application'
id
String
The unique identifier for this application.
secret
String
The secret required when integrating the mobile SDK with this application ID.
name
String
The name of this application. For mobile apps it's the name how it's available in the AppStore/PlayStore.
description
String
A short description of this application.
logging
String
An optional override for the default debug logging behavior of the mobile SDKs.
flavor
String
An optional override for the default flavor configuration of the mobile SDKs.
project_code
String
An optional project code used to assign users from the demo applications to this application.
created_at
String
The time when this user was created, ISO8601. Example: 2015-05-28T14:37:14.839+00:00.
account_id
String
The developer account this application belongs to.
activity
Activity information for this application, contains active users and no of installs
users
The users that belong to this application.
active_users
The users that belong to this application and have been active in the last 7 days.
trips
Trips across all users recorded against this application.

ApplicationActivity

Kind: Object
Access the activity information
Property
Type
Description
type
String
'ApplicationActivity'
active_users
The active users for the application
installs
The number of installs for the application

ApplicationActivityGroup

Kind: Object
Access the information for groups: ACTIVE_USERS, INSTALLS
Property
Type
Description
type
String
'ApplicationActivityGroup'
daily
The daily application activity data for type and group
monthly
The monthly application activity data for the type and group

ApplicationActivityGroupItem

Kind: Object
Access the daily and monthly activity information
Property
Type
Description
type
String
'ApplicationActivityGroupItem'
date
String
Date
count
Float
Count

ApplicationsConnection

Kind: Object
Access the application nodes
Property
Type
Description
type
String
'ApplicationsConnection'
slice
The individual application nodes. Provide the right paging parameters to slice your response data. By default a slice holds up to 100 applications.
paging
Paging

BehaviorAnnotationAnomalyEnum

Kind: ENUM
    phone_handling

BehaviorAnnotationPathWaypoint

Kind: Object
Property
Type
Description
type
String
'BehaviorAnnotationPathWaypoint'
latitude
Float
longitude
Float

BigInt

Kind: SCALAR
The BigInt scalar type represents non-fractional signed whole numeric values. BigInt can represent values between -(2^53) + 1 and 2^53 - 1.

BoundaryBehaviorAnnotation

Kind: Object
Property
Type
Description
type
'BoundaryBehaviorAnnotation'
start
String
end
String

BoundaryBehaviorAnnotationQuality

Kind: ENUM
    valid: Section of the transport where the event detection was successful.

Branch

Kind: Object
A possible series of events predicted for the user.
Property
Type
Description
id
String
Id of the branch unique among all the branches returned for the user at the given time.
probability
Float
Percentage probability of the events of this beam taking place.
events
The list of events predicted to occur.

CarBehaviorFeatures

Kind: Object
Property
Type
Description
type
String
'CarBehaviorFeatures'
phone_handling
Int
Total time in milliseconds we detected phone handling by the user during this transport. Value will be -1 when the transport data was not sufficient.
distance_during_annotations
Int
Distance in meter during which the system had good quality sensor data available to observe transport behavior. Value will be -1 when the transport data was not sufficient.

