Quote from CounterPoint Admin on August 4, 2018, 17:56
Hi,
I can explain further on the data structure for the csv.
Remember there are currently 4 count types (Easy, Regular, Carpool, Bicycle Gender) and 11 conditions.
The columns that relate to an Easy Count are:
- walk_generic
- bike_generic
- vehicle_personal
- vehicle_oversized
The columns that relate to a Carpool Count are:
- onePerson
- twoPerson
- threePerson
- fourPlusPerson
The columns that relate to a Bicycle Gender Count are
- gender_m (male)
- gender_f (female)
- gender_o (other/unsure)
The columns that relate to a Regular Count are: (These can be collapsed down into “Easy Count” columns if needed)
- bike_adult
- bike_child
- bike_baby (in-app bike passenger)
- bike_cargo
- bike_child_trailer
- walk_adult
- walk_child
- walk_stroller
- walk_disability
- walk_skateboard
- walk_visually_impaired
- walk_physically_impaired (walk)
- vehicle_car
- vehicle_moto
- vehicle_semi
- vehicle_transit (oversized)
The columns that relate to Conditions are:
- sunny
- cloudy
- raining
- snowing
- night
- construction
- special_event
- road_closed
- icy_roads
- wet_roads
- muddy_roads
The other columns:
- counterpoint_id: this is the unique identifier of the counterpoint the dataset came from. In one download there will only be one counterpoint_id
- count_session_id: This is the unqie identifier of each counting seasion that happenned at the counterpoint in question.
- count_id: This is the unique id of the minute during the cross section. We originally decided to group a count_session’s data into minute sections. Each minute section is a row in the csv, that has a parent count_session and the count_session has a parent of a counterpoint.
- user_id: Represents the unique id of the user that conducted the counterpoint that conducted this count. You can use this to group or segment your data how you like.
- team_id: Represents the unique identifier of a team that conducted this count. This helps if you are only interested in seeing data done by your team at this counterpoint.
- name: The name of the counterpoint
- intersection: The nearest intersection of the counterpoint
- description: The description or “comments” of the counterpoint
- lat: The latitude part of the coordinate of the counterpoint
- lng: The longitude part of the coordinate of the counterpoint
- start_datetime: The start time of the counting session
- end_datetime: The end time of the counting session
- elapsed_time: The difference between end_datetime and start_datetime in seconds
- timezone: The timezone of the counterpoint. This is needed if you want to compare counts across multiple timezones in their local time. Say you want to compare counts across Canada that all happen at 5:00pm local. Use the start_datetime and start_endtime rather than utc_start_datetime and utc_end_datetime.
- timestamp: The minute by minute timestamp of the count. All the timestamps associated to one unique count_session (count_session_id) should all be 1 minute apart.
Further, I will write this up in a better form of documentation, as well as adding these descriptions to the second row of the csv export.
Hi,
I can explain further on the data structure for the csv.
Remember there are currently 4 count types (Easy, Regular, Carpool, Bicycle Gender) and 11 conditions.
The columns that relate to an Easy Count are:
- walk_generic
- bike_generic
- vehicle_personal
- vehicle_oversized
The columns that relate to a Carpool Count are:
- onePerson
- twoPerson
- threePerson
- fourPlusPerson
The columns that relate to a Bicycle Gender Count are
- gender_m (male)
- gender_f (female)
- gender_o (other/unsure)
The columns that relate to a Regular Count are: (These can be collapsed down into “Easy Count” columns if needed)
- bike_adult
- bike_child
- bike_baby (in-app bike passenger)
- bike_cargo
- bike_child_trailer
- walk_adult
- walk_child
- walk_stroller
- walk_disability
- walk_skateboard
- walk_visually_impaired
- walk_physically_impaired (walk)
- vehicle_car
- vehicle_moto
- vehicle_semi
- vehicle_transit (oversized)
The columns that relate to Conditions are:
- sunny
- cloudy
- raining
- snowing
- night
- construction
- special_event
- road_closed
- icy_roads
- wet_roads
- muddy_roads
The other columns:
- counterpoint_id: this is the unique identifier of the counterpoint the dataset came from. In one download there will only be one counterpoint_id
- count_session_id: This is the unqie identifier of each counting seasion that happenned at the counterpoint in question.
- count_id: This is the unique id of the minute during the cross section. We originally decided to group a count_session’s data into minute sections. Each minute section is a row in the csv, that has a parent count_session and the count_session has a parent of a counterpoint.
- user_id: Represents the unique id of the user that conducted the counterpoint that conducted this count. You can use this to group or segment your data how you like.
- team_id: Represents the unique identifier of a team that conducted this count. This helps if you are only interested in seeing data done by your team at this counterpoint.
- name: The name of the counterpoint
- intersection: The nearest intersection of the counterpoint
- description: The description or “comments” of the counterpoint
- lat: The latitude part of the coordinate of the counterpoint
- lng: The longitude part of the coordinate of the counterpoint
- start_datetime: The start time of the counting session
- end_datetime: The end time of the counting session
- elapsed_time: The difference between end_datetime and start_datetime in seconds
- timezone: The timezone of the counterpoint. This is needed if you want to compare counts across multiple timezones in their local time. Say you want to compare counts across Canada that all happen at 5:00pm local. Use the start_datetime and start_endtime rather than utc_start_datetime and utc_end_datetime.
- timestamp: The minute by minute timestamp of the count. All the timestamps associated to one unique count_session (count_session_id) should all be 1 minute apart.
Further, I will write this up in a better form of documentation, as well as adding these descriptions to the second row of the csv export.