CarBehaviorScores

Kind: Object
Property
Type
Description
type
String
'CarBehaviorScores'
focus
Float
The proportion of time (percentage) the user is focused while driving, being focused means: not using the phone, which is detected through phone handling.
mounted
Float
The proportion of time (percentage) the phone is mounted while driving.
hard_accel
Float
Measures how often you accelerate hard. Every hard acceleration will be penalised by subtracting a percentage of your score. When we do not have sufficient data this value will be -1.
A hard acceleration is detected when the magnitude of an acceleration exceeds 2.5m/s2.
hard_brake
Float
Measures how often you need to brake hard. Every hard brake will be penalised by subtracting a percentage of your score. When we do not have sufficient data this value will be -1.
A hard brake is detected when the magnitude of a brake exceeds 3m/s2.
hard_events
Float
This is a combination of hard_accel and hard_brake score. The hard brakes and accelerations are also normalized by the total number of events. When we do not have sufficient data this value will be -1.
legal_v2
Float
The legal driving score measures how well you adhere to speed limits. The higher your score, the more you respect the speed limits! When we do not have sufficient data the value will be -1.
hard_turn
Float
Measures how often you turn hard. Every hard turn will be penalised by subtracting a percentage of your score. When we do not have sufficient data this value will be -1.
A hard turn is detected when the magnitude of a turn exceeds 3.75m/s2.
smooth_v2
Float
The smooth driving score measures how smooth you drive. High accelerations, heavy braking and heavy turning result in a lower score. Scores are normalized with respect to a wide population. The higher your score, the smoother you drive! When we do not have sufficient sensor data characterizing your drive, the value will be -1. Beside an updated normalization, the difference with smooth score v1 is that turns are also taken in account.
anticipative_v2
Float
The anticipative driving score measures how well you anticipate turns. Hard accelerations before or hard braking during a turn result in a lower score. The higher your score, the more anticipative you drive! When we do not have sufficient sensor data characterizing your drive, the value will be -1. Beside an updated normalization, this new version has a more accurate detection of brakes in turns.
handheld_calling
Float
A score based on how much time you spent using your phone and calling (1 means good behavior).
handheld_calling_duration
Int
Total time in seconds the user was calling while holding the phone during the transport.
handsfree_calling
Float
A score based on how much time you spent calling without holding your phone (1 means good behavior).
handsfree_calling_duration
Int
Total time in seconds the user was calling handsfree during the transport.
handling_without_calling
Float
A score based on how much time you spent holding your phone without calling (assume typing, texting etc.)
handling_without_calling_duration
Int
Total time in seconds the user held the phone without calling (assume typing, texting etc.) during the transport.
attention
Float
A combined score of handheld_calling, handsfree_calling and handling_without_calling.

CityMoment

Kind: Object
Implements: IMoment An occurrence of a City moment that we have detected for a user.
Property
Type
Description
type
'CityMoment'
start
String
The time this moment started, ISO8601. Value can change and become more accurate over time. Example: 2015-05-28T14:37:14.839+00:00
end
String
The time this moment ended, ISO8601. Value can be null. Value can change and become more accurate over time. Example: 2015-05-28T14:37:14.839+00:00
start_ts
BigInt
end_ts
BigInt
analysis_type
How well this moment is analyzed by the platform, this value will update over time. Possible values: preliminary, indepth, processed.
moment_definition_id
String
The ID of the MomentDefinition this moment relates to.
moment_definition
The MomentDefinition this moment relates to.
city_name
String
The name of the city this moment applies to.

CommuteTimeAggregate

Kind: Object
Property
Type
Description
type
'CommuteTimeAggregate'
period
transport_duration
Float
mode_category

ControlUser

Kind: Object
Implements: IUser A user that can authenticate using either password or token strategies, has an email address, might have access to dashboards, might have multiple roles, might manage multiple accounts and applications.
Property
Type
Description
type
UserType
'ControlUser'
email
String
The email address that is optionally linked to provide access to the https://developers.sentiance.com and others.
account_roles
The accounts this user has elevated permissions to.
id
String
The unique identifier for this user.
can_login
Boolean
created_at
String
The time when this user was created, ISO8601. Example: 2015-05-28T14:37:14.839+00:00
sdk
application_id
String
The ID of the Application this user relates to.
application
The Application this user relates to.
custom_event_history
Custom Event History
event_history
IEvent
An unordered list of events we have detected for this user.
car_behavior
The user car behavior aggregated over the last 9 weeks.
aggregated_driving_scores
transport_heatmaps
The aggregated transport heatmaps calculated over time.
metadata
JSON
All custom set metadata properties on this user. This is a JSON object with key->value pairs.
device
The last known active tracking device metadata
active_moments
IMoment
An unordered list of moments that are ongoing from the point of view of the platform.
moment_history
IMoment
An unordered list of moments we have detected for this user.
semantic_time
The user's semantic time averaged over time.
anomaly_history
IAnomaly
segments
ISegment
An unordered list of segments that are detected for this user.